1. Cell Biology
  2. Immunology and Inflammation

Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response

  1. Timurs Maculins
  2. Erik Verschueren
  3. Trent Hinkle
  4. Meena Choi
  5. Patrick Chang
  6. Cecile Chalouni
  7. Shilpa Rao
  8. Youngsu Kwon
  9. Junghyun Lim
  10. Anand Kumar Katakam
  11. Ryan C Kunz
  12. Brian K Erickson
  13. Ting Huang
  14. Tsung-Heng Tsai
  15. Olga Vitek
  16. Mike Reichelt
  17. Yasin Senbabaoglu
  18. Brent Mckenzie
  19. John R Rohde
  20. Ivan Dikic  Is a corresponding author
  21. Donald S Kirkpatrick  Is a corresponding author
  22. Aditya Murthy  Is a corresponding author
  1. Department of Cancer Immunology, Genentech, United States
  2. Institute of Biochemistry II, Goethe University, Germany
  3. Department of Microchemistry, Proteomics and Lipidomics, Genentech, United States
  4. Khoury College of Computer Sciences, Northeastern University, United States
  5. Department of Pathology, Genentech, United States
  6. Department of Oncology Bioinformatics, Genentech, United States
  7. Department of Translational Immunology, Genentech, United States
  8. IQ Proteomics LLC, United States
  9. Department of Mathematical Sciences, Kent State University, United States
  10. Department of Microbiology and Immunology, Dalhousie University, Canada
  11. Department of Infectious Diseases, Genentech, United States
  12. Interline Therapeutics, United States
https://doi.org/10.7554/eLife.62320
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Cite this article
  1. Timurs Maculins
  2. Erik Verschueren
  3. Trent Hinkle
  4. Meena Choi
  5. Patrick Chang
  6. Cecile Chalouni
  7. Shilpa Rao
  8. Youngsu Kwon
  9. Junghyun Lim
  10. Anand Kumar Katakam
  11. Ryan C Kunz
  12. Brian K Erickson
  13. Ting Huang
  14. Tsung-Heng Tsai
  15. Olga Vitek
  16. Mike Reichelt
  17. Yasin Senbabaoglu
  18. Brent Mckenzie
  19. John R Rohde
  20. Ivan Dikic
  21. Donald S Kirkpatrick
  22. Aditya Murthy
(2021)
Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response
eLife 10:e62320.
https://doi.org/10.7554/eLife.62320
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Abstract

Defective autophagy is strongly associated with chronic inflammation. Loss-of-function of the core autophagy gene Atg16l1 increases risk for Crohn’s disease in part by enhancing innate immunity through myeloid cells such as macrophages. However, autophagy is also recognized as a mechanism for clearance of certain intracellular pathogens. These divergent observations prompted a re-evaluation of ATG16L1 in innate antimicrobial immunity. In this study, we found that loss of Atg16l1 in myeloid cells enhanced the killing of virulent Shigella flexneri (S.flexneri), a clinically relevant enteric bacterium that resides within the cytosol by escaping from membrane-bound compartments. Quantitative multiplexed proteomics of murine bone marrow-derived macrophages revealed that ATG16L1 deficiency significantly upregulated proteins involved in the glutathione-mediated antioxidant response to compensate for elevated oxidative stress, which simultaneously promoted S.flexneri killing. Consistent with this, myeloid-specific deletion of Atg16l1 in mice accelerated bacterial clearance in vitro and in vivo. Pharmacological induction of oxidative stress through suppression of cysteine import enhanced microbial clearance by macrophages. Conversely, antioxidant treatment of macrophages permitted S.flexneri proliferation. These findings demonstrate that control of oxidative stress by ATG16L1 and autophagy regulates antimicrobial immunity against intracellular pathogens.

Introduction

Effective immunity against enteric pathogens requires complex signaling to coordinate the inflammatory response, pathogen clearance, tissue remodeling, and repair (Maloy and Powrie, 2011). Autophagy, a cellular catabolic pathway that eliminates cytosolic cargo via lysosomal degradation, has emerged as an important regulator of mucosal immunity and inflammatory bowel disease (IBD) etiology. Genome-wide association studies linked the missense T300A variant in the core autophagy gene Atg16l1 with increased risk for IBD (Hampe et al., 2007; Rioux et al., 2007). Later studies demonstrated that this variant contributes to enhanced caspase-mediated degradation of the ATG16L1 protein (Lassen et al., 2014; Murthy et al., 2014). Genetic loss-of-function of core autophagy genes including Atg16l1 increases secretion of pro-inflammatory cytokines by macrophages in response to toll-like receptor (TLR) activation (Lim et al., 2019; Saitoh et al., 2008). This contributes to increased mucosal inflammation, driving resistance to extracellular pathogens such as Citrobacter rodentium (Marchiando et al., 2013; Martin et al., 2018) and pathogenic Escherichia coli (Wang et al., 2019). Loss of autophagy-related genes Atg16l1, Beclin1, and Rbc1cc1 (encoding FIP200) in the myeloid compartment also confers enhanced antimicrobial immunity against certain intracellular pathogens, such as Salmonella typhimurium (S.typhimurium) and Listeria monocytogenes via induction of type I and II interferon responses (Samie et al., 2018; Wang et al., 2020). Thus, autophagy impacts antimicrobial immunity in vivo via innate immunosuppression as well as myeloid cell reprogramming.

Targeted elimination of intracellular pathogens by xenophagy, a form of selective autophagy, is well-described in cellular model systems (Bauckman et al., 2015). In contrast to non-selective autophagy triggered by nutrient stress, xenophagy functions to eliminate intracellular bacteria by sequestering them in autophagosomes and shuttling them to the degradative lysosomal compartment. Pathogenic bacteria have evolved mechanisms to either evade capture by the autophagy machinery, as by S.typhimurium and S.flexneri (Birmingham et al., 2006; Campbell-Valois et al., 2015; Dong et al., 2012; Martin et al., 2018; Xu et al., 2019b) or attenuate autophagic flux as by Legionella pneumophila (Choy et al., 2012). S.typhimurium primarily resides in a protective compartment known as the Salmonella containing vacuole (SCV). There it prevents formation of the ATG5~ATG12-ATG16L1 complex at the bacterial vacuolar membrane via secretion of the effector SopF, which blocks ATG16L1 association with vacuolar ATPases (Xu et al., 2019b). Despite its ability to interfere with autophagy, infected host cells still recognize 10–20% of cytosolic S.typhimurium and subject this subpopulation to lysosomal degradation via mechanisms involving direct recognition of either the bacterial surface (Huang and Brumell, 2014; Stolz et al., 2014) or damaged phagocytic membranes (Fujita et al., 2013; Thurston et al., 2012).

Compared to S.typhimurium, S.flexneri is not characterized by a vacuolar life cycle, but instead resides in the host cytoplasm. S.flexneri effector proteins IcsB and VirA are capable of completely inhibiting autophagic recognition to permit replication in the host cytosol (Liu et al., 2018; Ogawa et al., 2005). In response, the host cell attempts to further counteract S.flexneri infection via diverse mechanisms, such as coating bacterial cell surfaces with guanylate-binding proteins (GBPs) (Li et al., 2017; Wandel et al., 2017) or sequestering bacteria in Septin cage-like structures to restrict their motility (Mostowy et al., 2010). To reveal these mechanisms, cell-based studies have largely utilized attenuated variants (e.g. IcsB or IcsB/VirA double mutants of S.flexneri) or strains that inefficiently colonize the host cytosol (e.g. S.typhimurium which express SopF). Thus, observations from in vivo genetic models must be reconciled with observations made in cell-based systems to fully describe the roles of autophagy in antimicrobial immunity. Importantly, there is a lack of understanding of how autophagy contributes to immunity against non-attenuated S.flexneri. This insight is especially lacking in relevant host cell types, such as macrophages that are infected by S.flexneri (Ashida et al., 2015).

In this study, we investigated the role of ATG16L1 in myeloid cells in response to infection by virulent S.flexneri (strain M90T). We observed that loss of Atg16l1 in murine bone-marrow-derived macrophages (BMDMs) enhanced S.flexneri elimination in cell culture, as well as by mice lacking ATG16L1 in the myeloid compartment (Atg16l1-cKO; LysM-Cre-mediated deletion of Atg16l1). We utilized multiplexed quantitative proteomics to characterize total protein, phosphorylation, and ubiquitination changes in wild type (WT) and ATG16L1-deficient (cKO) BMDMs either uninfected or infected with S.flexneri. Together these data provide a comprehensive catalogue of basal differences between WT and cKO BMDMs and the dynamic response of each to infection. Expectedly, significant differences were observed for components in the autophagy pathway, along with proteins involved in cell death, innate immune sensing and NF-κB signaling. However, pharmacological inhibition of these pathways only modestly affected S.flexneri killing by BMDMs at later time points following infection. One key difference between control and ATG16L1-deficient BMDMs was a basal oxidative stress response, highlighted by accumulation of the sodium-independent cystine-glutamate antiporter (XCT) and multiple components of the glutathione metabolic pathway. XCT is responsible for importing the constituents required for glutathione (GSH) biosynthesis, and the consequent detoxification of reactive oxygen species (ROS) and lipid peroxides. Consistently, sustained pharmacological XCT inhibition in ATG16L1-deficient BMDMs compromised their viability. Likewise, XCT inhibition enhanced S.flexneri clearance by wild type BMDMs, demonstrating a role for this pathway in eliminating cytosolic bacteria. Finally, pharmacological scavenging of ROS permitted bacterial regrowth in BMDMs, thereby linking the antimicrobial capacity of ATG16L1-deficient macrophages to elevated ROS.

This study offers a comprehensive, multidimensional catalogue of proteome-wide changes in macrophages following infection by an enteric cytosolic pathogen, including key nodes of cell-autonomous immunity regulated by autophagy. Our findings demonstrate that ATG16L1 tunes antimicrobial immunity against cytosolic pathogens via oxidative stress as well as interferon responses. Pharmacological modulation of these pathways may represent opportunities for elimination of cytosolic pathogens.

Results

Enhanced clearance of intracellular S.flexneri by loss of Atg16l1

Recent studies identified that defective autophagy in macrophages enhances type I interferon and TNF-driven inflammatory signaling to promote antimicrobial immunity (Lim et al., 2019; Martin et al., 2018; Samie et al., 2018; Wang et al., 2020). Given these observations, we wanted to explore whether loss of Atg16l1 affects killing of the intracellular enteric pathogen S.flexneri (strain M90T). To test this directly in BMDMs, cells from either control (Atg16l1-WT) or mice lacking ATG16L1 in the myeloid compartment (Atg16l1-cKO) were subjected to the gentamycin protection assay that enables quantification of intracellular bacteria by enumerating colony forming units (CFUs). We first determined the kinetics of S.flexneri killing by following BMDM infection in a time-course experiment with multiplicity of infection (MOI) of 5. Compared to wild type (WT) controls, ATG16L1-deficient BMDMs (cKO) demonstrated enhanced bacterial clearance (Figure 1A and B and Figure 1—figure supplement 1A). Since prior studies demonstrated increased sensitivity of autophagy-deficient cells to programmed cell death following engagement of cytokine receptors or microbial ligands (Lim et al., 2019; Matsuzawa-Ishimoto et al., 2017; Orvedahl et al., 2019), BMDM viability was measured in parallel by quantifying the propidium iodide (PI)-positive population via live-cell imaging. WT and cKO BMDMs displayed similar cell death kinetics over the time course of infection, indicating that the phenotype was not driven by enhanced cell death, but potentially by other factors in cKO BMDMs (Figure 1C).

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Enhanced clearance of intracellular S.flexneri by loss of Atg16l1.

(A) Representative serial dilutions from gentamycin protection assays following S.flexneri M90T infection of WT or cKO BMDMs at the indicated timepoints. (B) Comparison of colony forming units (CFUs) per well from independent infection experiments using BMDM preparations from Atg16l1-WT or Atg16l1-cKO mice. 2 hr **p=0.002, 3 hr **** p = <0.0001, 4 hr **** p = <0.0001, multiple t-test comparison. (C) Percentage of propidium iodide (PI)-positive cells during time-course infection of WT or cKO BMDMs with S.flexneri M90T. Graph represents individual values from three independent experiments using three different BMDM preparations. ns, non-significant. (D, E) Liver bacterial load 6 hr (D) or 24 hr (E) following intravenous injection of Atg16l1-WT or Atg16l1-cKO mice with S.flexneri M90T. Graphs show data from representative experiments out of two (D) or four (E) independent experiments as log10 CFU count per liver in indicated number of mice. In (D) ***p=0.0002 and in (E) outliers removed using ROUT (Q = 1%) method, **p=0.0031.

We next asked whether ATG16L1 in myeloid cells similarly impacts antimicrobial immunity in vivo. Since chronic enteric infection with S.flexneri in wild-type mice is not possible, current murine models are limited to acute infection via intravenous or intraperitoneal routes. We performed tail vein injection of S.flexneri and evaluated bacterial colonization in liver, spleen, and lung tissues at 6 or 24 hr following infection. Infection of the lung was not detected (data not shown) and infection of the spleen was comparable between genotypes (Figure 1—figure supplement 1B). However, myeloid-specific loss of Atg16l1 resulted in a markedly decreased bacterial burden in the liver (Figures 1D, 6 hr; Figures 1E, 24 hr). These findings indicate that ATG16L1 deficiency in macrophages enhances clearance of intracellular S.flexneri in vitro, while myeloid-specific loss of Atg16l1 accelerates hepatic clearance of S.flexneri in vivo. Although enhanced clearance of S.flexneri by Atg16l1-cKO mice at the early time point (6 hr) supported a role for ATG16L1 in hepatic myeloid cells including macrophages, a Kupffer-cell-specific role cannot be asserted using the current model since the LysM-cre transgene induces Atg16l1-cKO in other myeloid cell populations that may also contribute to the in vivo phenotype.

Multiplexed proteomic profiling of BMDMs following infection

To reveal factors that may drive enhanced S.flexneri killing in ATG16L1-deficient BMDMs, we characterized changes in the global proteome and post-translational modifications (PTMs) between WT and cKO BMDMs. Specifically, we applied 11-plex isobaric multiplexing via tandem mass tagging (TMT) in combination with liquid-chromatography and tandem mass spectrometry (LC-MS/MS). Cell lysates were prepared from WT and cKO BMDMs that were either Uninfected (U) or infected with S.flexneri (MOI 5) at Early (E; 45–60 min) or Late (L; 3–3.5 hr) time-points (Figure 2A). Cumulatively, two 11-plex experiments were performed with uninfected samples represented in biological triplicates and infected samples represented in biological quadruplicates (see Materials and methods for details). Data were acquired using the synchronous precursor selection (SPS)-MS3 approach wherein dedicated MS3 scan events are collected from fragment ion populations representing a mixture of the 11 samples and used to report the relative abundance of each peptide feature per channel (McAlister et al., 2014; Ting et al., 2011; Figure 2A).

