1. Computational and Systems Biology

Knockdown of a regulatory barcode shifts macrophage polarization destination from M1 to M2 and increases pathogen burden upon S. aureus infection

  1. Sathyabaarathi Ravichandran
  2. Bharath Bhatt
  3. Awantika Shah
  4. Debajyoti Das
  5. Kithiganahalli Narayanaswamy Balaji
  6. Nagasuma Chandra  Is a corresponding author
  1. IISc Mathematics Initiative, Indian Institute of Science, India
  2. Department of Microbiology and Cell Biology, Indian Institute of Science, India
  3. Department of Biochemistry, Indian Institute of Science, India
  4. Centre for Biosystems Science and Engineering, Indian Institute of Science, India
https://doi.org/10.7554/eLife.91547.3
Share this article
Cite this article
  1. Sathyabaarathi Ravichandran
  2. Bharath Bhatt
  3. Awantika Shah
  4. Debajyoti Das
  5. Kithiganahalli Narayanaswamy Balaji
  6. Nagasuma Chandra
(2026)
Knockdown of a regulatory barcode shifts macrophage polarization destination from M1 to M2 and increases pathogen burden upon S. aureus infection
eLife 12:RP91547.
https://doi.org/10.7554/eLife.91547.3
  2. Download BibTeX
  3. Download .RIS

eLife Assessment

The authors of this manuscript study the transcriptional regulators that allow macrophages to assume different functional phenotypes in response to immune stimuli. They generate a computational map of the gene regulatory networks involved in determining macrophage phenotypes and experimentally validate the role of putative regulatory factors in a myeloid cell line. This study represents a valuable approach to understanding how gene regulation impacts macrophage polarization and their conclusions are supported by solid computational and experimental evidence. The revision has clarified that the focus is the identification of the regulatory barcodes in a myeloid cell line. Future studies in primary cells and in vivo will be required to assess the roles of these regulators in a broader context.

https://doi.org/10.7554/eLife.91547.3.sa0

Abstract

Macrophages are driven to form distinct functional phenotypes in response to different immunological stimuli, in a process widely referred to as macrophage polarization. Transcriptional regulators that guide macrophage polarization in response to a given trigger remain largely unknown. In this study, we interrogate the programmable landscape in macrophages to find regulatory panels that determine the precise polarization state that a macrophage is driven to. Toward this, we configure an integrative network analysis pipeline that utilizes macrophage transcriptomes in response to 28 distinct stimuli, reconstructs contextualized human gene regulatory networks, and identifies epicenters of perturbations in each case. We find that these contextualized regulatory networks form a spectrum of 13 distinct clusters, with M1 and M2 at the two ends. Using our computational pipeline, we identify combinatorial panels of epicentric regulatory factors (RFs) for each polarization state. We demonstrate that a set of three RFs, that is, CEBPB, NFE2L2, and BCL3, is sufficient to change the polarization destination from M1 to M2. siRNA knockdown of the 3-RF-set in THP1-derived M0 cells, despite exposure to an M1 stimulant, significantly attenuated the shift to M1 phenotype and instead increased the expression of M2 markers. Single knockdown of each RF also showed a similar trend. The siRNA-mediated knockdown of the 3-RF-set rendered the macrophages hyper-susceptible to Staphylococcus aureus infection, demonstrating the importance of these factors in modulating immune responses. Overall, our results provide insights into the transcriptional mechanisms underlying macrophage polarization and identify key RFs that may be targeted to modulate immune responses.

Introduction

Macrophages, the key components of the innate immune system, are responsible for processes ranging from the maintenance of healthy tissues, phagocytic clearance of microbes, to tissue repair and remodeling (Aderem, 2003; Greenberg and Grinstein, 2002; Mosser et al., 2021; Murray and Wynn, 2011). They do most of these in a bid to restore normal physiology and act as a bridge between innate and adaptive immunity. In response to a range of stimuli, including pathogen insults, macrophages are directed to distinct functional phenotypes in a process widely referred to as macrophage polarization (Benoit et al., 2008; Murray and Wynn, 2011). The most well-described phenotypes are the M1 and M2 polarization states, where M1 macrophages are pro-inflammatory in nature with microbicidal activity, while M2 is associated with an anti-inflammatory response. Stimulation of macrophages with lipopolysaccharides (LPS) or interferon-gamma (IFNγ) typically leads to the M1 state, whereas treatment with interleukins (IL4 and IL13) polarizes macrophages into the M2 state (Mills et al., 2000; Murray, 2017). M2 macrophages are further sub-categorized into M2a, M2b, M2c, and M2d depending on the activation triggers and the resulting transcriptional changes (Mantovani et al., 2004; Rőszer, 2015; Yao et al., 2019). However, this model is said to be oversimplified as it fails to capture the complexity exhibited by different macrophage states in an in vivo scenario where the interaction between different cytokines, interleukins, and other effector molecules define the final polarized state (Atri et al., 2018; Martinez and Gordon, 2014). A recent study on the transcriptomes of human monocyte-derived macrophages treated with a variety of physiological stimulants identified nine different polarization states, which better fit the description of a spectrum of states rather than a classical binary M1/M2 axis (Xue et al., 2014). The different states in the polarization spectrum present complex transcriptional profiles as seen from altered gene expression patterns in several hundred genes. The importance of transcriptional regulation in defining macrophage polarization has been brought out by several studies (Gerrick et al., 2018; Hörhold et al., 2020; Palma et al., 2020; Xue et al., 2014; Zhao and Popel, 2021). In vitro and ex vivo studies have shown complete reversal of macrophage polarization states depending upon the molecules (pathogen-associated molecular patterns, host-derived damage-associated molecular patterns, cytokines, etc.) present in the microenvironment.

Several studies have shown that activation of transcription factors (TFs) such as signal transducers and activators of transcription (STAT1, STAT3, STAT4, and STAT6), nuclear transcription factor-κB (NF-κB), NF-E2-related factors (NRF1 and NRF2), kruppel-like factors (KLF4 and KLF6), CCAAT-enhancer-binding proteins (C/EBP-α and C/EBP-β), and peroxisome proliferator-activated receptors (PPAR-α, PPAR-δ, and PPAR-γ) are associated with macrophage polarization (Lawrence and Natoli, 2011; Li et al., 2018; Tugal et al., 2013). But, the available information is insufficient to predict the polarization state that will result in a given condition. A possible reason is that a set of TFs together defines the state rather than individual TFs by themselves. While the triggers that lead to different polarization states are known, the specific set of TFs that nucleate the change or serve as control points to guide macrophage polarization is not known.

