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Journal of Leukocyte Biology logoLink to Journal of Leukocyte Biology
. 2010 Jan 20;87(4):655–662. doi: 10.1189/jlb.0809570

Proteome bioprofiles distinguish between M1 priming and activation states in human macrophages

Joseph N Brown *,1, Mark A Wallet *, Bryan Krastins †,2, David Sarracino †,2, Maureen M Goodenow *,3
PMCID: PMC2858305  PMID: 20007246

Abstract

Macrophage activation is a dynamic process that results in diverse functional outcomes ranging from immunoregulation to inflammation. The proinflammatory, or M1, response is a complex, bimodal progression composed of a “prime,” classically through IFN-γ, and “trigger,” such as LPS. To characterize the physiological response of M1 activation, a systems biology approach was applied to determine the intracellular proteome bioprofiles of IFN-γ- and LPS-treated primary human macrophages. Our goal was to develop intracellular proteomic fingerprints to serve as novel correlates of macrophage priming and/or activation to augment the existing approaches of analyzing secreted cytokines and cell-surface protein expression. The majority of the proteome, ∼78%, remained stable during activation, representing the core proteome. In contrast, three distinct patterns defined response proteomes: IFN-γ-specific, LPS-specific, or IFN-γ- and LPS-shared or M1-specific. Although steady-state expression levels of proteins involved in energy metabolism and immune response were increased during priming and triggering, changes in protein and fatty acid metabolism, signaling, and transport pathways were most apparent. Unique proteomic fingerprints distinguish among IFN-γ-specific, LPS-specific, or M1-specific activation states and provide a clear molecular, archeological profile to infer recent history of cells, as well as correlates for chronic macrophage activation in health and disease.

Keywords: IFN-γ, LPS, systems biology, molecular archeology, pathway analysis

Introduction

Macrophage activation is a dynamic process that can have beneficial or pathogenic effects to human health. A wide spectrum of macrophage activation states, including proinflammatory, regulatory, or wound-healing, is characterized by downstream cytokine secretion profiles [1]. Proinflammatory, or M1, activation aids in host defense against pathogen infections and typically occurs as a biphasic response. For example, microbial LPS, a TLR4 ligand, is sufficient to induce secretion of proinflammatory cytokines, such as TNF, IL-1β, or IL-6, by macrophages [2,3,4,5,6]. In contrast, a priming event, classically in response to IFN-γ, is insufficient to stimulate macrophages to release proinflammatory cytokines, although priming sensitizes macrophages, resulting in enhanced secretion of proinflammatory cytokines to subsequent LPS treatment [7,8,9].

Macrophage responses to stimulation result from networks of intracellular signaling molecules working in a regulated and concerted effort to induce transcription factors and prime cellular machinery on a systems level [2, 10, 11]. Although priming of macrophages produces limited change in the profile of secreted proteins, IFN-γ signals through the STAT pathway(s) to modulate gene expression and the intracellular macrophage proteome [12], which should differ from the M1 bioprofile produced in macrophages by TLR signaling. Advances in whole proteome analyses, specifically gel-based separation systems, allow unprecedented global views of protein expression. A unique advantage in performing gel-based proteomics is the ability to detect protein variations, which may reflect different isoforms and/or PTMs between observed and expected protein migration through SDS-PAGE. We designed a systems biology approach to discover global protein responses and identify an intracellular proteome signature of macrophages primed by IFN-γ or activated by LPS.

MATERIALS AND METHODS

Macrophage treatment and protein preparations

Elutriated monocytes from healthy HIV-negative/hCMV-negative donors were obtained from Dr. Howard Gendelman (Nebraska Medical Center, Omaha, NE, USA) [13] under protocols approved by the Institutional Review Boards at the University of Nebraska (Omaha, NE, USA) and the University of Florida (Gainesville, FL, USA). Monocytes were resuspended in DMEM media supplemented with 1000 U/ml rhM-CSF, 10% heat-inactivated human serum, 200 mM L-glutamine, 50 mg/ml Gentamicin, and 25 mg/ml Ciprofloxacin. Cells were plated 3 × 106 cells/well in six-well plates and differentiated for 7 days in the absence of additional rhM-CSF at 37°C, with 50% media changes every 2–3 days, as described previously [14]. Macrophages were treated for 24 h with 1 μg/ml Escherichia coli 0111:B4 LPS (Sigma Chemical Co., St. Louis, MO, USA) or 1 μg/ml IFN-γ (Sigma Chemical Co.) or mock-treated with media alone. After washing cultures four times with PBS, cells were lysed in lysis buffer (8 M GuHCL, 5% 1-propanol, 100 mM ammonium bicarbonate, 10 mM DTT, pH 8.6), and lysates were stored at –80°C.

