s2fieldsofstudy: Biology Psychology

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Facilitating open science without sacrificing IP rights

Conditional Access Agreements could improve replicability of research and enhance Open Science without jeopardizing intellectual property rights.

R eplicability is a cornerstone of the scientific enterprise. Validating published scientific findings enhances their credibility and helps to build a self-correcting cumulative knowledge base. It also increases public trust in science (Wingen et al 2020). Unfortunately, the scientific community has been facing a considerable problem for at least two decades: the replication crisis (Ioannidis, 2005). Scientists in various disciplines have significant difficulties trying to verify published scientific findings (Baker, 2016). One prominent factor accounting for nonreplicability is diminished access to research materials required for replication (replication materials). This problem is particularly noticeable in computational studies: research that utilizes computational models, often with an immense amount of data. With the rise of powerful computers, machine learning and big data, computational studies are increasingly used in a variety of disciplines. This trend is evident in biology as well, including in systems biology, genomics, proteomics, and other areas (Markowetz, 2017). A famous example that demonstrates the importance of computational biology is the Human Genome Project. Developments in computational biology are crucial in advancing promising research prospects in areas such as vaccine antigen design and structural bioinformatics. A scientific paper alone would not typically enable others to replicate the study described therein (Merali, 2010). Replicating a computational study generally requires access to the code, software documentation, datasets, workflows, and other information regarding the methodology (Easterbrook, 2014). In most cases, however, authors do not publicly share these elements, which renders such studies impossible to replicate (Merali, 2010;Stodden et al, 2018). The problem of diminished access to replication materials has been reported as a major stumbling block impeding the replicability of computational biology studies (Crook et al, 2013;Miłkowski et al, 2018).

IP law and replicability
Various factors contribute to the restricted access to materials: avoiding criticism, fear of falsification and retraction, or a desire to stay ahead of peers. Commercial and proprietary concerns also play a significant role in the decision of scientists and organizations to conceal replication materials (Campbell & Bendavid, 2002;Hong & Walsh, 2009). Such motivations are more prominent as the line between academic and commercially oriented research becomes blurred. Nowadays, commercial firms commonly publish in scientific journals, whereas scientists, universities, and research institutions benefit from the commercialization of research findings and often seek patent protection. All of this cultivates an environment of secrecy, in contrast with the scientific tradition of openness and sharing (Merton, 1942). Intellectual property (IP) law plays a significant role in encouraging this culture of secrecy. Both patent law and trade secret law-main IP regimes-motivate the concealment of certain information to qualify for legal protection. The following paragraphs elaborate on the mechanism by which each regime does so.
Patents are exclusive rights granted for a limited period to inventors in exchange for the disclosure of inventions. One of the conditions for patentability is that the invention be novel when the patent application is filed -that is, it was not publicly disclosed by the applicant or third parties. In addition, the invention must be non-obvious and reflect more than a trivial step over prior art. A patent application that does not meet any of these conditions would be rejected. The unforgiving nature of the novelty and nonobviousness requirements encourages the concealment of technological information by prospective patent applicants.
A computational study may utilize a software invention that is eligible for patent protection. The study may also lead to the development of a patentable product or process, not in the field of software-for example, a computational immunology study that advances the development of a new vaccine. To maximize chances of obtaining patent protection over such inventions, individual scientists and organizations are likely to minimize public disclosure of any information that they are not obligated to share-including replication materials (Hong & Walsh, 2009).
Trade secrets may lead to a similar outcome. Generally speaking, any piece of information that is not widely known, and from which its holder derives a competitive edge while it is kept secret, can be protected as a trade secret. In connection with a computational study, a researcher or affiliated institution may choose to protect as trade secrets certain information that is not included in the scientific publication. All replication materials could qualify for trade secret protection, including the algorithm itself (if not disclosed in the publication), code, database, workflows, details regarding the computational environment, and any other information pertaining to the study's methodology. Once information is protected as a trade secret, the law prohibits its misappropriation. This term encompasses the acquisition, use or disclosure of another's trade secret by improper means, such as theft, or by a person who has confidentiality obligations towards the trade secret holder. Trade secret protection can last infinitely. To enjoy protection, however, the information must remain out of the public domain and its holder must continuously take reasonable measures to keep it confidential.
Given the high societal interest in replications, a natural inclination of policymakers could be to restrict IP rights, to remove incentives to conceal information. In our opinion, though, narrowing IP protection is not imperative to enable replicability. Perhaps, in a utopian world, the scientific enterprise would be free of commercial considerations, which could facilitate information sharing. However, the disposition of individual scientists and research institutions to utilize IP protection for retaining commercial benefits is understandable. Patents also hold an academic value by increasing the reputation of researchers and enhancing funding opportunities for their institutions (Stevens et al, 2011). Therefore, limiting patenting practices might bear a detrimental impact on scientists beyond their commercial interests. This is possibly why, despite widespread awareness of the replication crisis, most journals have not adopted a policy mandating the disclosure of replication materials (Rousi & Laakso, 2020).