The precise polarization state attained by the macrophages in response to pathogenic triggers has a direct bearing on the outcome of the disease (Benoit et al., 2008; Koziel et al., 2009; Pidwill et al., 2020). In response to bacterial pathogens such as Staphylococcus aureus, macrophages induce the transcriptional activity of pro-inflammatory genes, activating the M1 program. The M1 phenotype is known to have an activated microbicidal machinery that is utilized in clearing such acute infections. However, prolonged or excessive inflammatory response by M1 macrophages can be pathological as it results in tissue damage and destruction. M2 macrophages produce anti-inflammatory mediators and resolve the inflammation (Koziel et al., 2009; Pidwill et al., 2020). Not surprisingly, the skewing of macrophage polarization is observed in several cases of severe bacterial infections, sepsis, tumor progression in cancers, and a range of other inflammatory diseases (Ardura et al., 2019; Hamilton and Tak, 2009; Sica and Mantovani, 2012; Zheng et al., 2018). Although our understanding of macrophage biology is rapidly increasing, the precise molecular controls and their associated processes involved in macrophage switching to different functional states are not well characterized. Identifying combinations of molecules that guide macrophage polarization will greatly facilitate a deeper understanding of how infections are contained, provide a basis to predict the precise phenotype that will be attained in different situations, and serve as a foundation for precision diagnostics and treatment strategies focused on the macrophages.

In this work, we seek to interrogate the programmable landscape in macrophages to identify regulatory panels that govern the precise macrophage polarization state. Toward this, we configured a computational pipeline that uses an integrative graph-theoretical approach where differential transcriptomes of macrophages were mapped onto the knowledge-based genome-scale gene regulatory network (hGRN), computed connected sets of top-ranked perturbations, and identified regulatory barcodes defining different polarization states ultimately. We then experimentally evaluated specific RFs in the M1 regulatory barcode in a THP-1-derived macrophage cell line. We also probed the role of these specific RF-sets in the S. aureus infected human monocyte-derived macrophages and cell-line infection model. Together, we validated genes in the M1 barcode and demonstrated that knocking down specific RF-sets in the barcode attenuated the expression of M1 markers and increased the expression of M2 genes. We further showed that such a state switch leads to hyper-susceptibility to S. aureus infection.

Results

Subnetworks from contextualized hGRN identify regulatory barcodes for macrophage polarization states

We first identified the ‘programmable space’ in macrophages in terms of the nature and range of transcriptional reprogramming that they achieve in response to different immunostimulants. The stimulants belong to broad classes of pro- and anti-inflammatory cytokines such as Toll-like receptors (TLRs) ligands, immune complexes, interleukins, glucocorticoids, or their selected combinations, comprehensively covering the macrophage polarization spectrum. We probed the programmable space by constructing and analyzing 28 unbiased ‘response networks’ of macrophages, corresponding to the treatment with 28 different immunostimulants. Toward this, we first constructed a knowledge-based hGRN. Our hGRN covers regulatory elements from the whole genome and contains 27,702 genes and 890,991 RF–RF and RF–target gene (TG) directed interactions, which was rendered context-specific for each stimulant using their respective differential transcriptome data. We then subjected the 28 contextualized hGRNs to our network mining algorithm, which identified a connected set of genes that collectively vary the most in their expression levels in response to the given stimulant. This yielded 28 different subnetworks containing the highest ranked regulatory perturbations, with nodes ranging from 4032 to 6785 nodes (Supplementary file 4, data file S2). The resultant subnetworks containing connected sets of weighted and directed interactions capture cascades of regulatory events which are in essence the top-perturbed networks (TPNs) in each condition. Next, we configured a pipeline to identify the most influential TFs from the resultant TPNs. We used EpiTracer, an algorithm previously developed in the laboratory (Sambaturu et al., 2016), which identifies the highest ranked influential nodes in a given network using a ripple centrality measure. This step results in the identification of the genes that have the strongest ‘causal’ association with a given perturbation pattern. The word ‘causal’ here refers to the potential of the given gene for wielding an influence on the system, as estimated by the network analysis. Put together, we identified epicentric TFs (Ripple centrality score >0) which are differentially expressed in each of these TPNs. When pooled, these RFs from all stimulants result in a set of 265 RFs (RF-set1— Supplementary file 4, data file S3), defining the programmable space in relation to macrophage polarization.

Further, to probe the number of distinct polarization states, we carried out agnostic clustering based on the differential transcriptome of RF-set1, using a Monte Carlo reference-based consensus clustering (M3C) method. This resulted in 13 different clusters. The clusters and the associated members are shown in (Supplementary file 3). The relative cluster stability index (RCSI) and Monte Carlo p-values further validated that the cluster stability (Figure 1, Figure 1—figure supplement 2A, B). Put together, it is clear that the TPNs group into 13 broad clusters (Figure 1A), each representing a distinct polarization state across the spectrum. Further, the cluster member associations inferred from RF-set1 were found to be concordant with the clustering pattern (Figure 1—figure supplement 3, cophenetic coefficient = 0.68; p-value = 1.25e−51) inferred by considering the entire differential transcriptome (n = 12,164). This clearly indicates that the RFs in RF-set1 are sufficient to characterize a given polarization state and that the subnetworks from the contextualized hGRNs define the macrophage polarization spectrum.

Figure 1 with 3 supplements see all
Download asset Open asset
A clustergram showing K = 13 for samples spanning the macrophage polarization spectrum and RF barcodes characteristic of each polarization state.

The clustering pattern was obtained by using M3C, a widely used agnostic clustering method based on Monte Carlo referencing that uses stability selection to estimate the number of clusters. (A) Cluster–member associations in the consensus matrix for K = 13. Clusters with a single member are starred. Clusters 2 and 12 are singletons. (B) A heatmap showing pathways commonly enriched in inflammation and other immunological processes. The pathways marked in red are upregulated, while those marked in blue are downregulated. The metric chosen was the negative logarithm of the p-value for each associated pathway. (C) RF barcodes which are characteristic of the 13 different clusters. Each barcode is a heatmap of the differential expression profile of the identified transcription factors (as compared to the unstimulated Mo state) in each cluster. The length of the barcode (which is the number of RFs in each cluster) is indicated within parentheses. Barcodes of members within a cluster resemble each other extensively and are shown together.