Cytokine expression assays

Elutriated monocytes were plated at 2.5 × 105 cells/well in 24-well plates and differentiated for 7 days to achieve monolayers of macrophages, as described previously [14]. Media were replaced with fresh media alone (mock) or supplemented with 1 μg/ml E. coli 0111:B4 LPS or 1 μg/ml IFN-γ. Culture supernatants were harvested 1, 12, 24, or 48 h following treatments, and human TNF, IL-6, IL-1β, or CXCL10 (IP-10) cytokine concentrations were determined using OptEIA ELISA kits (BD Biosciences, San Jose, CA, USA). Cytokine secretion from monocytes isolated from three independent donors was measured.

Protein fractionation, preparation, and LC-MS/MS analysis

Samples were prepared for analysis by MS as described previously [15, 16]. A one-dimensional SDS-PAGE LC-MS/MS approach was used to accommodate our strategy of maximizing the dynamic range and coverage of the proteome and because this method has demonstrated reproducibility [15, 16]. Briefly, samples were reduced with 10 mM DTT and alkylated with 40 mM iodoacetic acid prior to SDS-PAGE. Gels were fixed and stained with Coomassie blue, prior to slicing into 15 m.w. regions. Each region was subjected to in-gel trypsin digestion for 24 h. Peptides were extracted from the gel slices, lyophilized, and re-dissolved in loading buffer (5% acetonitrile, 0.25% formic acid) for LC-MS/MS analysis. Samples are run on a LCQ DECA XP Plus Proteome X workstation (Thermo Fisher Scientific, Waltham, MA, USA), which produced output as 135 individual RAW spectral files (see “Activated Macrophage Proteomics” at the Tranche Proteome Server: https://proteomecommons.org/. Files: Untreated_Donor1_24h; Untreated_Donor2_24h; Untreated_Donor3_24h; LPS_Donor1_24h; LPS_Donor2_24h; LPS_Donor3_24h; IFN_Donor1_24h; IFN_Donor2_24h; IFN_Donor3_24h).

Data analysis

Peptide IDs were made using SEQUEST through the Bioworks Browser 3.2. Sequential database searches were performed using the National Center for Biotechnology Information RefSeqHuman FASTA database, considering differential carboxymethylated, modified cysteines and oxidized methionines. Peptide score cutoff values were chosen at a cross-correlation of 1.5 for singly charged ions, 2.5 for doubly charged ions, and 3.0 for triply charged ions. Confidence in peptide identifications was increased further by filtering on ΔCN values ≥0.1, rank of preliminary score values ≥10, and a peptide probability value ≥1e-3. Within each slice, a minimal list of peptides for each protein was determined after duplicates were removed. This list was sorted by the total number of peptides in descending order of the number of unique peptides per protein identification. The first peptide array in this list was defined as a cluster and compared pair-wise with every other array in the list by determining whether the N-1 comparison was an equal or a proper subset. If the peptide array were determined to be an equal or proper subset, the array was added to the cluster and removed from the list. The process was repeated until all comparisons were exhausted. For each cluster, the gene with the greatest number of peptide elements was assigned to designate the cluster. If multiple genes within the cluster had the same number of peptides, an arbitrary member was assigned as representative of the cluster. Peptides shared between clusters were identified and removed from further analysis. Peptide area was calculated using the area function in BioWorks 3.2 (Thermo Fisher Scientific) with a scan window of 60. The gene area was calculated as the sum of the areas for each independent analyte for all unique peptides within a cluster. If multiple areas were identified for a given analyte, the largest area was selected and used in the area calculation. An independent analyte is defined as unique mass to charge identified in the SEQUEST search passing the filtering criterion.

