Knock-down of a regulatory barcode shifts macrophage polarization destination from M1 to M2 and increases pathogen burden upon S. aureus infection
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eLife assessment
The authors of this manuscript address the following question in the immunology field: what are 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 but the analyses remain incomplete without further validation in primary cells or by examining the identified genes in the in vivo setting.
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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. Towards this, we configure an integrative network analysis pipeline that utilizes macrophage transcriptomes in response to 28 distinct stimuli and reconstructs contextualized human gene regulatory networks, and identifies epicentres of perturbations in each case. We find that these contextualized regulatory networks form a spectrum of thirteen 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 i.e., 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 regulatory factors that may be targeted to modulate immune responses.
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eLife assessment
The authors of this manuscript address the following question in the immunology field: what are 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 but the analyses remain incomplete without further validation in primary cells or by examining the identified genes in the in vivo setting.
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Reviewer #1 (Public Review):
Summary:
Ravichandran et al investigate the regulatory panels that determine the polarization state of macrophages. They identify regulatory factors involved in M1 and M2 polarization states by using their network analysis pipeline. They demonstrate that a set of three regulatory factors (RFs) i.e., CEBPB, NFE2L2, and BCL3 can change macrophage polarization from the M1 state to the M2 state. They also show that siRNA-mediated knockdown of those 3-RF in THP1-derived M0 cells, in the presence of M1 stimulant increases the expression of M2 markers and showed decreased bactericidal effect. This study provides an elegant computational framework to explore the macrophage heterogeneity upon different external stimuli and adds an interesting approach to understanding the dynamics of macrophage phenotypes after …
Reviewer #1 (Public Review):
Summary:
Ravichandran et al investigate the regulatory panels that determine the polarization state of macrophages. They identify regulatory factors involved in M1 and M2 polarization states by using their network analysis pipeline. They demonstrate that a set of three regulatory factors (RFs) i.e., CEBPB, NFE2L2, and BCL3 can change macrophage polarization from the M1 state to the M2 state. They also show that siRNA-mediated knockdown of those 3-RF in THP1-derived M0 cells, in the presence of M1 stimulant increases the expression of M2 markers and showed decreased bactericidal effect. This study provides an elegant computational framework to explore the macrophage heterogeneity upon different external stimuli and adds an interesting approach to understanding the dynamics of macrophage phenotypes after pathogen challenge.
Strengths:
This study identified new regulatory factors involved in M1 to M2 macrophage polarization. The authors used their own network analysis pipeline to analyze the available datasets. The authors showed 13 different clusters of macrophages that encounter different external stimuli, which is interesting and could be translationally relevant as in physiological conditions after pathogen challenge, the body shows dynamic changes in different cytokines/chemokines that could lead to different polarization states of macrophages. The authors validated their primary computational findings with in vitro assays by knocking down the three regulatory factors-NCB.
Weaknesses:
One weakness of the paper is the insufficient analysis performed on all the clusters. They used macrophages treated with 28 distinct stimuli, which included a very interesting combination of pro- and anti-inflammatory cytokines/factors that can be very important in the context of in vivo pathogen challenge, but they did not characterize the full spectrum of clusters. Although they mentioned that their identified regulatory panels could determine the precise polarization state, they restricted their analysis to only the two well-established macrophage polarization states, M1 and M2. Analyzing the other states beyond M1 and M2 could substantially advance the field. They mentioned the regulatory factors involved in individual clusters but did not study the potential pathway involving the target genes of these regulatory factors, which can show the importance of different macrophage polarization states. Importantly, these findings were not validated in primary cells or using in vivo models.
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Reviewer #2 (Public Review):
Summary:
The authors of this manuscript address an important question regarding how macrophages respond to external stimuli to create different functional phenotypes, also known as macrophage polarization. Although this has been studied extensively, the authors argue that the transcription factors that mediate the change in state in response to a specific trigger remain unknown. They create a "master" human gene regulatory network and then analyze existing gene expression data consisting of PBMC-derived macrophage response to 28 stimuli, which they sort into thirteen different states defined by perturbed gene expression networks. They then identify the top transcription factors involved in each response that have the strongest predicted association with the perturbation patterns they identify. Finally, using …
Reviewer #2 (Public Review):
Summary:
The authors of this manuscript address an important question regarding how macrophages respond to external stimuli to create different functional phenotypes, also known as macrophage polarization. Although this has been studied extensively, the authors argue that the transcription factors that mediate the change in state in response to a specific trigger remain unknown. They create a "master" human gene regulatory network and then analyze existing gene expression data consisting of PBMC-derived macrophage response to 28 stimuli, which they sort into thirteen different states defined by perturbed gene expression networks. They then identify the top transcription factors involved in each response that have the strongest predicted association with the perturbation patterns they identify. Finally, using S. aureus infection as one example of a stimulus that macrophages respond to, they infect THP-1 cells while perturbing regulatory factors that they have identified and show that these factors have a functional effect on the macrophage response.
Strengths:
- The computational work done to create a "master" hGRN, response networks for each of the 28 stimuli studied, and the clustering of stimuli into 13 macrophage states is useful. The data generated will be a helpful resource for researchers who want to determine the regulatory factors involved in response to a particular stimulus and could serve as a hypothesis generator for future studies.
- The streamlined system used here - macrophages in culture responding to a single stimulus - is useful for removing confounding factors and studying the elements involved in response to each stimulus.
- The use of a functional study with S. aureus infection is helpful to provide proof of principle that the authors' computational analysis generates data that is testable and valid for in vitro analysis.
Weaknesses:
- Although a streamlined system is helpful for interrogating responses to a stimulus without the confounding effects of other factors, the reality is that macrophages respond to these stimuli within a niche and while interacting with other cell types. The functional analysis shown is just the first step in testing a hypothesis generated from this data and should be followed with analysis in primary human cells or in an in vivo model system if possible.
- It would be helpful for the authors to determine whether the effects they see in the THP-1 immortalized cell line are reproduced in another macrophage cell line, or ideally in PBMC-derived macrophages.
- The paper would benefit from an expanded explanation of the network mining approach used, as well as the cluster stability analysis and the Epitracer analysis. Although these approaches may be published elsewhere, readers with a non-computational background would benefit from additional descriptions.
- Although the authors identify 13 different polarization states, they return to the M0/M1/M2 paradigm for their validation and functional assays. It would be useful to comment on the broader applications of a 13-state model.
- The relative contributions of each "switching factor" to the phenotype remain unclear, especially as knocking out each individual factor changes different aspects of the model (Fig. S5).
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