Functional enrichment analysis of the genes using the clusterProfiler tool (Wu et al., 2021) with pathways related to inflammation and other immunological processes from the Reactome pathway resource with default parameters (Fabregat et al., 2017) encompassing the regulatory cores of each cluster established their biological relevance. Clusters C11, C12, C8, and C9 show strong activation of interleukin signaling, TLR cascades, as well as NF-κB pathways indicating a robust pro-inflammatory response (Figure 1B). In contrast, Clusters C3, C4, and C6 display significant downregulation of TLR cascades and absence of interleukin signaling, suggesting suppression of the innate immune response. Next, we set out to identify combinations of key regulatory factors (RFs) for each cluster. The subnetworks from which the RF-set1 and the subsequent clustering pattern was derived serve as precise molecular definitions for each polarization state. Here, we seek to identify minimal gene sets from each cluster for identifying gene panels that are the most influential in guiding the differentiation into specific polarization destinations. Our ultimate goal was to probe if any of them could serve as switching factors between different functional states. The core networks of each cluster provide us connected sets of weighted and directed interactions which capture cascades of regulatory events. The complexity in these subnetworks arises due to the plurality of interactions between RFs since (1) a given RF can regulate multiple targets, (2) multiple RFs can regulate a given target, and (3) RFs themselves are regulated by other RFs. A computational pipeline was configured to discover combinations of TFs with specific expression patterns from the subnetworks for each polarization state. The pipeline consists of computational steps to identify (1) common cores for each polarization state, and (2) differentially expressed epicenters of different members of the clusters. The first step was achieved by taking an edge-level intersection of the TPNs within each cluster, which resulted in pruning the subnetworks to retain only those nodes and edges that are common and exhibit the same pattern in all members within each cluster. The pipeline yielded a 52 RF-set characteristic of C11 and a 42 RF-set characteristic of C3 and similarly for all other states. These RF-sets serve as barcodes for each state where color gradient reflects the gene expression changes of each RF (as compared to an unstimulated state) (Figure 1C). Put together, these barcodes amount to a union of 223 RFs (RF-set2) that can be considered to form a pool of central RFs for the polarization states studied here (RF-set2— Supplementary file 4, data file S3).

Identification of a 3-RF-set as M1 to M2 switching factors

We investigated if the barcodes could serve as switching factors to tune the polarization destination of macrophages. For this, we selected a stimulant that is known to drive unstimulated macrophages (Mo) to M1 and asked if we could switch it to an M2 destination instead. We start with the barcodes for both states (C11 representing M1 and C3 representing M2) and pruned them to retain (1) only those genes that occur in the barcodes for both the states and (2) exhibit reversal in the gene expression variation trend between the two states. This resulted in identifying a panel consisting of 16 RFs (STAT4, ETS2, NFKB1, BCL3, NFE2L2, MTF1, HIF1A, NFKBIA, HEY1, FOXO3, ZFX, ZNF24, XBP1, CEBPB, MYC, and NFE2) where each gene exhibits a significant quantitative difference in gene expression variation between M1 and M2 states, both with reference to the Mo state (Figure 2C). Of these, we selected the top-ranked genes based on the extent of gene expression variation in the two states and the number of targets they influenced. We identified two well-known RFs: STAT4 and NFKB1, and three additional RFs NFE2L2, CEBPB, and BCL3, that qualified our criteria to be potential switching factors. STAT4 and NFKB1 are well studied and clearly linked to the pro-inflammatory M1 state and have many targets. We verified that in our study system, they are indeed upregulated upon LPS induction and downregulated upon IL4 treatment (both with reference to Mo, Figure 2C). We focused on the three newly identified RFs NFE2L2, CEBPB, and BCL3 (referred to as NCB hereafter), which show the same expression trend and are positioned to have a high degree of influence in the network. Put together, the 3-RF combination has 1293 targets, covering about 53% of the TPN. Put together, the 3-RF combination has 1293 targets, covering about 53% of the TPN. The parts of the respective TPNs containing the regulatory cores for C11 (M1) and C3 (M2) along with the associated pathways are shown in Figure 2A and B, respectively.

Figure 2 with 2 supplements see all
Download asset Open asset
Switching factors that guide M1 to M2 destination.

(A) The regulatory core for the M1 cluster (C11). (B) The regulatory core for the M2 cluster (C3). The core networks contain the top perturbed regulatory interactions common across C11 members (stimulants: TPP, sLPS, and sLPS + IC) and C3 members (stimulants: IL4 and IL13), respectively. (C) A heatmap of the genes in the barcode that exhibits a reversal in the fold change pattern between M1 (C11) and M2 (C3). RFs that are selected for experimental validation are starred.

We hypothesized that knock-down of NFE2L2, CEBPB, and BCL3 in an M1 state would switch it to M2. To test this hypothesis, we designed an experiment (experimental design shown in Figure 3A) where we take THP-1-derived macrophages (taken as Mo) and stimulate them with sLPS to obtain an M1 state. We first verified that sLPS treatment is leading to an M1 state by measuring known M1 markers (IL1B in Figure 3B), which confirmed that they were indeed in an M1 state. We also measured the levels of M2 markers (IL10 in Figure 3B) and verified that there was no significant change. STAT4 is known to have an effect on the polarization state as its knockdown in M1 was shown to significantly attenuate the M1 state (Ishii et al., 2009) and we therefore used STAT4 as a positive control. We checked whether STAT4 was exhibiting the expected behavior in our experimental setup and we found that was indeed the case (Figure 3, Figure 3—figure supplement 1). We then performed siRNA-mediated combined knock-down of NCB, the 3-RF-set, to evaluate the M1 to M2 switch. We verified that the siRNA-mediated knockdown (referred to as siNCB) had worked by testing the gene expression values of the three NCB genes and finding them to be significantly reduced (Figure 3D, Figure 3—figure supplement 2). Upon stimulation with sLPS in the siRNA-treated cells, we observed that the known M1 markers were significantly reduced while the known M2 markers were significantly pronounced, clearly showing that the siRNA knockouts lead the cells to shift from M0 to M2 (Figure 3D). The change was statistically significant when compared with the parent THP-1 cells without knockdown as well as with non-targeting siRNA controls. In summary, we observe that the knockdown of the 3-gene set leads to a clear shift of the polarization state away from M1 toward M2 (significant decrease in the expression of CXCL2, IL1B, and iNOS) and significant increase in the classical M2 marker (ARG1), despite the sLPS trigger. In addition, we conducted siRNA knockdown experiments on the RFs NFE2L2, CEBPB, and BCL3, and observed a similar trend of decreased expression of M1 markers and increased expression of M2 markers (Figure 3—figure supplement 3). Among the three RFs, CEBPB had the most significant effect on the switching of M1 and M2 marker expression, with significant decreases in the expression of CXCL2 and IL1B (M1 markers) and significant increases in the expression of ARG1, IL10, and MRC1 (M2 markers). In comparison to STAT4 (positive control—Figure 3C), knockdown of CEBPB, NFE2L2, and BCL3 had significantly higher effects on the expression of M1 and M2 markers, indicating their role in modulating the inflammatory response.