Statistical analysis for protein abundance was performed using DAnTE [17]. Spectral counts were log10-transformed, and missing data points were estimated by using half of the minimum value across all samples. An ANOVA was applied to identify statistical differences in protein expression levels that correlate with macrophage priming and triggering. Statistical significance was set as P < 0.05. For purposes of the heat-map, protein abundances were normalized across conditions by z-score and graphed using the OmniViz software package (OmniViz Inc., Tewksbury, MA, USA). Proteins altered in two out of three donors with P < 0.05 were included for pathway analysis, performed using Database for Annotation, Visualization, and Integrated Discovery [18, 19], Kyoto Encyclopedia of Genes and Genomes [20,21,22], and BioCarta (BioCarta LLC, San Diego, CA, USA) software. Results of pathway analyses were integrated manually and visualized as a network created in Adobe Illustrator (Adobe Systems Inc., San Jose, CA, USA).

RESULTS

LPS and IFN-γ induce distinct cytokine expression profiles in macrophages

Activation-induced cytokine/chemokine expression was evaluated over a time course to empirically determine the optimal time-point for whole proteome analysis of chronic priming or activation. Secretion of TNF, IL-1β, IL-6, or CXCL10 (IP-10) during 48 h of IFN-γ or LPS stimulation was measured. LPS treatment induced peak secretion of TNF, IL-1β, and IL-6 within 12–24 h, in contrast to IFN-γ treatment, which failed to stimulate secretion of any of the three cytokines (Fig. 1, A–C). IFN-γ treatment produced a 50-fold increase in secreted levels of CXCL10 that appeared by 12 h and persisted for 48 h (Fig. 1D). LPS induced expression of CXCL10, although maximal levels were only tenfold greater than untreated macrophages by 24 h. In general, the secreted cytokine/chemokine response to IFN-γ or LPS was stable for 24 h, which was the time chosen to develop an intracellular proteomic profile of chronic treatment.

Figure 1.

Figure 1.

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Profile of LPS- and IFN-γ-induced cytokine secretion by macrophages. Macrophages were treated with media alone (mock, ○), 1 μg/ml LPS (•), or 1 μg/ml IFN-γ (shaded circles) for 1, 12, 24, and 48 h. Accumulation of (A) TNF, (B) IL-6, (C) IL-1β, and (D) CXCL10 was determined by ELISA. Data represent mean ± sd of three independent monocyte donors.

IFN-γ and LPS perturb intracellular monocyte-derived macrophage protein expression profiles

Intracellular proteomes in macrophages from three independent donors were assessed using a gel-based, size-fractionation approach and identification by LC-MS/MS. In total, 4474 individual protein identifications, representative of 2207 unique proteins across all treatments and donors, were tallied. The first level of analysis was to determine overall protein distribution by mass among all donors and treatment groups, where physical gel slices are presented as m.w. bins. The total number of proteins based on peptide identifiers within each bin is presented as a reconstructed gel heat-map (heat-gel; Fig. 2A) Proteins were distributed with variable frequency within bins ranging from <10 kDa to >350 kDa but displayed similar profiles across treatments (P>0.1).

Figure 2.

Figure 2.

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Distribution of proteins across m.w. regions. Heat-gel representing proteins identified in gel slices following trypsinization and LC-MS analysis (A). Gel slices 1–15 are shown on the left with representative m.w. and P values. Heat-gels for Donors 1–3 for 24 h at a basal level, IFN-γ-primed, and LPS-activated. Gel slices are colored to represent the number of protein identifications within that mass region and scaled to reflect the relative size of the slice analyzed. Histogram of the percentage of proteins observed in each m.w. region (B). Basal, IFN-γ, and LPS are represented by blue, red, and green, respectively.

The next level of analysis was to determine how each treatment affected overall protein distribution within m.w. bins. Data from individual donors were averaged for each treatment to generate consensus estimates of protein distribution. For all treatments, ∼45% of proteins migrated between 35 kDa and 350 kDa, and ∼45% migrated in the lower m.w. bins (<35 kDa). Approximately 10% of the macrophage proteome was comprised of proteins >350 kDa (bin 1), and almost 10% were in the range <10 kDa (bin 15). IFN-γ or LPS treatment compared with each other or to basal levels produced a similar total number of intracellular proteins in any particular bin, indicating that treatments produced no significant shifts in global distribution of proteins based on mass (Fig. 2B).

To determine the relationship between observed and predicted protein migrations, observed m.w. bins for the 4474 proteins were plotted against their predicted m.w. (Fig. 3). The median predicted weight for observed proteins (vertical red lines) within individual bins correlated with the expected m.w. ranges of the bins (gray boxes) between 15 and 160 kDa (bins 4–13). In contrast, there was discordance between observed and predicted protein migrations in the extreme m.w. bins, >160 kDa and <15 (bins 1–3 and 14 and 15, respectively). A number of proteins, such as CD36, CD44, or integrin B, identified in the high m.w. region, are targets of additive PTMs, including glycosylation, phosphorylation, and lipidation, at multiple sites, which contribute to significant shifts in gel migration [23,24,25,26,27].