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Multiplexed proteomic profiling of BMDMs following infection.

(A) Schematic representation of multiplexed proteomic profiling of macrophages during S.flexneri infection. (B–D) Venn diagrams show overlapping quantitative data collected in Plex1 and/or Plex2 for (B) global proteins, (C) phospho-, and (D) KGG(Ub)-sites. (E) Venn diagram displays an overlap of quantitative data for phospho- and KGG(Ub)-sites with respect to the global proteins quantified. (F and G) Heatmaps displaying K-means clustered quantitative data for (F) phospho- and (G) KGG(Ub)-sites relative to their corresponding global proteins. Note that the global proteins subjected to clustering differ between panels F and G based on the proteins from which PTMs were quantified. (H, I) Line plots show representative clusters from the Heatmaps above. Phospho Cluster 7 (panel H) and KGG(Ub) Cluster 5 (panel I) each show PTM profiles that diverge from their corresponding global protein measurements. Proteins and PTMs making up each cluster are presented in Supplementary file 1.

For global proteome profiling, quantitative data were obtained from >103,700 peptide features mapping to 9430 proteins. From the PTM enriched samples, quantitative data were obtained for >25,600 unique phospho- (5052 proteins) and >12,400 unique KGG(Ub)-sites (3324 proteins). When considering only features bearing data in both 11-plexes, the final dataset contained quantitative data for 7260 proteins (i.e. global proteome), 9418 phospho- and 3691 KGG(Ub)-sites (Figure 2B–D). As expected, ~90% of the post-translationally modified peptide spectral matches derived from proteins that were also identified and quantified in the global proteome dataset (Figure 2E). Both within and between plexes, peptide and protein level quantitative data were highly reproducible, with Pearson correlations ranging from 0.96 to 0.99 (Figure 2—figure supplement 1A). Phospho- and KGG(Ub)-sites profiling data were subjected to K-means clustering, each paired with the corresponding global proteome data. Heatmap representations revealed clusters of PTM changes that occur in genotype- and/or infection-dependent manners (Figure 2F and G). A subset of these clusters comprised PTMs whose quantitative profiles mirrored that of the underlying protein level due to altered protein expression or stability (e.g. phospho-sites Clusters 1–2 in Figure 2F and Figure 2—figure supplement 1B; KGG(Ub)-sites Cluster 7 in Figure 2G and Figure 2—figure supplement 1C). In contrast, other clusters displayed PTM profiles that diverged from their underlying proteins (e.g. phospho-sites Cluster 7 in Figure 2F and H; KGG(Ub)-sites Cluster 5 in Figure 2G and I). The composition of PTMs and proteins comprising each cluster are available in Supplementary file 1.

Interrogation of the uninfected datasets revealed differences between the genotypes on the global protein level. Consistent with previous observations (Samie et al., 2018), cKO BMDMs showed upregulation in autophagy receptors and inflammatory regulators, such as SQSTM/p62 and ZBP1, respectively (Figure 3A). In the phosphoproteome and KGG(Ub) datasets interesting observations concerned elevated phosphorylation of ubiquitin (RL40) at serine-57 (RL40_S57) (Figure 3B) and ubiquitination of FIS1 at lysine-20 (FIS1_K20) (Figure 3C), which are involved in endocytic trafficking (Lee et al., 2017; Peng et al., 2003) and mitochondrial and peroxisomal homeostasis (Bingol et al., 2014; Koch et al., 2005; Zhang et al., 2012), respectively.

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A global overview of changes identified between the genotypes and upon infection.

(A–C) Volcano plots showing differential expression of global proteins (A), phospho- (B) and KGG(Ub)-sites (C) between uninfected cKO vs. WT BMDMs. Volcano plots display log2 fold changes and -log10 transformed adjusted p-values for the host proteome. Bar graphs at the bottom of each panel represent top hits with positive and negative log2 fold changes. Uninfected (U) samples are shown with orange (WT) and purple (cKO), early infection (E) in green (WT) and blue (cKO) and late infection (L) in yellow (WT) and red (cKO), respectively. Protein names are shown as UniProt identifiers with modification sites indicated by the modified amino acid (S/T/Y/K) and residue number (e.g. RL40_S57). Features enriched in cKO and WT BMDMs are highlighted in red and blue, respectively. (D–F) Volcano plots displaying differentially expressed global proteins (D), phospho- (E) and KGG(Ub)-sites (F) between infected and uninfected BMDMs. Infected refers to the aggregate condition in which early and late infected samples for WT and cKO are each weighted as 0.25 relative to 0.5 each for the WT and cKO uninfected samples. Features enriched in infected and uninfected BMDMs are highlighted in red and blue, respectively. As above, bar graphs below each panel show example hits. The relative abundance of TMT reporter ions sums up to 2.0 for features quantified in both Plex1 and Plex2.

Similar analysis of the infected datasets revealed the dynamic nature of the macrophage response to infection, irrespective of ATG16L1 genotype. For example, global proteome analysis revealed broad changes in pro-inflammatory cytokines and chemokines at early (GROA), late (CXL10, IL1A, IL1B) or both (CCL2, TNFA) time-points, as well as marked changes in several key cell surface receptors (Figure 3D, Figure 3—figure supplement 1A and B). Time-dependent changes were also observed for components of innate immune signaling that intersect with the ubiquitin pathway (PELI1/Pellino), kinase-phosphatase signaling (DUS1/Dusp1), and interferon-mediated GTP/GDP signaling (GBP5) (Figure 3—figure supplement 1C). For phosphorylation, notable examples included tyrosine-431 of the PI3-kinase regulatory subunit (P85A_Y431) which decreased rapidly upon infection in both genotypes, and S379 of the interferon regulatory factor (IRF3_S379) which was increased following infection, particularly in ATG16L1-deficient cells (Figure 3E). For ubiquitination, marked effects were seen for the selective autophagy receptor Tax1BP1 (TAXB1_K618) and an E3 ubiquitin ligase Pellino (PELI1_K202) (Figure 3F), both of which have defined roles at the intersection of cell death and innate immune signaling (Choi et al., 2018; Gao et al., 2011; Parvatiyar et al., 2010). Finally, annotation of the S.flexneri proteome permitted quantification of pathogen-derived proteins at the global level. These can be identified using the search term ‘*SHIFL’ in the interactive dashboard provided below.

To facilitate further exploration of pathways within these data, we have prepared interactive Spotfire dashboards and provided them as a resource to the community. These can be accessed at the following URL: https://info.perkinelmer.com/analytics-resource-center.

Characterizing PTMs of autophagy proteins and inflammatory signaling nodes revealed by loss of Atg16l1 and infection

To effectively integrate data for each protein within a single consolidated view, heatmaps were assembled to show proteome level changes alongside any PTMs that were quantified in the phospho- and KGG(Ub)-enriched samples. Using the selective autophagy receptor Tax1BP1 (TAXB1) as an example, heatmaps depict relative abundance of features present in one or both experiments (Plex1 and/or Plex2) (Figure 4A). Comparisons of interest include cKO versus WT (cKO/WT) for Uninfected (U), Early (E) and Late (L) infection time-points. For TAXB1, these show that the global protein level is elevated upon Atg16l1 deletion, as are a number of individual phosphorylation and ubiquitination sites. These include features quantified in one (e.g. T494, K624) or both plexes (e.g. S632, S693, K627). Additional comparisons depict time-dependent differences between infected and uninfected conditions for each genotype – namely early versus uninfected (E/U) and late versus uninfected (L/U). For TAXB1, certain PTMs such as phosphorylation at S632 and ubiquitination at K624 and K627 track with the protein, while other PTMs such as phosphorylation at threonine-494 (T494) and S693 display time-dependent changes that diverge from the underlying protein level (Figure 4A). Shown individually, histograms depict the relative abundance of TAXB1 and its specific PTMs (Figure 4B) to mirror what is shown in the combined heatmaps (Figure 4A).

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Characterization of proteomic changes in the autophagy pathway.

(A) Heatmap representation of log2 fold changes for global proteome (unmarked), phospho-(yellow section) or KGG(Ub)-sites (purple section) measurements made for TAXB1. Data are shown for features quantified from uninfected (U) WT and cKO BMDMs or cells infected at early (E) or late (L) time-points with S.flexneri. Log2 transformed ratios are shown for contrasting genotypes (cKO/WT) at each infection timepoint (U, E, L) on the left and between infection timepoints (E/U and L/U) within each genotype on the right. Gray boxes denote quantification of the feature in Plex1 and/or Plex2. Modification sites on TAXB1 denote the modified amino acid (S/T/Y/K) and residue number. (B) Bar graphs showing the relative abundance of TAXB1 global protein and representative phospho- and KGG(Ub)-sites in each of the six conditions. Note that TAXB1_K624 (Plex1) and TAXB1_T494 (Plex2) represent data collected only in a single Plex, with the relative abundance of TMT reporter ions summing up to 1.0. (C) Schematic representation of macro-autophagy and selective autophagy machinery. (D and E) Heatmap representations of E1/E2/E3-like pathway components responsible for conjugating LC3 (MLP3A) to regulate autophagosome membrane elongation (D) and selective autophagy receptors (E). The background shading for each panel corresponds to the functional color coding of proteins in the pathway schematic shown in (C). See Supplementary file 2 for a curated list of PTMs.

One pathway where we expected to see marked proteome and PTM level changes upon infection was in autophagy (Figure 4C and Figure 4—figure supplement 1). We confirmed genotype-dependent loss of each component of the ATG16L1-ATG5~ATG12 complex that conjugates LC3 (MLP3A) to phosphatidylethanolamine (Figure 4D). Only modest changes were seen in the core autophagy machinery following infection, with the most notable effects being differential phosphorylation of FIP200 (RBCC1), ATG2B, and VPS15/p150 (PI3R4) (Figure 4—figure supplement 1C–E). More substantial effects were seen for phosphorylation events on autophagy receptors such SQSTM/p62 and Optineurin (OPTN) (Figure 4E), in addition to TAXB1 (Figure 4A). In the case of SQSTM/p62, singly and multiply phosphorylated forms of T269, T271, T272, S275/6, S277 were elevated in ATG16L1-deficient macrophages. S28 phosphorylation of SQSTM/p62 was previously described to regulate activation of the antioxidant response (Xu et al., 2019a). We detected a substantial increase in basal SQSTM_S28 phosphorylation in cKO BMDMs, indicating that ATG16L1 deficiency may impact oxidative stress (Figure 4—figure supplement 1F). Previous work from several groups have demonstrated that TBK1 regulates OPTN and SQSTM through phosphorylation, although the sites quantified in this dataset differ from those previously characterized in detail (Heo et al., 2015; Matsumoto et al., 2015; Richter et al., 2016).

Our PTM datasets showed dynamic regulation for a range of inflammatory signaling components by infection as well as autophagy (Figure 4—figure supplement 2). For example, we detected ubiquitination on K278 of NEMO (Figure 4—figure supplement 2F), consistent with increased LUBAC activity (Tokunaga et al., 2009). Interestingly, the global proteome data reported a peptide with the sequence GGMQIFVK that is derived from linear polyubiquitin chains formed by the LUBAC complex (Figure 3—figure supplement 1D). This linear ubiquitin peptide was elevated upon infection in both WT and cKO BMDMs, further supporting increased E3 ubiquitin ligase activity of LUBAC. As noted above, TAXB1 phosphorylation was induced upon infection at a number of sites (Figure 4A). These changes in TAXB1 correlated with numerous elevated PTMs of the A20 (TNAP3) deubiquitinase, a protein whose anti-inflammatory activity modulates NF-κB signaling (Figure 4—figure supplement 2C). Phosphorylation at TAXB1 at S693 is important for the assembly of TNAP3-containing complex and negative regulation of NF-κB signaling (Shembade et al., 2011; Figure 4A).

We also identified notable changes across numerous components implicated in pathogen sensing such as TLRs, RLRs, NLRs and STING/cGAS (Figure 4—figure supplement 3A and B). Our datasets confirm several previously demonstrated PTMs that occur in response to infection, such as elevated phosphorylation of RIPK1 at S321 (Figure 4—figure supplement 2E), XIAP at S429 or IRF3 on multiple sites (Figure 4—figure supplement 3D and E). Similar effects were observed for ABIN1 (TNIP1), which showed modest changes in global protein levels, but elevated ubiquitination at multiple lysines including K360, K402, K480 at both timepoints and higher levels in cKO than WT (Figure 4—figure supplement 2F). Caspase-8 ubiquitination was elevated at K169 in both WT and cKO early post-infection, but was sustained through the late timepoint only in ATG16L1-deficient BMDMs (Figure 4—figure supplement 2G). Within the ubiquitin pathway, E3 ubiquitin ligases including HOIP (RNF31), TRAF2, and Pellino (PELI1) showed marked infection-dependent changes at the level of phosphorylation (e.g. RNF31_S445) and ubiquitination (e.g. PELI_K202 early, TRAF2_K313 late) (Figure 4—figure supplement 2C).

Cross-referencing all highlighted PTMs with PhosphoSitePlus revealed that ~40% were distinct from those previously identified in large-scale proteomic screens, with only ~15% of PTMs having been studied in connection to a biological function (Supplementary files 2 and 3). This analysis also revealed that nearly 25% of PTMs from the autophagy, innate sensing, inflammatory, and cell death signaling pathways identified in our study appear to be novel (summarized in Table 1).

Table 1
Novel post-translational modifications in specific autophagy, innate sensing, inflammatory signaling and cell death pathways revealed by TMT-MS in WT and cKO BMDMs following S.flexneri infection.

Please refer to heatmaps in Figure 4, Figure 4—figure supplements 1, 2 and 3 for PTM abundance changes.