Figure 3 with 4 supplements see all
Download asset Open asset
siRNA knockdown of NFE2L2, CEBPB, and BCL3 in LPS infected THP-1-derived macrophage cell line attenuates macrophages polarizing to C11 state (M1).

(A) Schema describing the experimental design for testing the role of the 3-gene set NCB, predicted to be a switching factor combination between M1 and M2 destinations. The LPS and IL4 treatment conditions are included to verify the experimental setup. The non-targeting siRNA is used as a negative control while siSTAT4 is used as a positive control. siNCB is a knock-down of the 3-gene set (NCB) which is evaluated in this study. (B) Gene expression changes of selected RFs and known markers of M1 (IL1B) and M2 (IL10) in THP1 cell lines upon LPS and IL4 treatment (for 72 hr), respectively. Color coding scheme is similar to that in (A). THP1 monocytes were treated with PMA (20 ng/ml) for 16 hr. Post 24 hr, LPS (100 ng/ml) and IL-4 (1000 IU) treatment was given for 72 hr. Two-way ANOVA (ns: p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001; N = 3 biological replicates) is used for estimating significance in gene expression fold change normalized over untreated cells. (C) Gene expression changes in the M1 markers upon siRNA knockdown of STAT4 (positive control). THP1 monocytes were treated with PMA (20 ng/ml) for 16 hr. STAT4-specific siRNA was transfected. Post 8 hr, LPS (100 ng/ml) treatment was given for 72 hr. Gene expression changes in M1 markers were quantified (two-way ANOVA; ns: p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001; N = 3 biological replicates; NT: non-targeting siRNA). (D) Gene expression changes in the M1 and M2 markers upon siRNA knockdown of the NCB set. THP1 monocytes were treated with PMA (20 ng/ml) for 16 hr. NCB (NFE2L2, CEBPB, and BCL3) specific siRNA was transfected. Post 8 hr, LPS (100 ng/ml) treatment was given for 72 hr. Gene expression changes of M1 and M2 markers were quantified (two-way ANOVA; ns: p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001; N = 3 biological replicates; NT: non-targeting siRNA).

In addition, we also identified a group of RFs that may be involved in switching macrophages from an M2 functional state to an M1 state using the similar approach. These factors include FOXQ1, SOX8, ETS2, MTF1, HIF1A, CEBPB, XBP1, IRF7, and TCF7L2 (Figure 2—figure supplement 1).

Knockdown of NCB restricts the growth of S. aureus within macrophages

S. aureus is a facultative intracellular pathogen causing a broad spectrum of diseases such as minor skin infections to serious bloodstream infections and endocarditis. This pathogen is known to survive inside the host cell and escapes from professional phagocytic cells such as macrophages through various immune evasion strategies, especially through cytoprotective mechanisms by altering the host gene expression (Koziel et al., 2009; Pidwill et al., 2020).

We performed differential analysis of peripheral macrophages infected with S. aureus using the publicly available data (Koziel et al., 2009), and profiled the expression levels of RFs in M1 (C11) barcode and M1 markers. This analysis showed an upregulation of NCB and other RFs in the M1 barcode at the 8, 24, and 48 hr time point (Figure 4—figure supplement 1). We then tested the same in an in vitro S. aureus infection model using THP-1 macrophages and made a very similar observation, where the mRNA expression levels of the STAT1, NCB, M1, and M2 markers were quantified at 12, 24, 48, and 72 hr time points (Figure 4—figure supplements 2 and 3). Specifically, the expression levels of NFE2L2, CEBPB, and BCL3 were significantly upregulated in the THP-1 macrophages upon S. aureus infection with respect to the uninfected control at 24 hr (Figure 4—figure supplement 3). This suggests that macrophages infected with S. aureus polarize to the M1 state within 24 hr. Hence, this time point was used for further experiments.

Next, to evaluate the potential of NCB in regulating polarization of macrophages during S. aureus infection, we used the same in vitro infection setup but using the NCB knockdown cells. (We ascertained that these RFs were sufficiently responsive at the selected time point of 24 hr (Figure 3—figure supplement 4A–C).) We observed a significant shift from M1 to M2 with the downregulation in the expression of M1 markers (CXCL2, IL1B, iNOS, and SOCS3) (Figure 4B, Figure 4—figure supplement 4) and upregulation in the expression of M2 markers (TGFB and IL10) (Figure 4B). Interestingly, we observed a 1.6-fold significant increase in the burden of S. aureus within these macrophages (Figure 4C). This clearly indicates that knockdown of NCB combination renders the macrophage more conducive for S. aureus to proliferate.

Figure 4 with 4 supplements see all
Download asset Open asset
siRNA knockdown of NCB (NFE2L2, CEBPB, and BCL3) in S. aureus infected macrophage increases infectivity.