Figure 3.

Figure 3.

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Distribution of observed proteins relative to predicted molecular mass. A total of 4474 observed proteins was binned according to identified m.w. regions and plotted against their respective predicted m.w. The shaded area indicates the m.w. region that each bin covers, with largest to smallest proteins arranged from upper right to lower left, respectively. Red vertical lines represent the median values, and horizontal lines indicate the interquartile ranges for the predicted m.w.

Differential protein profiles by treatment

To identify components of IFN-γ-specific, LPS-specific, or M1-specific proteomes, exclusive or shared proteins for each treatment were evaluated. Qualitative analysis examined the distribution of the 2207 unique proteins between treatment groups, and the data were represented by Venn diagrams (Fig. 4). To increase confidence in treatment-specific identifications across biological replicates, a filter that required proteins be observed in at least two of the three donors was applied, which condensed the list of proteins to 1178. Approximately 78% of proteins, representing the core macrophage proteome, were observed in macrophages independent of treatment (Fig. 4A), and ∼22% of proteins defined response-specific proteomes (Fig. 4, A and B). Response profiles included proteins expressed exclusively during IFN-γ or LPS treatment (Fig. 4, A and B), as well as a subset of proteins that fell below the level of detection with either treatment, representing a selective repression during macrophage activation (Fig. 4, A and B). Although the IFN-γ proteome was more similar to the basal condition, overlap between IFN-γ and LPS proteomes suggested that certain features of priming are necessary events for macrophages to progress to a triggered response.

Figure 4.

Figure 4.

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Response profiles of observed proteins. Proteins observed in two out of three donors from basal (red), IFN-γ (blue), and LPS (yellow) are shown to scale (A). The white region represents the core proteome; i.e., proteins present under all conditions. The colored regions indicate the response proteome. The table indicates the number of proteins observed for each treatment (Total) and within each region of the Venn diagram by color. The elliptical Venn diagram displays overlap among response proteomes (B).

To develop unique fingerprints for each treatment, proteins found exclusively and with statistical significance (P<0.05) in IFN-γ or LPS treatments relative to basal condition were identified. Three distinct bioprofiles appeared: IFN-γ-responsive (proteins expressed exclusively upon IFN-γ treatment), LPS-responsive (proteins expressed exclusively upon LPS), and responsive to either activation regime (proteins expressed during IFN-γ or LPS treatment; Fig. 5). Validation of the proteomic methodology is evident, where IFN-γ is detected only in IFN-γ-primed macrophages, most likely reflecting an exogenous recombinant protein that was bound to IFN-γR1 and/or internalized during culture. IFN-γ treatment resulted in a statistically significant, increased abundance of several enzymes, including LAP3, CTSB, WARS, and GAA, as well as the outer mitochondrial membrane CYB5B transport protein. In contrast, LPS treatment increased levels of several signaling molecules, including CCR7, IFIT2, ISG15, ISG20, and NAMPT. LPS also increased the expression of transport proteins, TFRC and TRAPPC1. Proteins defining the M1 bioprofile included enzymes, such as ACADM; signaling proteins, including IP-10 (CXCL10), CLIC1, and PCBP2; and transport proteins, such as TAP1 and SLC2A6. Overall, metabolic, signal transduction, and transport pathways were the primary pathways impacted in M1 macrophages.

Figure 5.

Figure 5.

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Heat-map of representative proteins expressed exclusively after 24 h of IFN-γ or LPS treatment across donors. Heat-map illustrates proteins that are expressed exclusively upon IFN-γ treatment (IFN-γ-responsive, blue), LPS treatment (LPS-responsive, yellow), and IFN-γ or LPS (M1-responsive, green). Protein symbols, Human Protein Reference Database (HPRD), SwissProt, and P value identifiers are represented on the right.

IFN-γ priming and activation network analysis

To define the functional context of the bioprofiles of IFN-γ priming and LPS activation, significantly altered proteins from any treatment group were organized into consensus pathways and interconnected into networks (Fig. 6). IFN-γ-specific proteins contributed primarily to protein metabolism, immune response, and protein transport. In contrast, LPS produced proinflammatory cytokines and increased levels of ACSL4, which can result in fatty acid metabolism promoting inflammatory lipids, such as PGs, LTs, lipoxins, and PAF [28].