Autophagy
Post-translational modification
Protein namepSTY/PhosphorylationKGG/Ubiquitination
 ATG5K234
 MLP3A/LC3K39
 TAX1BP1T426, T494K284, K311, K536, K551, K624
 P62/SQSTM1T280. S292, S308
 NBR1T6
 FUND2K114, K121
 TBC17S176K105
 RBCC1/FIP200T642
 PI3R4/VPS15S903, T904
 RUBICS252, S552, S554
 ATG2BS401T1570
Innate sensing
Post-translational modification
Protein namepSTY/PhosphorylationKGG/Ubiquitination
 DDX58/RIG-IK256
 MAVSY332
 CGASK55
 TLR4K692
 MYD88S136
 IRAK2S175, T587, S615
 IRAK3K60, K163, K392
 IRAK4T133, S134, S175_S186
 TBK1S509
 IRF3T126, S130
 IRF7S227, T277
 IFIT1S272, S296K89, K117, K123, K406, K451
 IFIT2K41, K61, K158, K291
 IFIT3S327, S333K246, K252, K266, K396
 ISG15K30
Inflammatory signaling, cell death
Post-translational modification
Protein namepSTY/PhosphorylationKGG/Ubiquitination
 TNFR1B/TNFR2K300
 M3K7/TAK1S331
 TAB2S353, T376, S584
 TRAF1K120
 TRAF2K194
 IKBzT188K5, K120, K132
 NFKB1K275
 RELS321
 RNF31/HOIPS441, S973K911
 TNAP3/A20S217, T567, S622, S730K31, K213
 TNIP1/ABIN1S601K288, K317, K386
 TNIP2/ABIN2T194, S196
 CASP8S60K33, K274
 CFLAR/cFLIPK175, K390
 RIPK1K429
 RIPK2S183, S381K369
 RIPK3S173, S177, S254, T386, T392, T398, T407K145, K230, K298

Elevated oxidative stress and pro-inflammatory signaling in ATG16L1-deficient macrophages contributes to accelerated bacterial killing

To obtain a global overview of the proteomics data, gene set enrichment analysis (GSEA) was performed using Hallmark gene sets to identify which signatures emerged from each of the global proteome (Figure 5—figure supplement 1A), phosphoproteome (Figure 5—figure supplement 1B), and KGG(Ub) data (Figure 5—figure supplement 1C). These results, particularly from the global proteome and KGG(Ub) datasets, showed a significant enrichment of pro-inflammatory gene signatures upon infection (e.g. type I interferon and TNFα signaling), but also at baseline when comparing cKO to WT cells (Figure 5—figure supplement 1A and C). To understand the transcriptional contribution to these proteomic signatures, RNA-Seq analysis was performed on BMDMs infected with S.flexneri. RNA-Seq results likewise showed a significant enrichment of pro-inflammatory gene signatures in cKO cells (e.g. type I interferon and TNFα signaling) (Figure 5—figure supplement 2A and B).

To explore if these pathways contribute to enhanced S.flexneri killing by cKO BMDMs, cells were infected in the presence of recombinant TNFα receptor II-Fc (TNFRII-Fc) to inhibit TNF signaling, or anti-IFNAR1 (α-IFNAR1) to block type I IFN signaling. In contrast to WT BMDMs (Figure 5—figure supplement 2C and F), inhibition of TNFα or type I interferon signaling in cKO cells resulted in a modest rescue of S.flexneri killing (Figure 5—figure supplement 2D, E, G and H), especially at later time points.

Our GSEA comparison between WT and cKO cells identified strong enrichment of the reactive oxygen species (ROS) pathway, both in the global proteome and KGG(Ub) data (Figure 5A Figure 5—figure supplement 1A and Supplementary file 4 for protein set terms). This was true in the global proteome data for both uninfected and infected cells at both time-points (Figure 5—figure supplement 1A), as well as in the KGG(Ub) data specifically for infected cells (Figure 5—figure supplement 1C). This group of proteins included several factors involved in glutathione (GSH) synthesis, such as the glutamate-cysteine ligase regulatory subunit (GSH0/Glcm) and GSH synthetase (GSHB/Gss), as well as GSH regeneration, such as microsomal glutathione S-transferase (MGST1) and NAD(P)H dehydrogenase 1 (NQO1), a major enzyme that assists in reducing oxidative stress in cells (Figure 5B; Hayes et al., 2020). Additionally, several ROS converting enzymes including catalase (CATA) and peroxiredoxin 1 (PRDX1) were also elevated in cKO BMDMs at steady state. Furthermore, a subset of these redox regulators changed abundance upon S.flexneri infection. For example, prostaglandin dehydrogenase 1 (PGDH) displayed a time dependent decrease upon infection that was accentuated in cKO versus WT cells, consistent with its known susceptibility to ROS (Figure 5C; Wang et al., 2018). Conversely, levels of the cysteine-glutamate antiporter SLC7A11 (XCT) (Conrad and Sato, 2012; Sato et al., 1999) exhibited a significant increase in cKO BMDMs following infection (Figure 5C). Thus, ATG16L1 deficiency and S.flexneri infection might each independently elevate ROS levels, with ATG16L1 deficiency further driving a compensatory increase in the redox regulators during infection to maintain macrophage viability.

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Elevated oxidative stress in ATG16L1-deficient BMDMs.

(A) Gene set enrichment analysis (GSEA) of global proteome data showing Hallmark gene sets overrepresented in uninfected cKO over WT BMDMs. (B) Bar graphs show the relative abundances for selected proteins involved in redox regulation and detoxifying reactive oxygen species. (C) Volcano plot of global protein changes at early infection timepoint between the genotypes. Proteins enriched in cKO and WT BMDMs are highlighted in red and blue, respectively. Bar graphs showing the cumulative effects of genotype and infection on PGDH and XCT protein levels are shown below. (D) Representative images from experiments shown in (E) demonstrating CellRox probe intensity, Hoechst nuclear staining and merged images (scale bar 25 μM). (E) Quantification of CellRox green mean intensity in WT and cKO BMDMs. Graph shows single cell data representative of three independent experiments. Unpaired t test ****p<0.0001.

We next asked whether ATG16L1-deficient BMDMs exhibit elevated basal oxidative stress using a fluorogenic probe (CellRox green) that enables cellular ROS measurements via confocal fluorescence microscopy. Despite upregulation of numerous redox regulatory factors, CellRox probe intensity was significantly higher in cKO BMDMs (Figure 5D and E; see also Figure 5—figure supplement 3A for high content imaging). We also detected an increase in the ratio between oxidized versus reduced GSH (GSSH/GSH) in lysates of cKO BMDMs (Figure 5—figure supplement 3B–D). Additionally, abundance of NQO1 was significantly higher in cKO BMDMs as determined by a specific NQO1-activated fluorescent probe (Figure 5—figure supplement 3E and F; Punganuru et al., 2019). Altogether, these findings indicate that cKO BMDMs are basally exposed to higher oxidative stress, and suggest that upregulation of redox homeostasis factors is required to maintain their viability (Tal et al., 2009). To test this hypothesis, we treated BMDMs with Erastin, a small molecule inhibitor of XCT which increases oxidative stress by diminishing the levels of reduced GSH in cells (Dixon et al., 2012). Time-course studies demonstrated that BMDMs exhibited decreased viability upon prolonged XCT inhibition, and revealed a greater dependency on this pathway when Atg16l1 is deleted (Figure 6—figure supplement 1A). Given the central role of autophagy in mitochondrial turnover, we analyzed mitochondrial morphology and respiration as a possible source of oxidative damage in uninfected cKO BMDMs (Figure 5—figure supplement 3G and H). However, mitochondrial phenotypes remained consistent between WT and cKO groups. This warrants further investigation into the underlying mechanism(s) of elevated oxidative stress in ATG16L1-deficient BMDMs and posits that altered homeostasis of other organelles may contribute to the ROS accumulation in cKO BMDMs. Interestingly, the KGG(Ub) data displayed a marked response in the peroxisome gene set in cKO cells over WT (Figure 5—figure supplement 1C), suggesting a possible link between this organelle and the elevated ROS signature.

Based on these observations, we hypothesized that higher levels of basal oxidative stress in cKO BMDMs may drive their enhanced ability eliminate cytosolic S.flexneri. To test this, we modulated oxidative stress in BMDMs using either Erastin, an oxidative stress inducer, or butylated hydroxyanisole (BHA), a ROS scavenger. BMDMs were pre-treated with Erastin or BHA prior to infection and the treatments were maintained throughout the time-course experiments (schematic in Figure 6—figure supplement 1B). Importantly, neither Erastin (Figure 6—figure supplement 1C and D) nor BHA (Figure 6—figure supplement 1E and F) treatments increased BMDM cell death throughout the experimental time-course. However, Erastin-treated BMDMs exhibited enhanced elimination of S.flexneri at early time-points following infection (Figure 6A–C). Conversely, BHA treatment promoted S.flexneri survival in BMDMs (Figure 6D–F). Together, these findings demonstrate a role for ATG16L1 in restraining oxidative stress, which in turn constrains macrophage microbicidal capacity.

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Manipulation of ROS levels modulates S.flexneri clearance.

(A, B) Comparison of CFUs per well from three independent infection experiments in the presence of 4 μg/ml Erastin using BMDM preparations from three different Atg16l1-WT (A) or Atg16l1-cKO (B) mice. In A, 0.5 hr *p=0.04, 1 hr *p=0.01, 2 hr ns, non-significant p=0.11, multiple t-test comparison. In (B), 0.5 hr ns p=0.08, 1 hr **p=0.005, 2 hr **p=0.009, multiple t-test comparison. (C, F) Representative serial dilutions from gentamycin protection assays following S.flexneri M90T infection of WT or cKO BMDMs in the presence of Erastin 4 μg/ml, (C, 1 hr timepoint) or BHA 150 μM (F, 3 hr timepoint). (D, E) Comparison of CFUs per well from four independent infection experiments in the presence of 150 μM BHA using BMDM preparations from four different Atg16l1-WT (D) or Atg16l1-cKO (E) mice. In (D), 2 hr *p=0.02, 3 hr ns, non-significant p=0.13, 4 hr ns p=0.14, multiple t-test comparison. In (E), 2 hr *p=0.01, 3 hr *p=0.03, 4 hr *p=0.02, multiple t-test comparison.

Discussion

Emerging insights from genetic mouse models have revealed that loss of Atg16l1 in the immune and epithelial compartments lowers the threshold for an inflammatory response (Cadwell et al., 2010; Hubbard-Lucey et al., 2014; Lim et al., 2019; Matsuzawa-Ishimoto et al., 2017). Consistently, deletion of autophagy-related genes in the innate and adaptive immune compartments have demonstrated enhanced pathogen clearance (Marchiando et al., 2013; Martin et al., 2018; Samie et al., 2018; Wang et al., 2020) as well as tumor control in vivo (Cunha et al., 2018; DeVorkin et al., 2019; Lim and Murthy, 2020). These observations prompted a re-evaluation of the pathway in antimicrobial immunity to better understand how loss of autophagy-related genes impacts cell-autonomous innate immunity against intracellular bacteria.

In this study, we show that macrophages lacking Atg16l1 demonstrate enhanced killing of S.flexneri. To identify mechanisms behind this phenotype we employed TMT-based multiplexed proteomic analysis – a technology capable of near-comprehensive characterization of the global proteome (Lapek et al., 2017). When isobaric multiplexing methods are coupled with enrichment, it enables quantification of PTMs on thousands of individual proteins (Rose et al., 2016). This method is ideally suited for interrogation of a complex response such as infection of a host cell with an intracellular pathogen, where the diversity of downstream changes does not lend themselves to candidate approaches involving immunoblotting.

Our approach identifies multiple novel PTMs in components of inflammatory cytokine signaling, innate sensing and the core autophagy machinery that emerge as a consequence of S.flexneri infection. The comparison of early and late infection time-points shows complex dynamics that reflect PTM as well as global protein abundance. The comparison of WT versus ATG16L1-deficient BMDMs further reveals critical nodes in each of the above pathways that are under regulatory control by autophagy. The PTMs listed in Table 1 and Supplementary files 2 and 3 represent a sizeable fraction of the relevant post-translational changes that occur in macrophages during infection and/or upon loss of autophagy. We have provided interactive, web-accessible Spotfire Dashboards to enable user interrogation of the global proteome, phosphoproteome, and the KGG(Ub) datasets (https://info.perkinelmer.com/analytics-resource-center).

Our study reveals that basal accumulation of cellular ROS in cKO BMDMs enforces a compensatory increase in antioxidant responses exemplified by elevated protein abundances of key components of the glutathione metabolic pathway. This permits cellular viability under relatively elevated cytosolic ROS levels, which in turn suppresses S.flexneri expansion in BMDMs. However, overall macrophage fitness is likely compromised owing to a shift in the basal redox pathway set-point. Pharmacological depletion of GSH phenocopies genetic loss of Atg16l1 and enhances S.flexneri clearance in wild-type cells, whereas the ROS scavenger BHA reverses this phenotype, demonstrating a direct role for cellular ROS in bacterial clearance upon loss of autophagy.

Mice lacking Atg16l1 in myeloid cells also demonstrate accelerated hepatic bacterial clearance in vivo. However, these findings have some limitations. First, murine enteric infection by S.flexneri requires deletion of the NAIP-NLRC4 inflammasome in intestinal epithelial cells (recently described in Mitchell et al., 2020). Thus, the scope of our observations is limited by the availability of murine models and primarily reveal a role of ATG16L1 in myeloid cells during acute bacterial infection. Second, we observe a liver-specific impact of ATG16L1 deletion in the myeloid compartment because splenic S.flexneri colonization was comparable between genotypes, while no colonization of lung tissue was detected. However, the broad deletion of Atg16l1 in the myeloid lineage via LysM-Cre raises a possibility that Kupffer cells alone may not be the sole drivers of accelerated hepatic clearance in vivo. Additional genetic models are required to directly test the role of autophagy in Kupffer cells, and the intestinal epithelium, as relevant sites of mucosal antimicrobial immunity.

In addition to demonstrating a role for autophagy in antimicrobial immunity via modulation of oxidative stress, our study provides the most comprehensive multiplexed proteomic analysis to date of the macrophage response to cytosolic bacterial infection. We hope this novel resource will be of broad utility to the study of myeloid signal transduction, host-pathogen interaction, and innate immunity.

Materials and methods

Mice

All animal experiments were performed under protocols approved by the Genentech Institutional Animal Care and Use Committee. Generation of myeloid-specific deletion of Atg16L1 was achieved by crossing Lyz2-Cre + mice with Atg16L1loxp/loxp mice (Atg16l1-cKO) and was described previously (Murthy et al., 2014). All mice were bred onto the C57BL/6N background. All in vivo experiments were performed using age-matched colony controls.

Bacterial strains and culture

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Shigella flexneri 5a strain M90T used in this study was obtained from ATCC (ATCC BAA-2402). Frozen bacterial stocks were streaked onto tryptic soy agar (TSA) plates and grown at 37°C overnight. Plates were kept at 4°C for up to 2 weeks.