(A) Schema describing the experimental design. (B) Effect of the NCB set knockdown in the S. aureus infection model. siRNA knockdown of NFE2L2, CEBPB, and BCL3 in THP-1 cells infected with S. aureus leads to a significant decrease in the gene expression of the M1 markers (CXCL2, IL1B, iNOS, and SOCS3) and an increase in the expression of the M2 markers (TGFβ and IL10). Color coding is based on the schema shown in (A). THP1 monocytes were treated with PMA (20 ng/ml) for 16 hr. NFE2L2-, CEBPB-, and BCL3-specific siRNAs were transfected. Post 24 hr, S. aureus infection at 1:10 MOI was given for 24 hr. Gene expression levels of M1 and M2 markers were assessed (two-way ANOVA; ns: p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001; N = 3 biological replicates; NT: non-targeting siRNA). (C) In vitro S. aureus CFU assay. THP-1 cells were differentiated to macrophages with PMA for 16 hr. Post 24 hr, siRNA specific for NFE2L2, BCL3, and CEBPB were transfected. S. aureus infection (MOI 10) was given for 2 hr prior to 2 hr gentamicin treatment. CFU assay was performed at 0 and 24 hr (two-way ANOVA; ns: p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001; N = 3 biological replicates; NCB: NFE2L2, CEBPB, and BCL3). CFU: colony-forming units.

Discussion

Macrophages display a wide array of functions, from being killers of pathogens to healers of damaged tissue (Murray, 2017; Murray and Wynn, 2011). Their versatility is attributed to their ability to respond to specific physiological or pathological cues, leading to their polarization into different states, each with a distinct functional phenotype. A range of molecular triggers that cause differentiation in macrophages is known (Hu et al., 2021; Jaguin et al., 2013; Xue et al., 2014). It is also well known that the differentiation spans a full spectrum of phenotypes with the classically activated M1 and the alternatively activated M2 states at two ends of the spectrum. The molecular profiles that define or cause these states, however, have not been sufficiently characterized. In particular, very little is known about the regulatory genes that control and guide the polarization in a specific direction. It is of great interest to identify RFs that are most influential in guiding the polarization. Given the importance of macrophages, several studies have investigated the variations in the transcriptomes in different diseases (Benoit et al., 2008; Mosser and Edwards, 2008; Ross et al., 2021; Schultze, 2016; Zheng et al., 2018) as well as upon exposure of macrophages to different exogenous and endogenous stimuli (Hu et al., 2021; Jaguin et al., 2013; Xue et al., 2014), which have resulted in a wealth of data that are publicly accessible. These large datasets provide opportunities for addressing a variety of questions.

In this study, we algorithmically identify distinct polarization states, minimal regulatory signatures for each state as TF barcodes, and demonstrate that manipulation of the barcode leads to loss of switching, in an example case. Additionally, our work has indicated that arresting the switch to M1 leads to hyper-susceptibility to S. aureus infection in cell lines, suggesting that M1 has a distinct role in limiting the extent of infection. Two aspects in our computational workflow are distinct from the previous methods, which facilitated the identification of barcodes characteristic of different macrophage polarization states. The first is the use of an unbiased knowledge-based gene regulatory network as compared to the more common correlation-based interaction networks. In the latter, the interactions are statistical associations, with no guarantee that the interaction is ‘real’ in the given biological system (Petri et al., 2015; Ravichandran and Chandra, 2019). We overcome this limitation by consolidating a comprehensively curated knowledge-based network where each interaction is based on experimental data reported in databases and in primary literature. We have previously carried out a systematic comparison and have shown that knowledge-based networks are superior to correlation-based networks in capturing biologically relevant genes and pathways (Ravichandran and Chandra, 2019). Our macrophage specific GRN (subset of hGRN) network contains 326, 281 interactions among 742 RFs and 11,423 TGs, which is more comprehensive than the previously reported knowledge-based macrophage GRNs in literature (Hörhold et al., 2020; Palma et al., 2020). The second is the network interrogation method. Our method of contextualizing the knowledge-based network for diverse stimulants using the corresponding transcriptomes allowed a quantitative comparison of variations over a common background network, yielding a top-perturbed regulatory subnetwork in each case. Our network interrogation method identifies the most influential ‘epicentric’ TFs, which lead to the derivation of quantitative barcodes of RF panels. To the best of our knowledge, such barcodes have not been reported earlier even for the well-studied M1 and M2 states. We are hopeful our results of epicentric RFs and their molecular interaction trajectories will serve as key inputs for several investigations on the RFs individually as well as for specific RF panels. The response networks for each stimulant also serve as a rich resource of the top-ranked regulatory pathways in each case.

As a first step into the identification of barcodes, we used a pooled set of differentially expressed epicentric RFs (n = 265) in different conditions that we identify from the networks and perform an unbiased clustering of the macrophages. We used a rigorous clustering method, which produced a robust clustering pattern that clearly indicated that the 28 stimulants group into 13 polarization states. A clustering pattern was described earlier for the same dataset based on the top 1000 most varying differentially expressed genes (DEGs) from each condition, which resulted in nine distinct states (Xue et al., 2014). The clustering pattern that we get here from the top-ranked epicenters in each yielded a similar pattern, indicating that a subset of DEGs that occur in our topnets are sufficient to achieve the differentiation. In fact, our gene-regulatory models implied that the saturated (SA and PA) and unsaturated (LA, LiA, and OA) fatty acids mediate different modes of resolution that were grouped together in the previous clustering pattern and are now resolved into two sub-branches. Similarly, the effects of IFNγ and sLPS, which were clubbed in the previous pattern, are now resolved, consistent with the known regulatory differences (Hoeksema et al., 2015; Kang et al., 2019). The principal takeaway from this analysis is not the exact number of clusters but rather the molecular basis it provides for the differentiation of functional states, with M1 and M2 representing two ends of the spectrum. Several other states are dispersed within the polarization spectrum, which can be described as a punctuated continuum. For our switching studies, we focused on clusters C11 (M1-like) and C2 (M2-like) due to their established functional relevance. However, future studies are required to explore the functional relevance of other clusters.