Conditional access agreement
Instead of choosing between IP rights and replicability, we suggest an inclusive approach that facilitates replications without depriving scientists of IP rights. Our proposal is to implement a new policy tool: the Conditional Access Agreement (CAA). Recall that it is public access to replication materials that jeopardizes both the prospect of securing patent protection (as novelty and non-obviousness are examined vis-a-vis the public prior art) and trade secret protection (since the pertinent information must be kept out of the public domain). Access, however, does not have to be public. This is precisely the gist of the CAA mechanism-establishing a private, controlled channel of communication for the transfer of replication materials between authors and replicators.
The CAA mechanism would work as follows (Fig 1): When submitting a paper for publication, an author would execute an agreement vis-a-vis the journal, pledging to provide full access to replication materials upon demand. The agreement would specify that anyone requesting access to the materials can only obtain it upon signing a nondisclosure agreement (NDA). Under an NDA, the receiving party commits to use the information disclosed by the other party only for a limited purpose while keeping it confidential. In the context of the CAA, the NDA would prohibit the use of the replication materials delivered by the original authors for any purpose other than replication and restrict their further dissemination. Establishing a mechanism of private access to replication materials facilitates, on the one hand, on-demand replicability, while maintaining, on the other hand, the proprietary potential inherent in the study. This mechanism can also alleviate other concerns that a researcher may have with respect to sharing replication materials, including the fear of being scooped or plagiarism.
Admittedly, the CAA is not a risk-free solution. If replicators breach their contractual obligations under the NDA, the replication materials may become public. However, even in such a scenario, the CAA mechanism offers some relief. In some cases, a patent application may have already been filed by the time the information leaks. Even if this is not the case, the information disclosed by the replicator will not be considered prior art, provided that the patent application is filed within a specified time period of up to 12 months, depending on the jurisdiction (Merges, 2012). Of course, authors can also file a lawsuit for breach of the NDA and obtain compensation for their damages. The CAA policy is feasible thanks to an important intermediary: scientific journals. Journals are the gatekeepers of responsible science. As such, journals are continuously involved in a publication's lifecycle from submission to post-publication. In addition to their substantive discretion regarding publications, journals oversee the procedural aspects of the publication process and monitor actions such as uploading the manuscript, declaring conflicts of interests, and signing a copyright-related agreement. As most scientific publications go through the scientific journals portal, it is a convenient path to implement the CAA policy. Notably, mitigating the replication crisis and promoting openness align with journals' mission of promoting rigorous and credible science. Altogether, the CAA policy can be implemented best with the collaboration of scientific journals.
One of the main advantages of the CAA solution is that it imposes minimal costs on replicators, researchers, and journals alike. Replicators will address authors directly-after signing an NDA-to ask for materials without having to pay any fees for accessing the materials. For authors, the CAA would entail minimum transaction costs, as the paper submission process remains almost the same, and the journal will provide the applicable CAA documents. Costs are also low for journals; they only need to formulate a CAA once and embed it as part of their electronic submission platform.
Our proposed solution can enhance replicability and open science without compromising the ability of researchers and their affiliated institutions to benefit from IP rights. Indeed, the CAA mechanism is not a panacea for the replication crisis, which is caused by various factors, not only IPrelated ones. Yet, we firmly believe that the CAA policy would reduce the portion of publications that cannot be replicated due to restricted access. Both the scientific community and the public at large would benefit from a greater level of replicability.