Bone-marrow-derived macrophage isolation

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Femurs and tibias were collected aseptically. After removing most of the muscle and fat, the epiphyses were cut and bones were placed into PCR tubes individually hung by the hinge into a 1.5 ml Eppendorf. The bone marrow was flushed by short centrifugation at 10,000 rpm for 30 s. Red blood cells were lysed with RBC lysis buffer (Genentech) by incubating for 5 min at RT. Cells were then pelleted and resuspended in BMDM media [high glucose Dulbecco’s Minimum Essential Media (DMEM) (Genentech) + 10% FBS (heat inactivated, custom manufactured for Genentech)+GlutaMAX (Gibco, 30050–061)+Pen/Strep (Gibco, 15140–122) supplemented with 50 ng/ml recombinant murine macrophage-colony stimulating factor (rmM-CSF, Genentech)] and plated in 15 cm non-TC treated dishes for 5 days (Petri dish, VWR, 25384–326). Fresh BMDM media was added on day three without removal of original media. On day 5, macrophages were gently scraped from dishes, counted and re-plated on TC-treated plates of the desired format for downstream assays in fresh BMDM media. After overnight culture in BMDM media, assays were performed on day 6 BMDMs.

BMDM infections in 24-well plates

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BMDMs isolated from control Atg16l1-WT or Atg16l1-cKO mice were plated at 2.5 × 105 cells/well in 24-well assay plates (Corning, 353047) in BMDM media. A duplicate plate was always plated for total PI-positive cell number enumeration after overnight incubation using IncuCyte ZOOM as described elsewhere. Bacterial cultures were prepared by picking a single bacterial colony from TSA plates and grown in 10 mL tryptic soy broth (TSB) in a shaking incubator overnight at 37°C. After overnight incubation, bacteria were sub-cultured in fresh 10 mL of TSB at 37°C until OD600 0.5–0.8, pelleted by centrifugation, resuspended in 1:1000 poly-L-lysine (Sigma-Aldrich, P4707) in PBS and incubated for 10 min at RT. Cell suspension was then centrifuged and the pellet washed twice with PBS and once with the infection media [high glucose DMEM (Genentech) +10% FBS (heat inactivated, custom manufactured for Genentech)+GlutaMAX (Gibco, 30050–061)]. After the final wash the bacterial pellet was resuspended in the infection media and OD600 was remeasured. To prepare multiplicity of infection (MOI) of 5 in the infection media, total PI-positive object count per well was used for accurate MOI calculations for every independent infection experiment. A cell suspension containing lysine coated bacteria were added to the wells at MOI five in a total volume of 250 μl/well and allowed to adhere by incubating for 30 min at 37°C in a CO2 incubator. After 30 min, bacterial suspension was aspirated and replaced with 500 μl/well of fresh infection media supplemented with gentamicin at 50 μg/mL (Sigma-Aldrich, G1397). This was defined as the time-point T = 0 min. Assay plates were subsequently incubated at 37°C in a CO2 incubator and used at the indicated time-points for CFU enumeration.

BMDM infections in 24-well plates with compounds and antibodies

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For experiments with Erastin (Sigma-Aldrich, E7781), BHA (Sigma-Aldrich, 78943), anti-IFNAR1 (Leinco Technologies, I-401), isotype control antibody clone HKSP (Leinco Technologies, I-536) or TNFRII-Fc (R and D, 9707-R2) day 5 BMDMs were plated at 2.5 × 105 cells/well in 24-well assay plates (Corning, 353047) in BMDM media and incubated overnight. A duplicate plate was also seeded and used for PI-positive object count per well enumeration to ensure accurate MOI as described elsewhere. For experiments with Erastin, BMDMs were plated in media supplemented with Erastin at 4 μg/ml and incubated at 37°C in a CO2 incubator for 18 hr before infection. For experiments with BHA, anti-IFNAR1 or TNFRII-Fc overnight media was replaced with fresh media containing 150 μM BHA, 5 μg/ml anti-IFNAR1, or 500 ng/ml TNFRII-Fc and incubated for 2 hr prior infection. The bacterial culture was prepared essentially as described elsewhere with the following modifications. After the final wash with infection media the bacterial pellet was resuspended in the infection media, OD600 was remeasured and bacterial suspension of MOI 10 was prepared. A cell suspension containing lysine coated bacteria was mixed 1:1 with infection media containing double concentrations of compounds and added to the wells in a total volume of 250 μl/well and allowed to adhere by incubating for 30 min at 37°C in a CO2 incubator. After 30 min, bacterial suspension was aspirated and replaced with 500 μl/well of fresh infection media supplemented with gentamicin at 50 μg/mL (Sigma-Aldrich, G1397) and compounds as indicated.

BMDM infections in 15 cm dishes for TMT proteomics

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For large scale infections, 5 day differentiated BMDMs isolated from control Atg16l1-WT or Atg16l1-cKO mice were plated at 10 × 106 cells per 15 cm non-TC treated dish (Petri dish, VWR, 25384–326) in BMDM media. Bacterial suspension was prepared essentially as described elsewhere with the following modifications. A suspension of lysine coated bacteria in infection media were added to the dishes containing BMDMs at MOI five in a volume of 15 ml/dish and allowed to adhere by incubating for 30 min at 37°C in a CO2 incubator. After 30 min, the medium was aspirated and replaced with 50 ml/dish of fresh infection media supplemented with gentamicin at 50 μg/mL (Sigma-Aldrich, G1397). This was defined as the time-point T = 0 min. Assay plates were subsequently placed at 37°C in a CO2 incubator and samples collected after 30–45 min incubation (‘early’ infection time-point) or after 3–3.5 hr incubation (‘late’ infection time-point). At the indicated time-points a set of 10 dishes per genotype was used to prepare cell lysates for downstream proteomic analysis. To prepare cell lysates, infection media was first aspirated and cells washed once with PBS. Cells were then scrapped in the presence of Urea lysis buffer (20 mM HEPES pH 8.0, 9M Urea, 1 mM sodium orthovanadate, 2.5 mM sodium pyrophosphate, 1 mM β-glycerolphosphate) and cell suspension stored at −80°C until further processing (Kirkpatrick et al., 2013).

In vivo Shigella flexneri infection

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Mice were injected intravenously in the tail vein with Shigella flexneri (M90T) bacterial culture that was prepared by picking a single bacterial colony from TSA plates and grown in 10 mL tryptic soy broth (TSB) in a shaking incubator overnight at 37°C. After overnight incubation, bacteria were sub-cultured in fresh 10 mL of TSB at 37°C until OD600 0.5–0.8, pelleted by centrifugation, washed with PBS once, resuspended in PBS and OD600 was recounted. Each animal was injected with 100 µl of bacterial suspension in PBS containing 2 × 106 Colony Forming Units (CFUs) S.flexneri strain M90T. Mice were euthanized six or 24 hr post infection to harvest spleen, liver, and lung for CFUs enumeration.

Colony-forming units (CFUs) assays

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To determine CFUs in infected BMDMs, infection media was aspirated, cells were washed once with PBS and lysed by adding 250 µl/well of 0.1% Igepal CA-630 (Sigma-Aldrich, I8896) in PBS, incubated for 5 min, resuspended and an aliquot of 200 µl was transferred to 96-well U-bottom plate (Costar, 3799) for making two-step serial dilutions in 0.1% Igepal CA-630 in PBS. Subsequently, 5 µl of each serial dilution was plated on TSA plates in triplicates, allowed to evaporate at RT after which the plate was placed in a 37°C incubator overnight. After overnight incubation, colonies from individual dilutions were counted and used for determining CFUs per well. To determine CFUs in the liver, spleen, and lung, mice were euthanized at the indicated time-points after infection and the organs surgically removed and placed in PBS on ice. Livers were processed in 5 ml of 0.1% Igepal CA-630 (Sigma-Aldrich, I8896) in PBS using the gentleMACS C Tubes (Miltenyi Biotec, 130-096-334) in combination with the gentleMACS Octo Dissociator (Miltenyi Biotec, 130-095-937) for the automated dissociation of tissues using standard tissue dissociation programs (program sequence: m_liver_01_02; m_liver_02_02, m_liver_01_02). Tissue suspensions were filtered through 100 µM filters (CellTreat, 229485) and remaining liver tissue was additionally homogenized using the rubber seal of the 5 ml syringe plunger. For the spleen and lung, organs were processed in 2 ml of 0.1% Igepal and dissociated using the rubber seal of the 5 ml syringe plunger before filtering through 70 µM filters. The resultant tissue suspension was used for generating serial dilutions and plated on TSA plates for CFUs enumeration as described elsewhere.

IncuCyte assays

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For IncuCyte assays, BMDMs were plated at 2 × 104 cells/well in flat-bottom 96-well (Corning, 353072) or at 2.5 × 105 cells/well in 24-well (Corning, 353047) assay plates. After overnight incubation at 37°C in a CO2 incubator, cells were used for infection experiments or treatments with compounds or growth factors as indicated. BMDM viability over time was assessed by supplementing assay media [(high glucose DMEM (Genentech) +10% FBS (heat inactivated, custom manufactured for Genentech)+GlutaMAX (Gibco, 30050–061)+Pen/Strep (Gibco, 15140–122))] with propidium iodide (PI) dye for live-cell imaging at 1:1000 (Invitrogen, P3566), and then measuring PI-positive cells per mm2 using live cell imaging with IncuCyte ZOOM (IncuCyte systems, Essen Biosciences) in a time-course experiment. Percent cell death was calculated by dividing PI-positive cells per mm2 with total plated cells per mm2. Total plated cells were enumerated from a duplicate plate seeded at the same time as the assay plates. After overnight incubation, media in the duplicate plate was exchanged to assay media containing 0.06 % NP-40 supplemented with 1:1000 PI, and imaged at a single time-point using IncuCyte ZOOM after 10 min incubation.

GSH assays

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BMDMs were established as described and 5 × 106 of BMDMs were pelleted by centrifugation, the pellet was lysed in mammalian lysis buffer (Abcam, ab179835), incubated 10 min at RT and centrifuged at top speed at 4°C 15 min. Supernatant was transferred to a fresh tube and used for deproteinization following manufacturer's instructions (Abcam, ab204708). The resultant supernatant was used for determining reduced GSH, total GSH and oxidized GSSG was calculated as per manufacturer’s instructions (Abcam, ab138881).

Flow cytometry analysis

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The NQO1 activity probe was generated in-house as described in Punganuru et al., 2019. The endogenous NQO1 activity of WT and cKO BMDMs was determined by flow cytometry after exposure to the probe. Cells were incubated with 10 μM probe for 60 min, harvested by non-enzymatic dissociation (Gibco, 13150–016) and washed with PBS. About 1 × 104 cells were analyzed by flow cytometry (λex = 488 nm). Mean fluorescence intensity (MFI) readout from each independent experiment was used for the graph.

RNA-Seq

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BMDMs were established from three pairs of control Atg16l1-WT or Atg16l1-cKO mice and infected with S.flexneri strain M90T as described elsewhere. Samples were collected after 45 min incubation (‘early’ infection time-point), 3.5 hr incubation (‘late’ infection time-point) or from uninfected samples. RNA was isolated according to manufacturer’s protocol (Qiagen, 74104) and RNA-sequencing data were analyzed using HTSeqGenie (Pau and Reeder, 2021) in BioConductor (Huber et al., 2015) as follows: first, reads with low nucleotide qualities (70% of bases with quality <23) or rRNA and adapter contamination were removed. The reads that passed were then aligned to the mouse reference genome GRCm38.p5 using GSNAP (Wu and Nacu, 2010) version ‘2013-10-10-v2’ with alignment parameters: ‘-M 2 n 10 -B 2 -i 1 N 1 w 200000 -E 1 --pairmax-rna=200000'. Alignments of the reads that were reported by GSNAP as ‘uniquely mapping’ were used for subsequent analysis. Gene expression levels were quantified as Reads Per Kilobase of exon model per Million mapped reads normalized by size factor (nRPKM), defined as number of reads aligning to a gene in a sample / (total number of uniquely mapped reads for that sample x gene length x size factor). Differential expression and gene set enrichment analysis were performed with voom +limma [PMID 27441086] and bioconductor package fgsea (Fast Gene Set Enrichment Analysis) (Korotkevich et al., 2021), respectively.

Fluorescence microscopy

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BMDMs grown on 96-well plates (Greiner Bio, 655090) were treated with 10 µM CellRox Green reagent for 30 min according to manufacturer’s protocol (Thermo Fisher Scientific, C10444), then fixed in 4% paraformaldehyde (PFA) solution in PBS (ChemCruz, SC281692) for 15 min at RT. Nuclei were stained with NucBlue Live ReadyProbes Reagent (Thermo Fisher Scientific, R37605) for 10 min in PBS. 3D confocal images corresponding to 12-µm-thick z-stacks of four stitched fields of views were collected on a Nikon A1R scanning confocal microscope using a Plan Apo NA 0.75 lens and x20 magnification. FITC and Hoechst 33342 signals were respectively imaged with the 488 nm and 405 nm laser lines. For each Z stack, images were combined into one focused image using Nikon Elements Extended Depth of focus (EDF) module that picks the focused regions from each frame and merges them together into a single focused image. The focused EDF images from different conditions were then analyzed with Bitplane Imaris software (version 9.2.0) using the cell segmentation module and intensity quantification. To specifically determine the cytoplasmic CellRox Green reagent intensity, the region corresponding to the Hoechst staining was excluded and FITC channel threshold was applied across all samples per given experiment. Mean cytosolic CellRox Green assay signal was then quantified per each individual cell and presented in the graphs.

Transmission electron microscopy

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Samples were fixed in modified Karnovsky’s fixative (2% paraformaldehyde and 2.5% glutaraldehyde in 0.1M sodium cacodylate buffer, ph7.2) and then post-fixed in freshly prepared 1% aqueous potassium ferrocyanide- osmium tetroxide (EM Sciences, Hatfield, PA), for 2 hr followed by overnight incubation in 0.5% Uranyl acetate at 4°C. The samples were then dehydrated through ascending series of ethanol (50%, 70%, 90%, 100%) followed by propylene oxide (each step was for 15 min) and embedded in Eponate 12 (Ted Pella, Redding, CA). Ultrathin sections (80 nm) were cut with an Ultracut microtome (Leica), stained with 0.2% lead citrate and examined in a JEOL JEM-1400 transmission electron microscope (TEM) at 80kV. Digital images were captured with a GATAN Ultrascan 1000 CCD camera.