Our results suggest that the barcodes could serve as the regulatory handles to attain different polarization states with different functional phenotypes. We test that hypothesis for the M1/M2 axis and indeed demonstrate that siRNA knockdown of the RFs in the barcode achieves polarization state switching from M1 to M2 polarization states. In the M1 barcode, we selected the RFs upregulated as compared to the baseline (M0): (NBC—Nuclear Factor, Erythroid 2 Like 2 (NFE2L2), B-Cell Lymphoma-3 (BCL3), and CCAAT/enhancer-binding protein beta (CEBPB)). The roles of NFE2L2 and BCL3 in inflammatory responses in general have been discussed earlier (Wessells et al., 2004; Kobayashi et al., 2016). By knocking down the expression of these TFs in LPS-treated THP-1-derived macrophage cell line, the cells were clearly seen not only to have not attained an M1 state but to have shifted toward the M2 state. This establishes their role as a switch between the states—where an upregulation moves them from an M0 to M1 while a knockdown shifts them from M0 to M2, even after exposure to an M1-stimulant, thus indicating them to be essential for establishing the M1 state. Individual knockdown of the three genes was also performed to assess the contribution of each gene. It was observed that knocking down CEBPB led to a significant downregulation in the expression of M1 markers and an increase in M2 marker expression. In contrast, NFE2L2 and BCL3 knockdown resulted in decreased expression of M1 markers without a corresponding significant increase in M2 markers. These results suggest that CEBPB is crucial for the M1 to M2 transition. After having established an arrest of polarization to M1, we were curious to see if it resulted in a functional phenotype. Our experiments with S. aureus infection clearly showed that the NBC knockdown led to increased infectivity of S. aureus. Expression of these RFs and their TGs are involved in cellular response to stress, tumor necrosis factor signaling pathway, and senescence-associated secretory phenotype, suggesting that inhibition of inflammatory-associated processes halts M1 phenotype and promotes the M2-like phenotype, which renders them hyper-susceptible to S. aureus infection.

In conclusion, we report a systems-level analysis of the gene regulatory networks to identify RFs that define different macrophage activation states. Perturbation of these factors for reprogramming the macrophage subpopulations to desired polarization states can be explored for applications in controlling the inflammation gradient in different diseases such as sepsis, autoimmune conditions, and chronic infections.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Staphylococcus aureus Cowan 1)Staphylococcus aureus Cowan 1MTCC, IMTECH, ChandigarhMTCC 902
Cell line (Homo sapiens)THP-1NCCS, PuneRRID:CVCL_0006
Chemical compound, drugPhosphate-buffered saline (PBS), pH 7.4GibcoCat# 10010023
Chemical compound, drugFetal bovine serum (FBS)Gibco, Thermo Fisher ScientificCat# 10270106
Chemical compound, drugRPMI 1640 Medium, powderGibco, Thermo Fisher ScientificCat# 31800022
Chemical compound, drugPhorbol 12-myristate 13-acetate (PMA)Sigma-AldrichCat# P8139
Chemical compound, drugPolyethyleneimineSigma-AldrichCat# 40872-7
Chemical compound, drugOpti-MEM Reduced Serum MediumGibcoCat# 31985070
Chemical compound, drugTRI ReagentSigma-AldrichCat# T9424
Chemical compound, drugLipopolysaccharideSigma-AldrichCat# 8630
Chemical compound, drugIL-4R&D SystemsCat# 204-IL
Chemical compound, drugGentamicinHIMEDIACat# SD195
Chemical compound, drugTriton X-100Sigma-AldrichCat# T8787
Chemical compound, drugLuria BrothHIMEDIACat# G575
Chemical compound, drugAgar powder, Bacteriological gradeHIMEDIACat# 9002-18-0
Sequence-based reagentPrimers for qRT-PCRSee Supplementary file 1See Supplementary file 1
Sequence-based reagentsiRNA Negative controlEurogentecSee Supplementary file 1
Sequence-based reagentsiGENOME Human NFE2L2DharmaconCat# M-003755-02-0005
Sequence-based reagentsiGENOME Human BCL3DharmaconCat# M-003874-02-0005
Sequence-based reagentsiGENOME Human CEBPBDharmaconCat# M-006423-03-0005
Sequence-based reagentsiGENOME Human STAT4DharmaconCat# M-011784-01-0005
Software, algorithmGraphPad Prism 10.0 softwareGraphPadRRID:SCR_002798
Software, algorithmMicrosoft ExcelMicrosoftRRID:SCR_016137
Software, algorithmMicrosoft PowerPointMicrosoftRRID:SCR_023631

Transcriptomes of macrophage polarization states

Request a detailed protocol

We retrieved transcriptomes of macrophages stimulated with a diverse set of stimulants (GSE46903) from NCBI-GEO (Barrett, 2020; Xue et al., 2014). Briefly, the authors of this study purified monocytes from peripheral blood mononuclear cells of healthy controls and subjected them to granulocyte–macrophage colony-stimulating factor or macrophage-colony stimulating factor for 72 hr to generate baseline macrophages (Mo). Then, they treated the Mo with one of the 28 different stimulants for different time points ranging from 30 min to 72 hr and generated transcriptomes from each stimulant and each time point. The stimulants spanned across the broad spectrum of pro- and anti-inflammatory triggers to generate a wide range of polarization states. The triggers and their end time points (n = 145) considered for this analysis are shown in Supplementary file 1. We chose this experiment for analysis since it had the maximum number of stimulants (n = 28) compared to other publicly available datasets. Such an experiment will allow us to capture stimulant-centric regulator factors without the effect of technical and biological confounders. Further, to study the relevance of macrophage polarization in acute bacterial infections, transcriptomes of peripheral macrophages exposed to S. aureus were retrieved from NCBI-GEO (GSE13670) (Koziel et al., 2009).

Raw data from the respective transcriptome experiments were retrieved and pre-processed in a platform-specific manner using appropriate packages in R (Carvalho et al., 2019; Gautier et al., 2004). Probes below the detection limit in ≥80% of the samples in each dataset were filtered out, and multiple probes associated with the same gene were summarized by taking an average. We then performed differential analysis for the macrophages treated with stimulants compared to their untreated counterparts, using the limma package in R (Ritchie et al., 2015). Genes with fold change ≥±1.3 and a false discovery rate corrected q-value ≤0.05 computed using the Benjamini–Hochberg procedure (Benjamini and Hochberg, 1995) were considered to be statistically significant DEGs.