Interval between prior SARS-CoV-2 infection and booster vaccination impacts magnitude and quality of antibody and B cell responses

SARS-CoV-2 mRNA booster vaccines provide protection from severe disease, eliciting strong immunity that is further boosted by previous infection. However, it is unclear whether these immune responses are affected by the interval between infection and vaccination. Over a 2-month period, we evaluated antibody and B cell responses to a third-dose mRNA vaccine in 66 individuals with different infection histories. Uninfected and post-boost but not previously infected individuals mounted robust ancestral and variant spike-binding and neutralizing antibodies and memory B cells. Spike-specific B cell responses from recent infection (<180 days) were elevated at pre-boost but comparatively less so at 60 days post-boost compared with uninfected individuals, and these differences were linked to baseline frequencies of CD27lo B cells. Day 60 to baseline ratio of BCR signaling measured by phosphorylation of Syk was inversely correlated to days between infection and vaccination. Thus, B cell responses to booster vaccines are impeded by recent infection.

Correspondence
In brief For COVID-19 mRNA vaccines, immunization with a booster dose elicits robust antibody and B cell responses that are further increased if a breakthrough infection occurs after vaccination. In contrast, when infection occurs prior to booster vaccination, antibody and B cell responses are muted closer to the infection time and achieve better levels as the time interval between infection and vaccination increases.

INTRODUCTION
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines provide protection against symptomatic infection through the induction of strong humoral and cellular immunity (Laidlaw and Ellebedy, 2022;Sette and Crotty, 2021). The original two-dose BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine elicits antibodies that are highly effective at neutralizing the ancestral virus (Baden et al., 2021;Polack et al., 2020). More recent studies show booster doses increase potency and breadth of the neutralizing antibody response and the induction of strong memory B cell responses against variants of concern (VOCs) . Boosted immunity as a result of infection and vaccination, commonly referred to as hybrid immunity, is also highly protective against VOCs (Bhattacharya, 2022;Laidlaw and Ellebedy, 2022). In a study designed to delineate the effects of mRNA vaccination and/or previous infection on symptomatic infection and severity of disease from Omicron subvariants BA.1 and BA.2, hybrid immunity resulting from previous infection and three doses of vaccine provided the best protection (Altarawneh et al., 2022).
Hybrid immunity from prior infection can provide both quantitative and qualitative benefits by imprinting effector CD4 + T cell populations with enhanced antiviral properties and improving potency and breadth of B cell and antibody responses (Andreano et al., 2021;Rodda et al., 2022). However, some of these benefits may not extend to booster doses (Rodda et al., 2022), and the effects may be modulated by vaccine and/or infection histories. For example, imprinting from booster vaccination has an attenuating effect on response to Omicron infection while responses to other VOCs are boosted and response to Omicron is severely dampened by prior infection with ancestral but less affected by infections with other VOCs (Reynolds et al., 2022). Added to the increasing complexities associated with effects of infection and re-infection histories are challenges associated with timing of vaccines and how repeated boosting, whether (legend on next page) ll through vaccination or infection, affects the magnitude and durability of protective immunity. Previous findings from primary two-dose mRNA vaccines suggest that an extended interval between doses increases neutralizing antibody and cellular responses (Payne et al., 2021), especially B cell responses (Nicolas et al., 2022). However, as exposures to SARS-CoV-2 increase, whether through vaccination, infection, or both, it is unclear how timing between exposures modulates these responses. The risk of deleterious effects on the immune system from repeated and frequent stimulation with the same antigen is known from animal models where antibody-mediated feedback and other regulatory mechanisms have been described (Mesin et al., 2020;Zhang et al., 2013). The role of pre-existing antibody levels in regulating and restricting B cell responses is also reported in a SARS-CoV-2 mRNA vaccinee plasma transfer model (Dangi et al., 2022).
In this study, we investigate the effects of SARS-CoV-2 infection on antibody and B cell responses to a third dose of BNT162b2 or mRNA-1273 vaccine in a longitudinal cohort of uninfected, previously infected, and post-boost infected subjects. While we find robust spike-specific antibody and B cell responses to the booster vaccine in both uninfected and postboost infected individuals, responses are muted in those who were infected prior to boosting. We present evidence that the interval between prior infection and booster vaccination is a critical determinant of the immune response to the booster vaccine and that B cells of individuals who were recently infected are minimally responsive to the booster vaccine. Our findings thus identify timing relative to infection as a key factor in immune boosting.