Tandem mass tag proteomics

Protein precipitation

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Protein concentration in the lysates were quantified using the Pierce micro-BCA assay (ThermoFisher Scientific, Waltham, MA). All protein from the cell lysates were precipitated with a combination of methanol/chloroform/water (Wessel and Flügge, 1984). In brief, 1X volume of lysate was mixed with 4X volume of methanol followed by 2X volume of chloroform and 3X volume of water. The protein pellets were washed a total of three times with 5X volume of methanol. The protein pellets were air dried and resuspended in 8M urea, 100 mM EPPS pH 7.0, 5 mM DTT. Proteins were alkylated with 15 mM N-ethylmaleimide (Sigma).

LysC/trypsin digestion

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The protein in 8M urea was diluted to 4M with 100 mM EPPS, pH 8.0. 15 mg of protein/sample was digested at 25°C for 12 hr with lysyl endopeptidase (LysC, Wako Chemicals USA) at a 1:25; protein:protease ratio. Following LysC digestion the peptides in 4M urea were diluted to 1M urea with 100 mM EPPS, pH 8.0. The LysC peptides were digested with trypsin at 37 °C for 8 hr (Promega) at a 1:50; protein:protease ratio.

Ubiquitin remnant peptide enrichment (KGG peptides)

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Prior to KGG peptide enrichment, the tryptic peptides were acidified to 2% formic acid and desalted with 1 g tC18 Sep-Pak cartridges (Waters). The desalted peptides were dried by vacuum. KGG peptide enrichment was performed with the PTMScan ubiquitin remnant motif kit (Cell Signaling Technologies, Kit#5562) as per the manufacturers protocol. KGG peptides eluted from the antibodies were dried by vacuum. The flow through peptides from the KGG enrichment were saved for phosphopeptide and total protein analysis.

TMT labeling of KGG peptides

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Peptides were resuspended in 200 mM EPPS, pH 8.0. 10 µL of TMT reagent at 20 µg/uL (ThermoFisher) was added to each sample. Peptides were incubated with TMT reagent for 3 hr at 25°C. TMT-labeled peptides were quenched with hydroxylamine (0.5% final) and acidified with trifluoroacetic acid (2% final). The samples were combined, desalted with 50 mg tC18 Sep-Paks, and dried by vacuum.

Ubiquitin remnant peptide fractionation

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TMT-labeled KGG peptides were fractionated using the high pH reversed-phase peptide fractionation kit (ThermoFisher). The dried KGG peptides were resuspended in 0.1% trifluoroacetic acid and fractionated according to the manufacturers protocol into six fractions (17.5%, 20%, 22.5%, 25%, 30%, and 70% acetonitrile +0.1% triethylamine). The KGG peptide fractions were dried by vacuum, desalted with StageTips packed with Empore C18 material (3M, Maplewood, MN.), and dried again by vacuum. KGG peptides were reconstituted in 5% formic acid +5% acetonitrile for LC-MS3 analysis.

TMT labeling of KGG flow through peptides

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The flow through peptides from the KGG enrichment were labeled with TMT prior to phosphopeptide enrichment. The flow through peptides were resuspended in 1X IAP buffer from the ubiquitin remnant kit (from prior step). The pH of the resuspended peptides was adjusted by adding 1M EPPS, pH 8.0 in a 3:1 ratio (peptide volume:1M EPPS volume; 250 mM EPPS final). 2.1 mg of peptide from each sample was labeled with 2.4 mg of TMT reagent resuspended in 60 µL, 100% acetonitrile. The peptides were incubated with TMT reagent for 3 hr at 25°C. TMT-labeled peptides were quenched with hydroxylamine (0.5% final) and acidified with trifluoroacetic acid (2% final). The samples were combined, desalted with 1 g tC18 Sep-Paks, dried by vacuum.

Phosphoserine, -threonine, -tyrosine enrichment and fractionation

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Phosphotyrosine (pY) peptides were enriched using the Cell Signaling Technologies pY-1000 antibody kit as per the manufacturers protocol (Cell Signaling Technologies, Kit#8803). The flow through from the pY enrichment was desalted on a 1 g tC18 Sep-Pak cartridge (Waters Corporation, Milford, MA) and dried by centrifugal evaporation and saved for phosphoserine and phosphothreonine (pST) analysis. pST phosphopeptides were enriched using the Pierce Fe-NTA phospho-enrichment kit (ThermoFisher). In brief, peptides were bound and washed as per manufacturers protocol. Phosphopeptides were eluted from the Fe-NTA resin with 50 mM HK2PO4 pH 10.5. Labeled phosphopeptides were subjected to orthogonal basic-pH reverse phase fractionation on a 3 × 100 mm column packed with 1.9 µm Poroshell C18 material (Agilent, Santa Clara, CA), utilizing a 45 min linear gradient from 8% buffer A (5% acetonitrile in 10 mM ammonium bicarbonate, pH 8) to 30% buffer B (acetonitrile in 10 mM ammonium bicarbonate, pH 8) at a flow rate of 0.4 ml/min. Ninety-six fractions were consolidated into 18 samples, acidified with formic acid and vacuum dried. The samples were resuspended in 0.1% trifluoroacetic acid, desalted on StageTips and vacuum dried. Peptides were reconstituted in 5% formic acid +5% acetonitrile for LC-MS3 analysis. The flow-through peptides from the pST enrichment were saved for total protein analysis.

Peptide fractionation for total protein analysis

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The flow-through from the pST enrichment was dried by centrifugal evaporation. The dried peptides were resuspended in 0.1% TFA. Approximately 250 µg of peptide mix was subjected to orthogonal basic-pH reverse phase fractionation on a 3 × 100 mm column packed with 1.9 µm Poroshell C18 material (Agilent, Santa Clara, CA), utilizing a 45 min linear gradient from 8% buffer A (5% acetonitrile in 10 mM ammonium bicarbonate, pH 8) to 35% buffer B (acetonitrile in 10 mM ammonium bicarbonate, pH 8) at a flow rate of 0.4 ml/min. Ninety-six fractions were consolidated into 12 samples, acidified with formic acid and vacuum dried. The samples were resuspended in 5% formic acid, desalted on StageTips and vacuum dried. Peptides were reconstituted in 5% formic acid +5% acetonitrile for LC-MS3 analysis.

Mass spectrometry analysis

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All mass spectra were acquired on an Orbitrap Fusion Lumos coupled to an EASY nanoLC-1000 (or nanoLC-1200) (ThermoFisher) liquid chromatography system. Approximately 2 µg of peptides were loaded on a 75 µm capillary column packed in-house with Sepax GP-C18 resin (1.8 µm, 150 Å, Sepax Technologies) to a final length of 35 cm. Peptides for total protein analysis were separated using a 180 min linear gradient from 8% to 23% acetonitrile in 0.1% formic acid. The mass spectrometer was operated in a data-dependent mode. The scan sequence began with FTMS1 spectra (resolution = 120,000; mass range of 350–1400 m/z; max injection time of 50 ms; AGC target of 1e6; dynamic exclusion for 60 s with a ± 10 ppm window). The ten most intense precursor ions were selected for ITMS2 analysis via collisional-induced dissociation (CID) in the ion trap (normalized collision energy (NCE) = 35; max injection time = 100 ms; isolation window of 0.7 Da; AGC target of 2e4). Following ITMS2 acquisition, a synchronous-precursor-selection (SPS) MS3 spectrum was acquired by selecting and isolating up to 10 MS2 product ions for additional fragmentation via high energy collisional-induced dissociation (HCD) with analysis in the Orbitrap (NCE = 55; resolution = 50,000; max injection time = 110 ms; AGC target of 1.5e5; isolation window at 1.2 Da for +2 m/z, 1.0 Da for +3 m/z or 0.8 Da for +4 to+6 m/z). pY peptides were separated using a 180 min linear gradient from 7% to 26% acetonitrile in 0.1% formic acid. The mass spectrometer was operated in a data dependent mode. The scan sequence began with FTMS1 spectra (resolution = 120,000; mass range of 350–1400 m/z; max injection time of 50 ms; AGC target of 1e6; dynamic exclusion for 75 s with a ± 10 ppm window). The ten most intense precursor ions were selected for FTMS2 analysis via collisional-induced dissociation (CID) in the ion trap (normalized collision energy (NCE) = 35; max injection time = 150 ms; isolation window of 0.7 Da; AGC target of 3e4; m/z = 2–6; Orbitrap resolution = 15 k). Following FTMS2 acquisition, a synchronous-precursor-selection (SPS) MS3 method was enabled to select five MS2 product ions for high energy collisional-induced dissociation (HCD) with analysis in the Orbitrap (NCE = 55; resolution = 50,000; max injection time = 300 ms); AGC target of 1e5; isolation window at 1.2 Da. pST peptides were separated using a 120 min linear gradient from 6% to 26% acetonitrile in 0.1% formic acid. The mass spectrometer was operated in a data-dependent mode. The scan sequence began with FTMS1 spectra (resolution = 120,000; mass range of 350–1400 m/z; max injection time of 50 ms; AGC target of 1e6; dynamic exclusion for 60 s with a ± 10 ppm window). The ten most intense precursor ions were selected for ITMS2 analysis via collisional-induced dissociation (CID) in the ion trap (normalized collision energy (NCE) = 35; max injection time = 200 ms; isolation window of 0.7 Da; AGC target of 2e4). Following MS2 acquisition, a synchronous-precursor-selection (SPS) MS3 method was enabled to select five MS2 product ions for high-energy collisional-induced dissociation (HCD) with analysis in the Orbitrap (NCE = 55; resolution = 50,000; max injection time = 300 ms; AGC target of 1e5; isolation window at 1.2 Da for +2 m/z, 1.0 Da for +3 m/z or 0.8 Da for +4 to+6 m/z).

KGG peptides were separated using a 180 min linear gradient from 7% to 24% acetonitrile in 0.1% formic acid. The mass spectrometer was operated in a data dependent mode. The scan sequence began with FTMS1 spectra (resolution = 120,000; mass range of 350–1400 m/z; max injection time of 50 ms; AGC target of 1e6; dynamic exclusion for 75 s with a ± 10 ppm window). The 10 most intense precursor ions were selected for FTMS2 analysis via collisional-induced dissociation (CID) in the ion trap (normalized collision energy (NCE) = 35; max injection time = 100 ms; isolation window of 0.7 Da; AGC target of 5e4; m/z 3–6, Orbitrap resolution set to 15 k). Following MS2 acquisition, a synchronous-precursor-selection (SPS) MS3 method was enabled to select 10 MS2 product ions for high-energy collisional-induced dissociation (HCD) with analysis in the Orbitrap (NCE = 55; resolution = 50,000; max injection time = 500 ms; AGC target of 1e5; isolation window at 1.0 Da for +3 m/z or 0.8 Da for +4 to+6 m/z).

MS/MS spectra for the global proteome, serine/threonine phosphorylated, tyrosine phosphorylated, and ubiquitylated data sets were searched using the Mascot search algorithm (Matrix Sciences) against a concatenated target−decoy database comprised of the UniProt mouse and Shigella flexneri protein sequences (version 2017_08), known contaminants and the reversed versions of each sequence. For all datasets, a 50 ppm precursor ion mass tolerance was selected with tryptic specificity up to two missed cleavages. For the global proteome and serine/threonine phosphorylated datasets a 0.8 Da fragment ion tolerance was selected. While for the tyrosine phosphorylated and KGG (ubiquitin) datasets a 0.02 Da fragment ion tolerance was selected. The global proteome and phosphorylated datasets used a fixed modification of N-ethylmaleimide on cysteine residues (+125.0477) as well as TMT 11-plex on Lysine and the peptide N-term (+229.1629). The ubiquitylated data set used a fixed modification of N-ethylmaleimide on cysteine residues (+125.0477) as well as TMT 11-plex on the peptide N-term (+229.1629). For variable modifications the global proteome dataset used methionine oxidation (+15.9949) as well as TMT 11-plex on tyrosine (+229.1629). The phosphorylated dataset used the same variable modifications as the global proteome dataset plus phosphorylation on serine, threonine, and tyrosine (+79.9663). Finally, the KGG(Ub) dataset used methionine oxidation (+15.9949), TMT 11 plex on tyrosine and lysine (+229.1629), as well as TMT 11 Plex +ubiquitylation on lysine (343.2059). PSMs were filtered to a 1% peptide FDR at the run level using linear discriminant analysis (LDA) (Kirkpatrick et al., 2013). PSM data within each plex and dataset (global proteome, phosphorylation, and ubiquitylation) was aggregated and these results were subsequently filtered to 2% protein FDR. For PSMs passing the peptide and protein FDR filters within the phosphorylated and ubiquitylated datasets, phosphorylation and ubiquitylation site localization was assessed using a modified version of the AScore algorithm (Beausoleil et al., 2006) and reassigned accordingly. Finally, reporter ion intensity values were determined for each dataset and plex using the Mojave algorithm (Zhuang et al., 2013) with an isolation width of 0.7.