Reconstruction of hGRN

Request a detailed protocol

We reconstructed a comprehensive knowledge-based hGRN, consisting of RFs to TG and RF to RF interactions. Here, RFs refer to TFs and cofactors. To achieve this, we curated the experimentally determined regulatory interactions (RF–TG and RF–RF) associated with human RFs (Wingender et al., 2013) in this study. These interactions were obtained from the following resources: (1) literature curated resources such as the Human Transcriptional Regulation Interactions database (HTRIdb) (Bovolenta et al., 2012), Regulatory Network Repository (RegNetwork) (Liu et al., 2015), Transcriptional Regulatory Relationships Unravelled by Sentence-based Text-mining (TRRUST) (Han et al., 2015), and TRANSFAC resource from Harmonizome (Rouillard et al., 2016); (2) ChEA3 containing ChIP-seq determined interactions (Keenan et al., 2019); and (3) high confidence protein–protein binding interactions (RF–RF) were retrieved from human protein–protein interaction network-2 (hPPiN2) (Ravichandran et al., 2021). The resulting interactions from both categories were collated and made a non-redundant set. This resulted in a connected set of RF–RF and RF–TG interactions that comprise a ‘master’ hGRN with 27,702 nodes and 890,991 interactions (Supplementary file 4, data file S1). This extensive hGRN, which encompasses the experimentally determined interactions/edges, was used to infer stimulant-specific hGRNs and top paths using our in-house network mining algorithm, ResponseNet. We have previously demonstrated that ResponseNet, which utilizes a knowledge-based network and a sensitive interrogation algorithm, outperformed data-driven network inference methods in capturing biologically relevant processes and genes. Furthermore, we have previously conducted a systematic comparison of our network mining strategy with other data-driven module detection methods, including jActiveModules (Ideker et al., 2002), WGCNA (Langfelder and Horvath, 2008), and ARACNE (Margolin et al., 2006). Our findings demonstrated that our approach outperformed conventional data-driven network inference methods in capturing the biologically pertinent processes and genes (Ravichandran and Chandra, 2019). This was also validated by comparison with simulated data (Sambaturu et al., 2021).

Model contexting and identification of epicentric RF-set

Request a detailed protocol

Starting from the hGRN, stimulant-specific GRNs were derived by weighting the master hGRN network with differential transcriptomes of stimulants (ie., stimulant vs. baseline) and generated weighted networks for individual stimulants. A sensitive network mining approach, previously established in the laboratory, was then applied to identify top-ranked activated and repressed sub-networks (ResponseNet) using Equations 1–3. This method of computing response networks, which involves a knowledge-based network and a sensitive interrogation algorithm, has been shown to outperform data-driven network inference methods in capturing biologically relevant processes and genes (Ravichandran and Chandra, 2019; Sambaturu et al., 2021). Briefly, the network mining algorithm works by computing minimum weight shortest paths, in which each path begins from a source node and ends with a sink node, identifying connected sets of edges that make up the least-cost paths. The shortest paths between all pairs of genes were computed using Dijkstra’s algorithm using the Zen library implemented in Python 2.7. For a path of length n, the path cost was calculated as a summation of the edge weights EWij of all edges forming the path, and normalized by the path length. Subsequently, all shortest paths were sorted based on their path costs from lowest to highest, and the top 1% were considered to constitute the TPN. Nodes forming the TPN were evaluated for enrichment of DEGs using Fisher’s exact test. Further, the epicentric nodes in each TPN were identified using the EpiTracer algorithm developed previously in the laboratory (Sambaturu et al., 2016). EpiTracer estimates the influential state of a node by computing ripple centrality, which is the product of closeness centrality and outward reachability.

The TPN has two components, a top-activated network and a top-repressed network, generated by using appropriate node-weighting schemes. To generate the activated network, the weight of node i in a condition treated with stimulant A (SA) was computed as

(1) Ni(SA)=FCi(SA/SB)

where FC was the fold change of gene i in a condition treated with stimulant A (SA) with respect to the baseline SB (antilog values were used to compute fold changes). To generate the repressed network, the node weight of node i in SA was computed as

(2) Ni(SA)=FCi(SB/SA)

The edge weight EWij in a given condition SA for an edge e comprised of nodes Ni(SA) and Nj(SA) was calculated as

(3) EWij(SA)=1/(Ni(SA)∗Nj(SA))1/2

where Ni(SA) and Nj(SA) are the node weights of nodes i and j, respectively. Lower the edge weight, stronger is the interaction between the nodes.

These subnetworks were combined to obtain a TPN for each stimulant. Nodes in the TPN were rank-ordered based on the ripple centrality score. Differentially expressed RFs with the ripple centrality score >0 were considered as influential TFs for a given TPN. The resultant RF-set defines the regulatory control for a given stimulant (Figure 1—figure supplement 1).

Clustering macrophage polarization states

Request a detailed protocol

We performed Monte Carlo reference-based consensus clustering using the M3C package implemented in R (John et al., 2020) to identify the optimal number of macrophage polarization states. Spectral clustering with Pearson’s correlation coefficient as a correlation metric for 200 iterations with 250 replicates was used to estimate the cluster structure that best fits the differential transcriptome of (1) entire transcriptome (n = 12,164) and (2) RF-set1 (n = 265). Further, RCSI and Monte Carlo adjusted p-values were computed using M3C to test the number and structure of the clusters. For the cluster visualization and comparison, the Dendextend package in R (Galili, 2015) was used.

Network visualization and enrichment analysis

Request a detailed protocol

We have visualized networks using Cytoscape 3.2.0 in Allegro Spring-Electric layout (Shannon et al., 2003). We performed enrichment analysis for the genes of the regulatory cores of each cluster using the Reactome pathway resource with default parameters (Fabregat et al., 2017), and the resultant hits with q-value ≤0.05 were considered to be significant (using a hypergeometric test).

Reagents, cell line, and bacteria

Request a detailed protocol

General laboratory chemicals were procured from Merck, Promega, Invitrogen, Himedia, and Sigma-Aldrich. Tissue culture plastic wares were purchased from Biofil, BD Falcon, and Tarson. Phorbol 12-myristate 13-acetate (PMA #P8139) and LPS (#8630) were procured from Sigma-Aldrich. IL4 (#204-IL) was purchased from R&D Systems. The THP-1 human monocytic cell line was obtained from the National Centre for Cell Sciences, Pune, India. The cell line has been authenticated by STR profiling and is negative for mycoplasma contamination as tested via PCR. It was cultured in RPMI (Gibco, Invitrogen Corporation, USA) supplemented with 10% heat-inactivated fetal bovine serum (Sigma-Aldrich, USA). THP-1 cells were treated with PMA (20 ng/ml) for 24 hr for their differentiation into macrophages. Post-PMA treatment, THP-1-derived macrophages were washed and kept in complete RPMI media followed by treatment/infection as mentioned. S. aureus Cowan 1 (MTCC-902) was procured from Microbial Type Culture Collection, IMTECH Chandigarh. S. aureus was grown in Luria Broth at 37°C overnight. S. aureus was harvested and washed with phosphate-buffered saline (PBS) once the OD600 reached 0.6, and was then resuspended in the cell culture RPMI complete medium without antibiotics. S. aureus was used at 10 MOI for in vitro experiments.