RESULTS
Prior SARS-CoV-2 infection restricts post-boost binding and neutralizing antibodies From October 2021 through March 2022, a cohort of 66 adults scheduled to receive a third dose (booster) of either BNT162b2 or mRNA-1273 vaccine was recruited to donate blood at baseline (day 0) and days 30 and 60 post-vaccination. At day 60, participants were stratified into three groups based on exposure to SARS-CoV-2: uninfected (N = 44) by nucleocapsid antibody serology, prior-infected (N = 11), or post-infected (N = 11) depending on whether exposure occurred prior to or after booster vaccination, respectively ( Figure 1A; Tables 1 and S1). In the post-infected group, BA.1 Omicron was the PCR-confirmed or suspected infecting variant while in the prior-infected group, all infections occurred before the first reports of Omicron (Table S1). The interval between vaccination and post-boost infection ranged from 12 to 44 days with a median of 29 days, and the interval between prior infection and vaccination ranged from 59 to 601 days with a median of 160 days (Tables 1 and  S1). Demographics and schedule/source of vaccines did not significantly differ between the groups, except that age was slightly lower in the post-infected group compared with uninfected group, but not prior-infected group (Table 1; Figure S1A).
Serum immunoglobulin G (IgG) antibodies were measured against spike proteins of ancestral B.1 (D614G mutation) and nine variants, including VOC B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta; AY.4 lineage), and B.1.1.529 (Omicron; BA.1, BA.2, and BA.3 lineages) using a multiplex platform. Antibody titers increased from baseline to day 30 in all three groups, albeit more modestly for the prior-infected group ( Figure S1B). By day 60, titers had plateaued in the uninfected group, increased slightly in the post-infected group against B.1 D614G and three variants, yet decreased in the prior-infected group against B.1 D614G and six variants ( Figure S1B; Table S2). These temporal differences between groups, evaluated in a mixed-effects model considering time and exposure status, were highly significant for all three groups combined (Figures 1B and S1C) and between prior-infected and both uninfected and post-infected (p < 0.0001 for each of the 10 spike measurements) but not between uninfected and post-infected groups (Table S3 showing all comparisons). At baseline, titers against all 10 spike proteins were higher in the prior-infected than uninfected and post-infected groups, while day 60 titers were lower in the prior-infected than post-infected and for five variants in the uninfected group ( Figure S1D; Table S2). Titers were similar between the uninfected and postinfected group at baseline and marginally higher at day 60 in the post-infected group ( Figure S1D; Table S2). The combination of high baseline titers with modest increases post-boost in the prior-infected group translated into fold increases between baseline and day 60 that were much lower compared with those of the uninfected and post-infected groups ( Figure 1C; Table S2). As expected, the fold increases were modestly higher in the post-infected group compared with the uninfected group (Figure 1C; Table S2).
Serum neutralizing antibody activities were measured against B.1 D614G, B.1.351, B.1.617.2, and BA.1 using a pseudovirusbased assay (Gagne et al., 2022). Consistent with the antibody-binding titers, neutralization increased from baseline to day 60 in the uninfected and post-infected groups, whereas in contrast to antibody binding, neutralizing titers did not increase between baseline and day 60 in the prior-infection group (Figure S2A). These temporal differences, evaluated with the mixed-effects model described above, were highly significant for all three groups combined ( Figure 1D), as well as between each group (p < 0.0001 for the three group comparisons and (E) Fold-change day 60 over baseline in serum neutralizing titers against SARS-CoV-2 lentiviral pseudovirus. Horizontal lines represent geometrical means and coloring scheme matches groups described in (A). Statistical analyses by mixed-effects model (B and D) or Kruskal-Wallis followed by pairwise Mann-Whitney tests (C and E). *p % 0.05, **p % 0.01, ***p % 0.001, and ****p < 0.0001. See also Figures S1 and S2 and Tables S2 and S3.  for each pseudovirus). Baseline neutralizing titers were higher in the prior-infected group compared with the uninfected and postinfected groups, while day 60 titers were higher in the post-infected compared with the other two groups ( Figure S2B). These differences translated into higher fold increases between baseline and day 60 in the post-infected group compared with the two other groups and higher in the uninfected than prior-infected group ( Figure 1E), consistent with the antibody-binding data. Overall, the kinetics and magnitude of antibody-binding and neutralizing titers differed between the groups, although most strikingly in the prior-infected group where antibody responses to the booster vaccine were muted.