Quantification and statistical analysis of global proteomics and phosphoproteomic data

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Quantification and statistical testing of global proteomics data were performed by MSstatsTMT v1.8.0, an open-source R/Bioconductor package (Huang et al., 2020; Tsai et al., 2020). MSstatsTMT was used to create quantification reports and statistical testing reports using the Peptide Spectrum Matches (PSM) as described above. First, PSMs were filtered out if they were (1) from decoy proteins; (2) from peptides with length less than 7; (3) with isolation specificity less than 70%; (4) with reporter ion intensity less than 2^eight noise estimate; (5) from peptides shared by more than one protein; (6) with summed reporter ion intensity (across all eleven channels) lower than 30,000; (7) with missing values in more than nine channels. In the case of redundant PSMs (i.e. multiple PSMs in one MS run corresponding to the same peptide ion), only the single PSM with the least missing values or highest isolation specificity or highest maximal reporter ion intensity was retained for subsequent analysis. Multiple fractions from the same TMT mixture were combined in MSstatsTMT. In particular, if the same peptide ion was identified in multiple fractions, only the single fraction with the highest mean or maximal reporter ion intensity was kept. Next, MSstatsTMT generated a normalized quantification report across all the samples at the protein level from the processed PSM report. Global median normalization, which equalized the median of the reporter ion intensities across all the channels and TMT mixtures, was carried out to reduce the systematic bias between channels. The normalized reporter ion intensities of all the peptide ions mapped to a protein were summarized into a single protein level intensity in each channel and TMT mixture. For each protein, additional local normalization on the summaries was performed to reduce the systematic bias between different TMT mixtures. For the local normalization, we created an artifact reference channel by averaging over all the channels except 131C for each protein and TMT mixture. The channel 131C was removed in order to make each mixture have the same number of samples from each condition. The normalized quantification report at the protein level is available in Supplementary file 5. As a final step, the differential abundance analysis between conditions was performed in MSstatsTMT based on a linear mixed-effects model per protein. The inference procedure was adjusted by applying an empirical Bayes shrinkage. To test two-sided null hypothesis of no changes in abundance, the model-based test statistics were compared to the Student t-test distribution with the degrees of freedom appropriate for each protein or each PTM site. The resulting p values were adjusted to control the FDR with the method by Benjamini-Hochberg. The table with the statistical testing results for all the proteins is available as in Supplementary file 6. Quantification and statistical testing for phospho- and KGG(Ub)-sites data were performed by the same procedure as for global proteomics data with some modifications. First, PSMs from non-modified peptides were filtered out from the PSM report and the remaining preprocessing analyses were the same as above. Second, custom PTM site identifiers were created for each PSM by identifying the modified residue index in the reference proteome that was used to search the MS/MS spectra. Finally, all steps for quantification and differential abundance analysis were performed at the PTM site level, rather than the protein level (Supplementary files 7, 8, 9 and 10). The relative abundance of TMT reporter ion abundances in bar graphs throughout the paper stems from MSstatsTMT modeling and sums up to 1.0 for each Plex. Thus, the sum of all signal shown sums to 1.0 or 2.0 depending on whether the feature was quantified in one or both plexes. For the consolidated heatmaps showing proteome level changes immediately adjacent to any identified PTMs, the ComplexHeatmap R package was used.

Overview heatmaps/clustering

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For the overview heatmaps showing PTM and global proteome datasets side by side, clustering was performed as follows. First, protein quantification results from MSstatsTMT for the PTM and global proteome datasets were merged with the phospho-proteome and KGG datasets, respectively. For each of the two combined datasets, the pheatmap R package was used to cluster the protein model results into 16 row wise clusters using the clustering method ‘ward.D’. The columns of the dataset were kept static and not clustered.

Gene set enrichment analysis

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Gene set enrichment analysis was performed using MsigDB (Liberzon et al., 2015; Subramanian et al., 2005). Global proteome data were filtered to include features with an absolute value log2fc values of greater than one as well as adjusted p values of less than 0.05. Subsequently the data were filtered to require that every protein must be found in both multiplexed experiments. UniProt identifiers were transformed to gene symbols and fed into GSEA for an enrichment analysis against MsigDB’s hallmark gene sets. Gene set enrichment results were filtered to 5% FDR.

Statistical analysis

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Pairwise statistical analyses were performed using an unpaired t-test using two-stage step-up method of Benjamini, Krieger and Yekutieli and false discovery rate of 1% to determine if the values in two sets of data differ. Multiple-comparison corrections were made using the Sidak method with family-wise significance and confidence level of 0.05. Analysis of in vivo infection data was done using unpaired two-tailed t-test after outliers were removed using ROUT method (Q = 1%). Analysis of kinetic (time) with Erastin was performed using two-way ANOVA followed by multiple comparison testing. Line graphs and associated data points represent means of data; error bars represent standard deviation from mean. GraphPad Prism eight software was used for data analysis and representation. p-Values: *<0.05, **<0.01, ***<0.001, ****<0.0001.

The software availability statement

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Raw files were converted to mzXML using ReadW (v 4.3.1) available through https://sourceforge.net/projects/sashimi/files/ReAdW%20%28Xcalibur%20converter%29/. Spectra were searched using Mascot (v 2.4.1) licensed from Matrix Sciences. Search results were filtered using the LDA function in the MASS Package in R as described in Huttlin et al. Cell 143, 1147–1189 (2010). Mojave is an in-house tool developed to report TMT reporter ion intensity values and is available upon request. MSstatsTMT (v 1.8.0) is a freely available open-source R/Bioconductor package to detect differentially abundant proteins in TMT experiments. It can be installed through https://www.bioconductor.org/packages/release/bioc/html/MSstatsTMT.html. Gene set enrichment was performed using the GSEA/MSigDB web portal https://www.gsea-msigdb.org/gsea/msigdb/annotate.jsp. Heatmaps were generated using the pheatmap (v1.0.12) (https://cran.r-project.org/web/packages/pheatmap/index.html) or ComplexHeatmap (v 2.4.2) (https://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html) R packages.

Data availability

Mass spectrometry raw files have been uploaded to the UCSD MassIVE repository and are available: (https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?accession=MSV000085565; Password = shigella).

The following data sets were generated
    1. Maculins T
    2. Verschueren E
    3. Hinkle T
    4. Choi M
    5. Chang P
    6. Chalouni C
    7. Rao S
    8. Kwon Y
    9. Lim J
    10. Katakam AK
    11. Kunz RC
    12. Erickson BK
    13. Huang T
    14. Tsai TH
    15. Vitek O
    16. Reichelt M
    17. Senbabaoglu Y
    18. Mckenzie B
    19. Rohde JR
    20. Dikic I
    21. Kirkpatrick DS
    22. Murthy A
    (2021) UCSD MassIVE
    ID MSV000085565. Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response.
    https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?accession=MSV000085565
    1. Maculins T
    2. Verschueren E
    3. Hinkle T
    4. Choi M
    5. Chang P
    6. Chalouni C
    7. Rao S
    8. Kwon Y
    9. Lim J
    10. Katakam AK
    11. Kunz RC
    12. Erickson BK
    13. Huang T
    14. Tsai TH
    15. Vitek O
    16. Reichelt M
    17. Senbabaoglu Y
    18. Mckenzie B
    19. Rohde JR
    20. Dikic I
    21. Kirkpatrick DS
    22. Murthy A
    (2021) Sourceforge.net
    ID ReAdW%20%28Xcalibur%20converter%29/. Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response.
    https://sourceforge.net/projects/sashimi/files/ReAdW%20%28Xcalibur%20converter%29/
    1. Maculins T
    2. Verschueren E
    3. Hinkle T
    4. Choi M
    5. Chang P
    6. Chalouni C
    7. Rao S
    8. Kwon Y
    9. Lim J
    10. Katakam AK
    11. Kunz RC
    12. Erickson BK
    13. Huang T
    14. Tsai TH
    15. Vitek O
    16. Reichelt M
    17. Senbabaoglu Y
    18. Mckenzie B
    19. Rohde JR
    20. Dikic I
    21. Kirkpatrick DS
    22. Murthy A
    (2021) Bioconductor
    Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response.
    https://doi.org/10.18129/B9.bioc.MSstatsTMT

References

  20. Software
    1. Huang T
    2. Choi M
    3. Hao S
    4. Vitek O
    (2020)
    MSstatsTMT: Protein Significance Analysis in Shotgun Mass Spectrometry-Based Proteomic Experiments with Tandem Mass Tag (TMT) Labeling, version Bioconductor version: Release (3.11)
    MSstatsTMT: Protein Significance Analysis in Shotgun Mass Spectrometry-Based Proteomic Experiments with Tandem Mass Tag (TMT) Labeling.
  47. Software
    1. Pau G
    2. Reeder J
    (2021)
    HTSeqGenie: A NGS Analysis Pipeline, version Bioconductor version: Release (3.12)
    HTSeqGenie: A NGS Analysis Pipeline.

Decision letter

  1. Christina L Stallings
    Reviewing Editor; Washington University School of Medicine, United States
  2. Wendy S Garrett
    Senior Editor; Harvard T.H. Chan School of Public Health, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

In this manuscript, the authors examine the role of ATG16L1 in macrophages in controlling Shigella flexneri infection. The authors report an interesting link between regulation of oxidative stress by autophagy genes and Shigella pathogenesis. These findings reveal important information regarding this clinically relevant pathogen as well as provide important resources for the field.

Decision letter after peer review:

Thank you for submitting your article "Proteomics of autophagy deficient macrophages reveals enhanced antimicrobial immunity via the oxidative stress response" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Wendy Garrett as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

In this manuscript, the authors examine the role of ATG16L1 in macrophages in controlling Shigella flexneri infection. The authors report that: (1) macrophages deficient in ATG16L1 demonstrate accelerated killing of Shigella flexneri in vitro, (2) mice harboring myeloid ATG16L1 deficiency control Shigella better in vivo, (3) a panel of changes in global proteome and post-translational modifications occur, pointing to an accumulation of ROS in ATG16L-deficient BMDM, and (4) depletion of or reduced but not oxidized GSH in cells with Erastin accelerates Shigella clearance by WT macrophages.

The authors have also created a website for users to investigate the proteomics data in detail, providing an important resource to the community.

The discovery of a new mechanism by which autophagy proteins affect host defenses would be very exciting. However, although the authors present a compelling model of how Atg16L1 promotes Shigella survival based on their proteomics data; the manuscript currently falls short of proving the model. Although the authors show increased ROS in Atg16L1-deficient macrophages, they do not present evidence to show this is responsible for the modest decrease in CFUs in vitro and whether any of the in vitro phenotypes directly relate to the in vivo decrease in liver burden in the absence of ATG16L1 in myeloid cells.

Thus, there are still many critical questions that remain unanswered. These critical questions are as follows. Does ATG16L suppress Shigella clearance in macrophages through regulating ROS in vivo? Does this role of ATG16L involve the process of autophagy? In addition, the authors make a point of emphasizing the strengths of their proteomic approach in identifying downstream mechanisms; however, this aspect of the manuscript requires further verification and detailed analysis.

Essential revisions:

Revisions required to connect ROS production in macrophages to control of Shigella replication in vivo:

1. The authors report that Atg16L1-cKO mice display reduced shigella liver burden and conclude that macrophages deficient in ATG16L1 demonstrate an accelerated killing of Shigella in vivo. However, LysM-Cre mediates deletion in a wide range of myeloid cells systemically in the mouse. Is the effect the authors reported specific to the liver? Is this mediated by Atg16L1-deficient Kupffer cells in the liver?

2. There are numerous ways autophagy proteins affect inflammatory responses (as also observed in the authors' proteomic analysis) and, therefore, the in vivo phenotype may be due to inflammatory differences and not due to a direct role for Atg16L1 in a cell-intrinsic antimicrobial pathway in macrophages. The authors present evidence that several cytokine pathways are significantly affected by loss of Atg16l1, including interferons and TNF. Since autophagy affects cytokine production and release, is it possible that increased ROS and pathogen clearance is a consequence of increased cytokine signaling in the basal state and after infection? This could also explain the observed changes in protein abundance or modifications.

3. For the Erastin data, this was performed in WT BMDMs, not ATG16L1 deficient BMDMs, and therefore, may have nothing to do with what is going on in ATG16L1 cKOs. The authors will need to show directly that increased ROS in ATG16L1 cKOs is leading to decreased bacterial replication. In addition, the authors do not demonstrate that the Erastin treatment resulted in elevated ROS. Notably, the difference in CFUs following Erastin treatment was much larger in magnitude than that reported for loss of ATG16L1. Another helpful approach here may be the addition of a ROS scavenger (e.g. NAC) in experimental conditions to functionally test the role of ROS in the observed phenotypes.

4. There appears to be significant variability in the GSSG/GSH ratio in sFigure 7a. Is there a more reproducible or comprehensive way to measure the redox state? Perhaps by additionally examining protein carbonyl adduct formation or other forms of damage (lipids, DNA)?

5. As a result of the absence of linkage between the authors' observations, there are many times that data are over-interpreted. For example, in the last line of abstract: "These findings demonstrate that control of oxidative stress by ATG16L1 regulates antimicrobial immunity against intracellular pathogens". The authors never show that control of oxidative stress by ATG16L1 regulates antimicrobial immunity. Another example is in Lines 132-135- "Our findings demonstrate that ATG16L1 tunes antimicrobial immunity against cytosolic pathogens via the oxidative stress response…". This is not supported by the data presented. The authors also start a number of sentences with the "Interestingly" or "Unexpectedly" even when the data could be expected. For example, Line 310, loss of autophagy has already been linked to increased ROS in other systems. Please address these issues with text revision.

Revisions required to evaluate and verify the proteomics data:

1. To examine potential mechanisms, the authors performed global proteomic analysis in BMDMs treated ex vivo with Shigella. This led to the identification of numerous modifications in phosphorylation, ubiquitylation and protein abundance. The authors focused on autophagy cargo adaptors, and described numerous modifications to TAX1BP1, OPTN, p62, etc. The proteomic analysis was performed by tandem mass tagging, which allowed comparisons across 2 11-plex experiments. The authors provide a detailed overview of modifications of proteins in the autophagy pathway, including substrate-specific receptors. However, there are a few limitations of the study: (1) There isn't much in the way of comparisons with phosphorylation and ubiquitylation patterns seen previously when these proteins – e.g. TAX1BP1 or OPTN – are phosphorylated during selective forms of autophagy. This has been examined in multiple papers previously (e.g. PMID: 26365381; PMID: 27035970; PMID: 25972374 ).

2. Although all the modification data is nice and provides an overview, the authors don't really use it for analysis of mechanisms. Are any of the phosphorylation or ubiquitylation events central to the response to Shigella or loss of ATG16L1? As presented, the modification side of the story doesn't go anywhere mechanistically.

3. The stoichiometry of the modifications are not addressed. While some modifications appear to have pretty large fold changes, many are only ~2-fold, so it may be that this is a tiny fraction of the protein that is modified. Therefore, it is hard to know what is significant biologically without further follow-up. For example, is the modification of the cargo adaptors linked with removal of Shigella directly or is it some other role (for example turnover of aggregated proteins as a result of increased ROS). For example, TAX1BP1 T494 phosphorylation is already maximal in the ATG16L1 KO without Shigella (Figure 3). Similarly, the relationship between phosphorylation and ubiquitylation on the same polypeptide (i.e. combinatorial modification) isn't addressed.

4. The e3 ubiquitin ligase and xenobiotic metabolism pathways are increased in ATG16L1 KO macrophages. This is suggestive of increased aryl hydrocarbon receptor activity. Since AhR is a driver of ROS in the cell, as part of the xenobiotic detoxifying response, it would be interesting to see if AhR was involved in the observed phenotype.

https://doi.org/10.7554/eLife.62320.sa1

Author response

Essential revisions:

Revisions required to connect ROS production in macrophages to control of Shigella replication in vivo:

1. The authors report that Atg16L1-cKO mice display reduced shigella liver burden and conclude that macrophages deficient in ATG16L1 demonstrate an accelerated killing of Shigella in vivo. However, LysM-Cre mediates deletion in a wide range of myeloid cells systemically in the mouse. Is the effect the authors reported specific to the liver? Is this mediated by Atg16L1-deficient Kupffer cells in the liver?