RNA isolation and quantitative real-time PCR

Request a detailed protocol

Total RNA was isolated from PMA-differentiated THP-1 cells using TRI Reagent (Sigma-Aldrich, USA) as per the manufacturer’s protocol. For RT-PCR, 1 μg of total RNA was converted into cDNA using the First Strand cDNA Synthesis Kit (Bio-Rad, USA) according to the manufacturer’s protocol. Quantitative real-time PCR was performed with SYBR Green PCR mix (KAPABIOSYSTEMS, USA) for quantification of TG expression. Amplification of the housekeeping gene GAPDH was used as an internal control. The list of primers used for the experiment is provided in Supplementary file 2.

Transient transfection

Request a detailed protocol

PMA-differentiated THP-1 cells were transiently transfected with 100 nM targeted siRNA using low molecular weight polyethyleneimine (Sigma-Aldrich, 40872-7). Further, 36 hr post-transfection, cells were treated or infected as indicated for the required time and processed for analysis.

In vitro CFU analysis

Request a detailed protocol

PMA-differentiated THP-1 cells were infected with S. aureus Cowan I at an MOI of 10 for 2 hr to facilitate internalization. Following 2 hr, cells were washed with complete DMEM (without antibiotics), and Gentamicin (100 μg/ml) treatment was given for 2 hr to remove extracellular bacilli. Cells were lysed in 0.1% Triton X-100 containing 1X PBS, and CFU estimation was done at 0 hr (after gentamicin treatment) and 24 hr by plating appropriate dilution on LB plates.

Statistical analysis

Request a detailed protocol

Levels of significance for comparison between samples were determined by the Student’s t-test distribution and one-way ANOVA followed by Tukey’s multiple comparisons. The data in the graphs are expressed as the mean ± standard error for the values from at least three or more independent experiments, and p-values ≤0.05 were defined as significant. R version 3.6.3 was used for all statistical analyses.

Code availability

Request a detailed protocol

The code for EpiTracer is available at https://github.com/NarmadaSambaturu/EpiTracer (Sambaturu and Mishra, 2019).

Data availability

We used transcriptome data from a previously published work involving macrophages stimulated with a diverse set of stimulants (GSE46903).

The following previously published data sets were used

References

Article and author information

Author details

  1. Sathyabaarathi Ravichandran

    IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2488-8617

  2. Bharath Bhatt

    Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
    Contribution
    Validation, Investigation, Writing – review and editing
    Contributed equally with
    Awantika Shah
    Competing interests
    No competing interests declared

  3. Awantika Shah

    Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
    Contribution
    Validation, Investigation, Writing – review and editing
    Contributed equally with
    Bharath Bhatt
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3806-4360

  4. Debajyoti Das

    Department of Biochemistry, Indian Institute of Science, Bangalore, India
    Contribution
    Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2992-0009

  5. Kithiganahalli Narayanaswamy Balaji

    Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
    Contribution
    Resources, Supervision, Validation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  6. Nagasuma Chandra

    1. IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India
    2. Department of Biochemistry, Indian Institute of Science, Bangalore, India
    3. Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
    Contribution
    Conceptualization, Resources, Supervision, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    nchandra@iisc.ac.in
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9939-8439

Funding

Department of Biotechnology (SP DBT0-24-0023)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

Department of Biotechnology (SP DBT0-20-0025)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

Department of Biotechnology (BT/PR27952/INF/22/212/2018)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

Department of Biotechnology (BT/PR13522/COE/34/27/2015)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

Department of Biotechnology (BT/PR27352/BRB/10/1639/2017)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

Department of Science and Technology (EMR/2014/000875)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

Science and Engineering Research Board (JBR/2021/000011)

  • Sathyabaarathi Ravichandran
  • Bharath Bhatt
  • Awantika Shah
  • Debajyoti Das
  • Kithiganahalli Narayanaswamy Balaji
  • Nagasuma Chandra

JC Bose National Fellowship (SB/S2/JCB-025/2016)

  • No recipients declared.

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 supported by funds from the Department of Biotechnology BIC and NNP projects (SP|DBT0-24-0023 and SP|DBT0-20-0025) and the Department of Biotechnology, Department of Science and Technology—Fund for Improvement of S&T Infrastructure in Higher Educational Institutions (DST-FIST), the University Grants Commission, and the Department of Biotechnology (DBT), Government of India; (DBT)-Indian Institute of Science partnership program (Phase-II at IISc, BT/PR27952/INF/22/212/2018); (DBT, No. BT/PR13522/COE/34/27/2015 and BT/PR27352/BRB/10/1639/2017 to KNB) and the Department of Science and Technology (DST, EMR/2014/000875 to KNB). KNB also thanks the Science and Engineering Research Board (SERB), DST, for the award of JC Bose National Fellowship (JBR/2021/000011 and SB/S2/JCB-025/2016) for the requisite funding support.

Version history

  1. Preprint posted:
  2. Sent for peer review:
  3. Reviewed Preprint version 1:
  4. Reviewed Preprint version 2:
  5. Version of Record published:

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.91547. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2023, Ravichandran 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.

Metrics

  • 1,144
    views
  • 57
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Sathyabaarathi Ravichandran
  2. Bharath Bhatt
  3. Awantika Shah
  4. Debajyoti Das
  5. Kithiganahalli Narayanaswamy Balaji
  6. Nagasuma Chandra
(2026)
Knockdown of a regulatory barcode shifts macrophage polarization destination from M1 to M2 and increases pathogen burden upon S. aureus infection
eLife 12:RP91547.
https://doi.org/10.7554/eLife.91547.3

Share this article

https://doi.org/10.7554/eLife.91547

Categories and tags

Research organism