Prior SARS-CoV-2 infection restricts post-boost SARS-CoV-2 B cell responses
We used spectral flow cytometry to evaluate B cell responses to the booster vaccine with a 21-marker panel that included six tet-ramers for evaluating responses against the spike protein subunit 1 (S1) of B.1, its receptor-binding domain (RBD) and N-terminal domain (NTD), as well as identifying B cells that bound RBD and NTD of both B.1 and BA.1 (Figures 2A and S3A). Frequencies of B.1 RBD, NTD, as well as dual B.1/BA.1 RBD and NTD-binding B cells increased significantly from baseline to day 60 in the uninfected and post-infected groups, peaking at day 30 in the uninfected group while continuing to increase (RBD) in the post-infected group ( Figure S3B). In contrast, frequencies of spike-binding B cells in the prior-infected group did not significantly increase between baseline and day 60 and declined between days 30 and 60 against B.1 RBD and NTD and B.1/BA.1 NTD ( Figure S3B). These temporal differences between groups, evaluated with the mixed-effects model described above, were highly significant for all three groups combined ( Figure 2B), as well as between group pairs (Table S4 showing all comparisons). As with antibody titers, baseline frequencies of spike-binding B cells were higher in the prior-infected than uninfected and post-infected groups for B.1 RBD and NTD and B.1/BA.1 RBD ( Figure S3C). At day 60, spike-binding frequencies were similar between the prior-infected and uninfected group yet were lower for B.1 RBD and B.1/BA.1 RBD in both groups when compared with the post-infected group ( Figure S3C). These temporal differences translated into higher fold increases between baseline and day 60 in the post-infected group compared with the two other groups and higher in the uninfected than prior-infected group for both RBD and NTD and dual counterparts ( Figure 2C). We then considered whether post-boost BA.1 infection and/or the booster vaccine altered the ratio of dual B.1/BA.1 to single B.1 RBD or NTD binding. For RBD, the ratio of dual B.1/BA.1 to single B.1 binding increased from baseline to day 60 in all three groups; however, the ratio was highest in the post-infected group and lowest in the prior-infected group ( Figure 2D). For NTD, the ratios increased from baseline to day 60 in the uninfected and post-infected but not prior-infected group and at day 60, and the ratios at day 60 were similarly higher in the uninfected and post-infected than in the prior-infected group (Figure S3D). Overall, the B cell response to the booster vaccine was poor in magnitude and breadth in the prior-infected group, whereas the response is robust in the uninfected and post-infected groups, with the latter having an additional boost from the breakthrough infection.  Binding frequencies represent percentage of total CD19 + B cells; see also Figure S3A for gating strategy. Horizontal lines represent geometrical means and coloring scheme matches groups described in Figure 1A. Statistical analyses by mixed-effects model (B), Wilcoxon signed rank test (D, baseline versus day 60), or Kruskal-Wallis followed by pairwise Mann-Whitney tests (C and D, right panel). *p % 0.05, **p % 0.01, ***p % 0.001, and ****p < 0.0001. See also Figure S3 and Table S4.    