Is the effect the authors reported specific to the liver?

S.flexneri is a human enteric pathogen with poor tropism for murine intestinal tissue. As such, it is unable to colonize the intestinal mucosa in mice unless genetically modified strains are utilized that compromise inflammasome function in the intestinal epithelium (recently described by Mitchell et al., 2020). Thus, infection via the tail-vein is commonly used as an acute model to approximate bacterial colonization in vivo. We have added this clarification in the text to better outline the limitations of current in vivo models (Page 7; Lines 176-179 and Page 17; Lines 495-500).

In response to this query, we provide new data showing comparison of additional tissues beyond the liver. Specifically, we observe that infection of the spleen is comparably low, without a significant difference between ATG16L1-WT (control) and ATG16L1-cKO animals. This data is presented in new panel Figure S1B. Additionally, we evaluated lung tissue and found that this tissue does not show detectable infection (data not shown) and added a comment about this in the text (Page 7, 8; Lines 181-185).

Is this mediated by Atg16L1-deficient Kupffer cells in the liver?

We agree that the in vivo model (LysM-Cre) utilized in the manuscript does not formally demonstrate Kupffer cells as the sole driver of the phenotype, however, the model permits studies with targeted gene deletion in the monocyte and Kupffer cell populations. We have added clarifications in the manuscript text to acknowledge this limitation (Page 8; Lines 185-193. Pages 17, 18; Lines 503-508).

We agree that the LysM-Cre model also edits granulocyte populations which may impact the phenotype at later time points following liver injury. We have adjusted our interpretation of the data to reflect this limitation. Additionally, to better focus our findings on macrophage populations, we repeated the infection studies and quantified bacterial colonization at an earlier infection time point (6 hours post infection). This enabled us to observe that mice lacking Atg16l1 in the myeloid compartment (LysM-Cre) continue to exhibit significantly decreased S.flexneri CFU in the liver. This data is presented in new panel Figure 1D.

We improved the manuscript text accordingly and hope this addresses the Reviewer concern.

2. There are numerous ways autophagy proteins affect inflammatory responses (as also observed in the authors' proteomic analysis) and, therefore, the in vivo phenotype may be due to inflammatory differences and not due to a direct role for Atg16L1 in a cell-intrinsic antimicrobial pathway in macrophages. The authors present evidence that several cytokine pathways are significantly affected by loss of Atg16l1, including interferons and TNF. Since autophagy affects cytokine production and release, is it possible that increased ROS and pathogen clearance is a consequence of increased cytokine signaling in the basal state and after infection? This could also explain the observed changes in protein abundance or modifications.

..is it possible that increased ROS and pathogen clearance is a consequence of increased cytokine signaling in the basal state and after infection?

Thank you for this important question. Indeed, our quantitative mass spectrometry data (new Figure 5—figure supplement 1) and RNA-Seq data (new panels Figure 5—figure supplement 2A and B) demonstrate a significant enrichment of pro-inflammatory signaling pathways either at baseline or following infection of cKO BMDMs. We addressed the role of TNFa and Type I Interferon signaling in pathogen clearance using BMDM infection model. Indeed, blocking TNFa or IFNAR1 resulted in a modest but significant rescue of bacterial proliferation in cKO, but not WT, BMDMs (new data in Figure 5—figure supplement 2C-H). This is in contrast to modulation of ROS levels that affect bacterial proliferation in both WT and cKO BMDMs (new Figure 6). Therefore, it is likely that contributions from pro-inflammatory signaling and ROS together drive bacterial clearance in the BMDM infection model, with a more pronounced phenotype in cells lacking ATG16L1.

We then decided to follow this observation in vivo and performed a study aiming to assess the role of overactive interferon signaling inherent to ATG16L1 KO animals in clearing S.flexneri. We focused on blocking type I IFN signaling given the more significant impact of a-IFNAR1 treatment on cKO BMDMs (new panel Figure 5—figure supplement 2G). Briefly, mice were pre-treated via intraperitoneal injections (IP) with isotype control or anti-IFNAR1 antibodies for 24h. We used anti-IFNAR1 antibody clone MAR1-5A3 2.5 mg per mouse (Leinco Technologies). Mice were injected with antibodies again prior infection (0.5 mg per mouse, IP) and infected with S.flexneri as before (IV). Subsequently, 24h after infection the livers were collected for CFU analysis.

The result shown in Author response image 1 demonstrates a significant CFU reduction in livers from ATG16L1-cKO animals in the isotype control group, which is consistent with our observations presented in panels Figure 1D and 1E. We also observed a trend towards higher CFU counts in ATG16L1-cKO animals treated with the anti-IFNAR1 antibody. However, this experiment failed to demonstrate a statistically significant increase in CFUs within each genotype. S.flexneri CFUs did not increase significantly between isotype-treated and anti-IFNAR1-treated WT or cKO animals. In addition to the inherent variability of this challenging model, other factors could contribute to the lack of a clear rescue of bacterial proliferation. These include an incomplete pharmacological blockade of IFNAR1 signaling and redundant pathways which control bacterial proliferation. Additional genetic models (e.g. combined deletion of Atg16l1 and Ifnar1) or combination therapies are currently out-of-scope for the study but supported by our findings.

Taken together, experiments to address this query led us to acknowledge a contribution of pro-inflammatory signaling in clearing S.flexneri in our in vitro BMDM infection model and introduce a new Figure S8 to reflect this aspect. We thank the Reviewer for this important question.

Author response image 1
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in vivo blockade of Type I Interferon signaling during S.flexneri infection.

3. For the Erastin data, this was performed in WT BMDMs, not ATG16L1 deficient BMDMs, and therefore, may have nothing to do with what is going on in ATG16L1 cKOs.

The authors will need to show directly that increased ROS in ATG16L1 cKOs is leading to decreased bacterial replication.

We addressed this experimentally and by determining whether Erastin treatment of ATG16L1-deficient BMDMs further accelerated S.flexneri clearance. Indeed, this was the case, which is aligned with the main message of our manuscript. We thank Reviewer for this suggestion. This data is presented in new panels Figure 6B and 6C.

In addition, the authors do not demonstrate that the Erastin treatment resulted in elevated ROS.

In the initial submission, we provided data showing that Erastin treatment for 24 hours resulted in elevated oxidative stress, prior to evaluating its effects on bacterial clearance (former panel Supplemental Figure 7G). While WT BMDMs exhibited a clear increase in oxidative stress upon Erastin treatment, ATG16L1-deficient BMDMs with basally elevated oxidative stress did not show a further increase at this time point. We also observed that prolonged Erastin treatment resulted in BMDM cytotoxicity, with ATG16L1-deficient BMDMs exhibiting enhanced sensitivity to the inhibitor at a later time point (new Figure 6—figure supplement 1A). This is consistent with our findings that ATG16L1-deficient BMDMs are dependent on XCT/GSH mediated antioxidant pathways. It is technically challenging to measure oxidative stress at a later time-point since a large population of ATG16L1-deficient BMDMs are undergoing cell death. However, following Reviewer recommendations, we measured oxidative stress upon prolonged Erastin treatment over 48h. We used high content microscopy enabling imaging of larger cell populations. Consistent with our previous observations, we detected a highly significant increase in CellRox probe mean intensity between untreated WT and cKO BMDMs (this data is shown here as Author response image 2 and presented in a new panel Figure 5—figure supplement 3A). Consistent with the established role of Erastin in increasing oxidative stress, we also observed a significant increase in CellRox probe mean intensity following 48h treatment with Erastin. We hope that the data presented here addresses the Reviewer question. Taken together, we decided to remove the initial data presented in the first submission in relation to this question (former panel Supplemental Figure 7G).

Author response image 2
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Quantification of CellRox green mean intensity in WT and cKO BMDMs.

Graph shows single cell data from high content imaging (n = 1). Unpaired t test **** P < 0.0001.

Notably, the difference in CFUs following Erastin treatment was much larger in magnitude than that reported for loss of ATG16L1

Thank you for this question. During optimization experiments we observed that the larger difference in CFUs is a reflection of drug exposure. A dose-titration of Erastin revealed a dose-dependent decrease in bacterial CFUs. In Author response image 3 we provide a dose-titration of Erastin and its impact on S.flexneri CFUs in response to Reviewer question. We hope that this answers Reviewer question.

Author response image 3
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Dose-titration of Erastin and its impact on S.flexneri killing by BMDMs.

Another helpful approach here may be the addition of a ROS scavenger (e.g. NAC) in experimental conditions to functionally test the role of ROS in the observed phenotypes.

We thank the Reviewer for this important suggestion. We assessed if a ROS scavenger (butylated hydroxyanisole or BHA) is capable to rescue S.flexneri killing in our experimental in vitro infection system using both WT and ATG16L1-deficient macrophages. We found that BHA was able to rescue bacterial killing by both WT and cKO BMDMs. This data is now a part of new Figure 6.

4. There appears to be significant variability in the GSSG/GSH ratio in sFigure 7a. Is there a more reproducible or comprehensive way to measure the redox state? Perhaps by additionally examining protein carbonyl adduct formation or other forms of damage (lipids, DNA)?

We believe there are several reasons for variable results presented in the Supplemental Figure 9B-D of the resubmitted revised manuscript. We noted that only a subpopulation of ATG16L1-deficient BMDMs demonstrates increased cellular ROS levels in our single-cell imaging data presented in Figure 5E. We have followed up on this observation using high content single-cell imaging for cellular ROS and obtained very similar data for a large population of cells (>1000 cells per genotype). We believe this stochasticity is an important point to convey and provide the high content imaging data to the supplement of the revised manuscript (new panel Figure 5—figure supplement 3A).

The GSSG/GSH ELISA-type experiments presented in Figure 5—figure supplement 3B-D, to which the Reviewer refers, are performed using cell lysate from a population of cells, which likely accounts for some of this variability since it fails to achieve single-cell resolution as in the above assays. An additional factor contributing to this variability is the exclusive usage of primary macrophages in this study, which lends itself to variability between independent experiments. Nonetheless, we believe the data presented in Figure 5—figure supplement 3B-D show a consistent trend across independent biological repeats of the assay.

Additionally, we now provide further evidence to support our observations. In our initial submission, we noted that ATG16L1 deficient BMDMs exhibit elevated levels of the NAD(P)H:quinone acceptor oxidoreductase NQO1 (Figure 5B). Elevated NQO1 levels and activity are indicative of a heightened redox state (Reviewed by Ross et al., 2017; Siegel et al., 2018; Raina et al., 1999). To address the Reviewer’s query, we have synthesized a recently described NQO1-activated fluorescent probe (Punganuru et al., 2019), which enabled us to assess the redox state of WT and ATG16L1-deficient BMDMs using flow cytometry. These experiments represent a highly sensitive assay to measure redox state at the single-cell level in primary cells and demonstrate and increased MFI of the probe in cKO BMDMs. This data constitute new panels Figure 5—figure supplement 3E and F.

We have also attempted quantification of s-GSH adducts and 4-HNE damaged lipids by competitive ELISA kits, however were not able to achieve reliable assay performance using assay standards, which is an essential step prior evaluating assay results (data not shown).

We hope the Reviewer will find our answer and the provided data satisfactory.

5. As a result of the absence of linkage between the authors' observations, there are many times that data are over-interpreted. For example, in the last line of abstract: "These findings demonstrate that control of oxidative stress by ATG16L1 regulates antimicrobial immunity against intracellular pathogens". The authors never show that control of oxidative stress by ATG16L1 regulates antimicrobial immunity. Another example is in Lines 132-135- "Our findings demonstrate that ATG16L1 tunes antimicrobial immunity against cytosolic pathogens via the oxidative stress response…". This is not supported by the data presented. The authors also start a number of sentences with the "Interestingly" or "Unexpectedly" even when the data could be expected. For example, Line 310, loss of autophagy has already been linked to increased ROS in other systems. Please address these issues with text revision.

We hope that new data generated with the aim of addressing previously discussed queries (Questions 1-4) also address the concerns above. Specifically:

1. We show elevated oxidative stress and downstream responses in ATG16L1 cKO BMDMs via new assays additional high content imaging of cellular ROS – new Figure 5—figure supplement 3A; elevated NQO1 activity via a new fluorescent probe – new Figure 5—figure supplement 3E, F; additional RNA-Seq analysis – new Figure 5—figure supplement 2A for GSEA ; (2) We now show that anti-oxidant treatment (BHA) reverses the phenotype in ATG16L1 cKO BMDMs more dramatically than TNF or IFN inhibition (BHA inhibition – new Figure 6D-F, Figure 6—figure supplement 1E, F; TNF inhibition – new Figure 5—figure supplement 2C-E; IFNAR1 inhibition – new Figure 5—figure supplement 2F-H); (3) we show that XCT inhibition phenocopies enhanced anti-microbial immunity via the GSH pathway, and that ATG16L1 deficient macrophages exhibit increased dependency on XCT for viability (new Figure 6A-C, Figure 6—figure supplement 1A, C, D). We thank the Reviewers for guiding us towards these critical functional studies and believe that assertions made in the text are sufficiently supported by these new data.

We have also edited the text of the revised manuscript to avoid usage of qualifying adjectives and instead directly state the observations. We thank the Reviewer for highlighting this issue.

Revisions required to evaluate and verify the proteomics data:

1. To examine potential mechanisms, the authors performed global proteomic analysis in BMDMs treated ex vivo with Shigella. This led to the identification of numerous modifications in phosphorylation, ubiquitylation and protein abundance. The authors focused on autophagy cargo adaptors, and described numerous modifications to TAX1BP1, OPTN, p62, etc. The proteomic analysis was performed by tandem mass tagging, which allowed comparisons across 2 11-plex experiments. The authors provide a detailed overview of modifications of proteins in the autophagy pathway, including substrate-specific receptors. However, there are a few limitations of the study: (1) There isn't much in the way of comparisons with phosphorylation and ubiquitylation patterns seen previously when these proteins – e.g. TAX1BP1 or OPTN – are phosphorylated during selective forms of autophagy. This has been examined in multiple papers previously (e.g. PMID: 26365381; PMID: 27035970; PMID: 25972374 ).

We are unsure of the specific question being posed here, but have revised the text to include additional commentary about signaling via autophagy pathway components, including the cargo receptors based on in the papers referenced above. The phosphorylation sites reported in these three papers (OPTN_HUMAN_S473, OPTN_HUMAN_S513, and SQSTM_HUMAN_S403) have conserved sites in MOUSE (OPTN_MOUSE_S476, OPTN_MOUSE_S517, SQSTM_MOUSE_S405), but were not identified or quantified in this work.