Figure 6.

Figure 6.

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Intracellular proteomic profile of IFN-γ-primed or TLR4-activated macrophages. AA=arachidonic acid, COX-2=cyclooxygenase 2, cPLA2=cytoplasmic phospholipase A2, GRB2=growth factor receptor-bound protein 2, IGF2R=insulin-like growth factor 2 receptor, iNOS- inducible NO synthase, iPLA2=Ca2+- independent cytosolic phospholipase A2, IRF3=IFN regulatory factor 3, Lyso-PtdCho=lysophosphotidylcholine, MRC1= mannose receptor 1, PAK2=p21-activated kinase 2, sPLA2=secretory phospholipase A2.

In addition to IFN-γ- or LPS-specific response factors, several pathways were similarly impacted by either treatment. Specifically, immune response and energy metabolism pathways were modulated the greatest by IFN-γ priming or LPS activation. IFN-γ and LPS triggered the production of PCBP1 and PCBP2, two RNA-binding proteins that are known to interact directly with mRNA and viral RNA to control translation [29, 30], and IDO1, an IFN-induced marker of inflammation that depletes L-tryptophan to prevent the growth of intracellular pathogens or tumor cells [31]. IFN-γ or LPS similarly decreased the expression of multiple proteins, including MMP9, ABCD1, and thrombospondin receptor (CD36).

DISCUSSION

This study establishes a paradigm for categorizing macrophage activation based on a panel of intracellular biomarkers. Although cellular activation states are traditionally determined by cytokine expression profiles [1, 2], the diversity of cytokines produced by primed macrophages is limited and nonspecific to macrophages. A global proteome bioprofile for primed as well as triggered macrophages identifies characteristic features that define specific macrophage activation states. Intracellular bioprofiling represents a robust extension to secreted cytokine measurements and a cell-surface marker phenotype, enhancing our understanding and characterization of the M1-activated response proteome.

Macrophage activation is a dynamic and complex process capable of producing a broad spectrum of functional outcomes [1]. The core proteome, which represented ∼80% of the proteins, remained relatively stable, independent of stimulation, indicating that only modest changes in the proteome can have substantial effects on the functional activity of the cell. In contrast, three distinct bioprofiles characterized unique chronic activation states of macrophages. The response proteome presents a reproducible fingerprint that distinguishes activated from basal macrophages and furthermore, between different chronic stages of classical activation.

Similarities in proteome bioprofiles between IFN-γ and LPS stimulation, specifically the immune response and metabolism pathways, reinforce the concept that priming and activation are not discrete functional pathways but rather, phases within a progression continuum, resulting in full activation [1]. Substantial overlap between primed and basal macrophage proteomes suggests that temporal structure underlies the activation process and that certain features may be necessary during the course of priming for macrophages to become activated. One key mechanism of shared responses to IFN-γ and LPS is secretion of type I IFNs. The cellular autocrine response to LPS-induced type I IFNs, IFN-α and/or IFN-β, may induce signaling that mimics IFN-γ priming to facilitate early events that are required for TLR4-mediated macrophage activation. In addition to regulation of gene and protein expression, subsequent post-translational modifications can extend functional diversity of the proteome [32].

Ex vivo differentiated human macrophages provide a model for studies of basic macrophage function. Although in vivo tissue-specific macrophages represent a complex population of specialized phenotypes with diverse functions that may not be modeled ideally by ex vivo macrophages [33,34,35], in-depth, unsupervised discovery approaches are difficult to apply when sample size and anatomical location are limiting. An alternative approach presented in this study is development of an empirical biological model that can be used to generate novel hypotheses, which are tested subsequently with supervised, reductionist methods that are compatible with tissue-derived macrophages.

Although receptor engagement by IFN-γ or LPS stimulates PTMs, specifically phosphorylation, to induce transcription and translation, changes in global protein expression levels reflect upstream signaling events. The extreme m.w. regions of the gels (>350 and <15 kDa) produced the greatest protein identifications, which is likely the consequence of multiple processes of PTMs. Although individual modifications on a particular protein may correspond to a specific state, such as priming or triggering, this study is an initial screen of global protein abundance in primary human macrophages. Although several proteins were observed outside of their predicted m.w. regions, the approach used in this study has advantages for identifying targets for focused analysis of novel sites of PTMs.

Metabolic, signal transduction, and transport pathways were impacted by IFN-γ and LPS. LPS and IFN-γ decreased the intracellular expression of MMP9, probably by inducing activation through cleavage and secretion of MMP9, which has been observed during activation of macrophages [36, 37]. Increased levels of the cysteine protease, CTSB, were observed during IFN-γ or LPS stimulation. MMP9 and CTSB are regulated by the NF-κB/p38 signaling pathway [37,38,39], contributing to an inflammatory response. As expected, several proteins involved with the immune response were increased, notably, RNA-binding proteins, including PCBP1 and PCBP2, and the tyrosine metabolic enzyme, IDO1, emphasizing the important role of pathogen recognition and clearance upon IFN-γ and LPS stimulation.

A consensus core proteome defines macrophages independent of activation state, and inducible proteomes of IFN-γ-primed versus LPS-triggered macrophages possess unique components that define an activation-specific fingerprint. Exclusive expression of several proteins (LAP3, CTSB, WARS, FAM26F, and CYB5B) provides a positive bioprofile of IFN-γ priming that distinguishes primed macrophages from basal (resting) or LPS-activated macrophages. Similar to IFN-γ priming, LPS induced a divergent signature proteome (ISG15, NAMPT, IFIT2, CCR7, and TFRC), which was also unique from basal macrophages. Finally, the shared proteome of IFN-γ-primed and LPS-triggered macrophages distinguishes M1 macrophages from basal macrophages and might reflect complex mechanisms of regulation of STAT or TLR signaling during macrophage activation [40]. Other activation or inhibitory stimuli, for example, triggering through TLRs other than TLR4 or by different pathogens, may produce unique proteomic fingerprints that can be differentiated from basal, IFN-γ, and LPS [2].

IFN-γ and TLR signaling are connected intimately during microbial infections, a connection highlighted by cytokine secretion during sequential stimulations [41]. In addition to immune response, other pathways, including adhesion/receptors, tissue remodeling, transporters, and metabolic enzymes, are altered in macrophages by LPS stimulation [42]. Several factors, including CD44 [43, 44], MMPs [45], serine proteinase inhibitors [46, 47], and IDO1 [48], within these pathways were also identified in our analysis, consistent with previous observations of macrophage response to LPS and infection. An extended, in-depth analysis of macrophage proteomics may yield comprehensive bioprofiles that identify multiple activation states and provide a molecular archeological approach to infer the recent history of a cell through its proteomic fingerprint.

Acknowledgments

Research was supported in part by PHS R01 awards HD032259, AI047723, and AI065265; T32 awards AR07603 and CA09126; the Laura McClamma Fellowship; Center for Research in Pediatric Immune Deficiency; and Stephany W. Holloway University Chair for AIDS Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Dr. Howard Gendelman and Myhanh Che at the Nebraska Medical Center for providing elutriated monocytes and Dr. Daniel Lopez-Ferrer for helpful discussions.

Footnotes

Abbreviations: ABCD1=ATP-binding cassette, subfamily D, member 1, ACADM=acyl-CoA dehydrogenase, medium chain, ACSL4=acyl-CoA synthetase long-chain family member 4, CLIC1=chloride intracellular channel 1, CTSB=cathepsin B, CYB5B=cytochrome b5, EIF2S3=eukaryotic translation initiation factor 2, subunit 3, FAM26F=family with sequence similarity 26, member F, GAA=lysosomal α-glucosidase, h=human, IDO1=indole 2,3-dioxygenase 1, IFIT2=IFN-induced protein with tetratricopeptide repeats 2, IP-10=IFN-inducible protein 10, ISG15/20=IFN-induced protein 15/20, LAP3=leucine aminopeptidase 3, LC-MS=liquid chromatography-mass spectrometry, LT=leukotriene(s), MMP9=matrix metalloproteinase 9, NAMPT=nicotinamide phosphoribosyltransferase, PAF=platelet-activating factor, PCBP2=poly(rC)-binding protein 2, PTM=post-translational modification, SLC2A6=solute carrier family, member 6, TAP1=transporter 1 ATP-binding cassette, subfamily B, TFRC=transferrin receptor, TRAPPC1=trafficking protein particle complex 1, WARS=tryptophanyl tRNA synthetase

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