The heatmaps in Figure 3 show PTMs from multiple autophagy-related proteins that were identified and quantified and have been described in the text. Additionally, the interactive dashboards are provided to facilitate comparisons like the ones suggested in the question. However, the totality of the insights generated by multiplexed TMT-MS revealed altered oxidative stress response as a fundamentally important aspect of anti-microbial immunity.

2. Although all the modification data is nice and provides an overview, the authors don't really use it for analysis of mechanisms. Are any of the phosphorylation or ubiquitylation events central to the response to Shigella or loss of ATG16L1? As presented, the modification side of the story doesn't go anywhere mechanistically.

We thank the Reviewer for this question. We agree that our initial manuscript did not sufficiently highlight how the PTM data contributed to our conclusions and have tried to remedy that in this revision. Our initial observation that basal mitochondrial function was largely intact in ATG16L1-deficient cells (new Figure 5—figure supplement 3G-H) was surprising and prompted us to query additional organelle proteomes in the TMT-MS modification dataset. We focused on mitochondria and peroxisomes, given the established role of these organelles with modulation of cellular ROS. While we did not detect significant or broad ranging accumulation of mitochondrial or peroxisomal proteins, a specific enrichment of peroxisomal matrix proteins in the ubiquitinated fraction was observed in ATG16L1-deficient BMDMs, as shown in the GSEA heatmap presented in the new Figure 5—figure supplement 1C. An additional hit in the PTM dataset that points us to the intersection of oxidative stress and the peroxisome is elevated ubiquitination of Fis1 at lysine-20 (FIS1_K20). Fis1 is known to play a key role in peroxisomal homeostasis. From the phosphoproteomics data, ubiquitin phosphorylation at serine-57 also stands out given its recent connection to oxidative stress signaling in yeast (Hepowit et al., 2020).

These findings leverage the modification (PTM) data generated by TMT-MS and reveal that elevated basal oxidative stress upon Atg16l1 loss appears more closely connected to peroxisomal rather than mitochondrial dysfunction. This commentary has been included in the revised manuscript (Page 15; Lines 428-431). Additional follow up work will be required to elucidate the full mechanistic picture connecting deficient autophagy to the peroxisomal and oxidative stress pathways, but remain outside the scope of our current submission

3. The stoichiometry of the modifications are not addressed. While some modifications appear to have pretty large fold changes, many are only ~2-fold, so it may be that this is a tiny fraction of the protein that is modified. Therefore, it is hard to know what is significant biologically without further follow-up. For example, is the modification of the cargo adaptors linked with removal of Shigella directly or is it some other role (for example turnover of aggregated proteins as a result of increased ROS). For example, TAX1BP1 T494 phosphorylation is already maximal in the ATG16L1 KO without Shigella (Figure 3).

In the context of MS proteomics technologies, TMT and the majority of other approaches report relative rather than absolute quantitative values. Moreover, the majority of physiologically meaningful PTM level changes occur at sub-stoichiometric levels, owing to subcellular compartmentalization and cellular heterogeneity in the responses. Single cell profiling technologies consistently show that a physiologically relevant experimental perturbation like infection causes a diverse range of responses across a population of cells, especially when considering that individual cells are infected at different rates both in vivo and ex vivo. In contrast, non-physiological biochemical treatments such as high level LPS or CpG stimulation show more consistent, population-wide changes but do not faithfully recapitulate the infection cycle of a pathogen.

Similarly, the relationship between phosphorylation and ubiquitylation on the same polypeptide (i.e. combinatorial modification) isn't addressed.

Given the transient nature of most ubiquitination events, the low stoichiometries of both ubiquitination and phosphorylation sites within the population, and the distribution of these post-translational modifications across the proteome, the frequency doubly modified peptides bearing these two heterogenous marks is exceedingly rare in this and other proteomics data. This can be seen within the existing data when looking at the relative (in)frequency of multiply phosphorylated or multiply ubiquitinated peptides, compared to singly modified peptides.

It is important to call out an analytical caveat that additionally hampers this investigation. Permuting an all by all matrix of two post-translational modifications at proteome scale results in a combinatorial explosion that poses challenges for the software tools tasked with identifying and quality filtering these rare peptide spectral matches.

4. The e3 ubiquitin ligase and xenobiotic metabolism pathways are increased in ATG16L1 KO macrophages. This is suggestive of increased aryl hydrocarbon receptor activity. Since AhR is a driver of ROS in the cell, as part of the xenobiotic detoxifying response, it would be interesting to see if AhR was involved in the observed phenotype.

During the revision we took a deeper look by performing GSEA with larger curated MSigDb gene set collections to see if there was an indication of AhR pathway that might further explain the quantitative changes in S.flexneri infected and ATG16L1-deficient macrophages. Unfortunately, no evidence was found that points to a connection of this S. flexneri infection of WT or cKO BMDMs directly to the AhR signaling pathway.

https://doi.org/10.7554/eLife.62320.sa2

Article and author information

Author details

  1. Timurs Maculins

    1. Department of Cancer Immunology, Genentech, South San Francisco, United States
    2. Institute of Biochemistry II, Goethe University, Frankfurt am Main, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9333-301X

  2. Erik Verschueren

    Department of Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, United States
    Present address
    Galapagos NV, Mechelen, Belgium
    Contribution
    Data curation, Software, Formal analysis, Visualization, Methodology, Writing - review and editing, TMT data acquisition, analysis and representation
    Competing interests
    is a current employee at Galapagos.

  3. Trent Hinkle

    Department of Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, United States
    Contribution
    Data curation, Formal analysis, Visualization, Methodology, Writing - review and editing
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  4. Meena Choi

    1. Department of Microchemistry, Proteomics and Lipidomics, Genentech, South San Francisco, United States
    2. Khoury College of Computer Sciences, Northeastern University, Boston, United States
    Contribution
    Data curation, Software, Formal analysis, Visualization, Methodology, Writing - review and editing, TMT data acquisition, analysis and representation
    Competing interests
    was employed at Northeastern Univeristy during the preparation of the manuscript and is currently an employee of Genentech Inc and a shareholder in Roche.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6025-5035

  5. Patrick Chang

    Department of Pathology, Genentech, South San Francisco, United States
    Contribution
    Investigation, Methodology, Immunofluorescence microscopy data acquisition and quantification
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  6. Cecile Chalouni

    Department of Pathology, Genentech, South San Francisco, United States
    Contribution
    Investigation, Methodology, Immunofluorescence microscopy data acquisition and quantification
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  7. Shilpa Rao

    Department of Oncology Bioinformatics, Genentech, South San Francisco, United States
    Contribution
    Formal analysis, Visualization, Added during revision: Performed analysis and visualization of new RNA-Seq data presented in the revision (Figure 5-figure supplement 2A, B)
    Competing interests
    was employed at Genentech, Inc during the preparation of the manuscript.

  8. Youngsu Kwon

    Department of Translational Immunology, Genentech, South San Francisco, United States
    Contribution
    Investigation, Methodology, Added during revision: Performed additional in vivo studies presented in the revision (Figure 1D, E; Figure 1-figure supplement 1B; Figure R1)
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  9. Junghyun Lim

    Department of Cancer Immunology, Genentech, South San Francisco, United States
    Contribution
    Investigation, Methodology, Assisted in design and execution of large-scale proteomic experiments
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  10. Anand Kumar Katakam

    Department of Pathology, Genentech, South San Francisco, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Electron microscopy data acquisition, analysis and representation
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  11. Ryan C Kunz

    IQ Proteomics LLC, Cambridge, United States
    Contribution
    Formal analysis, Methodology, TMT data acquisition and initial data analysis
    Competing interests
    is an employee of IQ Proteomics LLC.

  12. Brian K Erickson

    IQ Proteomics LLC, Cambridge, United States
    Contribution
    Formal analysis, Methodology, TMT data acquisition and initial data analysis
    Competing interests
    is an employee of IQ Proteomics LLC.

  13. Ting Huang

    Khoury College of Computer Sciences, Northeastern University, Boston, United States
    Contribution
    Software, Formal analysis, TMT data acquisition, analysis and representation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1041-2167

  14. Tsung-Heng Tsai

    1. Khoury College of Computer Sciences, Northeastern University, Boston, United States
    2. Department of Mathematical Sciences, Kent State University, Kent, United States
    Contribution
    Software, Formal analysis, TMT data acquisition and initial data analysis
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5211-2985

  15. Olga Vitek

    Khoury College of Computer Sciences, Northeastern University, Boston, United States
    Contribution
    Software, Formal analysis, TMT data acquisition and initial data analysis
    Competing interests
    No competing interests declared

  16. Mike Reichelt

    Department of Pathology, Genentech, South San Francisco, United States
    Contribution
    Supervision, Investigation, Methodology
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  17. Yasin Senbabaoglu

    Department of Oncology Bioinformatics, Genentech, South San Francisco, United States
    Contribution
    Formal analysis, Supervision, Methodology, Added during revision: Designed RNA-Seq study, supervised analysis and visualization of new RNA-Seq data presented in the revision (Figure 5-figure supplement 2A, B)
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  18. Brent Mckenzie

    Department of Translational Immunology, Genentech, South San Francisco, United States
    Contribution
    Resources, Supervision, Project administration, Added during revision: Guided and supervised in vivo studies presented in the revision (Figure 1D, E; Figure 1-figure supplement 1B; Figure R1)
    Competing interests
    is an employee of Genentech, Inc and a shareholder in Roche.

  19. John R Rohde

    Department of Microbiology and Immunology, Dalhousie University, Halifax, Canada
    Contribution
    Resources, Investigation, Writing - review and editing, Generation of S.flexneri strains. Guidance for in vitro BMDM infection experiments
    Competing interests
    No competing interests declared

  20. Ivan Dikic

    1. Institute of Biochemistry II, Goethe University, Frankfurt am Main, Germany
    2. Department of Infectious Diseases, Genentech, South San Francisco, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    dikic@biochem2.uni-frankfurt.de
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8156-9511

  21. Donald S Kirkpatrick

    Interline Therapeutics, South San Francisco, United States
    Contribution
    Conceptualization, Resources, Supervision, Visualization, Writing - original draft, Writing - review and editing, Designed the conceptual framework of the study and experiments. TMT data acquisition, analysis and representation
    For correspondence
    dkirkpatrick@interlinetx.com
    Competing interests
    is an employee of Interline Therapeutics.

  22. Aditya Murthy

    Interline Therapeutics, South San Francisco, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    amurthy@interlinetx.com
    Competing interests
    is an employee of Interline Therapeutics.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6130-9568

Funding

AXA Research Fund (16-AXA-PDOC-078)

  • Timurs Maculins

Genentech

  • Timurs Maculins

H2020 European Research Council (742720)

  • Ivan Dikic

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded in part by a fellowship awarded to TM by the AXA Research fund (16-AXA-PDOC-078) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program to ID (grant agreement No. 742720). We thank the Genentech Visiting Scientist Program and Research Innovation Fund for supporting this work. We also thank Avinashnarayan Venkatanarayan, Beatrice Breart and the laboratory of Eric Brown at Genentech for technical assistance.

Ethics

Animal experimentation: All animal experiments were performed under protocols approved by the Genentech Institutional Animal Care and Use Committee. Generation of myeloid-specific deletion of Atg16L1 was achieved by crossing LysM-Cre+ mice with Atg16L1loxp/loxp mice and was described previously (Murthy et al., 2014). All mice were bred onto the C57BL/6N background. All in vivo experiments were performed using age-matched colony controls. Protocol ID 17-2842.

Senior Editor

  1. Wendy S Garrett, Harvard T.H. Chan School of Public Health, United States

Reviewing Editor

  1. Christina L Stallings, Washington University School of Medicine, United States

Publication history

  1. Received: August 21, 2020
  2. Accepted: May 12, 2021
  3. Version of Record published: June 4, 2021 (version 1)

Copyright

© 2021, Maculins et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Timurs Maculins
  2. Erik Verschueren
  3. Trent Hinkle
  4. Meena Choi
  5. Patrick Chang
  6. Cecile Chalouni
  7. Shilpa Rao
  8. Youngsu Kwon
  9. Junghyun Lim
  10. Anand Kumar Katakam
  11. Ryan C Kunz
  12. Brian K Erickson
  13. Ting Huang
  14. Tsung-Heng Tsai
  15. Olga Vitek
  16. Mike Reichelt
  17. Yasin Senbabaoglu
  18. Brent Mckenzie
  19. John R Rohde
  20. Ivan Dikic
  21. Donald S Kirkpatrick
  22. Aditya Murthy
(2021)
Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response
eLife 10:e62320.
https://doi.org/10.7554/eLife.62320

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    Yuanyuan Li, Wenyan Xu ... Zhaoxia Sun
    Research Article

    Nephronophthisis (NPHP) is a ciliopathy characterized by renal fibrosis and cyst formation, and accounts for a significant portion of end stage renal disease in children and young adults. Currently no targeted therapy is available for this disease. INVS/NPHP2 is one of the 25 NPHP genes identified to date. In mouse, global knockout of Invs leads to renal fibrosis and cysts. However, the precise contribution of different cell types and the relationship between epithelial cysts and interstitial fibrosis remains undefined. Here, we generated and characterized cell-type specific knockout mouse models of Invs, investigated the impact of removing cilia genetically on phenotype severity in Invs mutants and evaluated the impact of the histone deacetylase inhibitor valproic acid (VPA) on Invs mutants. Epithelial specific knockout of Invs in Invsflox/flox;Cdh16-Cre mutant mice resulted in renal cyst formation and severe stromal fibrosis, while Invsflox/flox;Foxd1-Cre mice, where Invs is deleted in stromal cells, displayed no observable phenotypes up to the young adult stage, highlighting a significant role of epithelial-stromal crosstalk. Further, increased cell proliferation and myofibroblast activation occurred early during disease progression and preceded detectable cyst formation in the Invsflox/flox;Cdh16-Cre kidney. Moreover, concomitant removal of cilia partially suppressed the phenotypes of the Invsflox/flox;Cdh16-Cre mutant kidney, supporting a significant interaction of cilia and Invs function in vivo. Finally, VPA reduced cyst burden, decreased cell proliferation and ameliorated kidney function decline in Invs mutant mice. Our results reveal the critical role of renal epithelial cilia in NPHP and suggest the possibility of repurposing VPA for NPHP treatment.