Article performed in vitro culturing of peripheral blood mononuclear cells (PBMCs) at baseline and day 60 using polyclonal stimulation with TLR7/8 agonist R848 and IL-2, known to favor memory B cell differentiation and antibody secretion (Goel et al., 2021). We measured culture-derived antibodies with the same multiplex platform used for serum IgG titers, although relative activity levels were typically 2 to 3 logs lower in cultures than in serum and reported per total secreted IgG ( Figure 3A). Consistent with the spike-binding B cell kinetics, spike-specific antibodies secreted from cultured PBMCs increased between baseline and day 60 in the uninfected and post-infected but not prior-infected group ( Figure S4A; Table S5). These temporal differences, evaluated with the mixed-effects model described above, were highly significant for all three groups combined ( Figures 3A and S4B), between prior-infected and both uninfected and post-infected groups (p < 0.0001 for each of the 10 spike measurements), as well as between the uninfected and post-infected group, albeit somewhat less pronounced (Table S6 showing all comparisons). Baseline secreted antibodies were modestly higher in the priorinfected group compared with uninfected and/or post-infected group against the five non-Omicron spike proteins whereas there were no baseline differences against the five Omicron variants tested ( Figure S4C; Table S5), the lack of measurable differences possibly due to low baseline Omicron-specific antibodies. However, by day 60, secreted antibody titers were lower in the priorinfected compared with both uninfected and post-infected group, and lower in the uninfected compared with the post-infected group ( Figure S4C; Table S5). As with serum antibodies and spike-binding B cells, these temporal differences translated into higher fold increases of secreted antibodies between baseline and day 60 in the post-infected group compared with the two other groups and higher in the uninfected than prior-infected group against all 10 spike proteins ( Figure 3B; Table S5). Overall, spike-specific antibodies secreted from memory B cells did not increase in the prior-infected group after the vaccine boost, and while levels compared with both uninfected and post-infected groups were similar or modestly higher at baseline, they were sharply lower by day 60.

Enhanced breadth of antibodies secreted from memory B cells
The robust secreted antibody response at day 60 in the large uninfected group provided an opportunity to consider whether antibodies derived from memory B cells had greater breadth compared with serum antibodies, as suggested in previous studies (Muecksch et al., 2022;Purtha et al., 2011). Accordingly, we compared ratios of B.1 D614G to variant antibody titers between the two sources. For seven of the nine spike variants tested, the fold difference was significantly reduced for the culture-secreted antibodies compared with those in serum while differences were not significant for two variants ( Figure 3C), confirming the superior breadth in memory B cell compartment.

Prior SARS-CoV-2 infection alters B cell responses and phenotypes
The fold changes in memory B cell responses to booster vaccination were significantly lower in the prior-infected group compared with the two other groups, whether measured by direct binding (Figure 2) or secreted antibodies ( Figure 3). However, at day 60, titers of secreted spike-binding antibodies were significantly lower in the prior-infected compared with the uninfected group ( Figure S4C), while frequencies of spike-binding B cells were similar ( Figure S3C). This contrast suggested that prior infection may have led to a reduced B cell responsiveness to stimulation after vaccination. We also observed a dichotomous pattern of antibody titers and B cell responses within the prior-infected group (clearly discernable in Figures 1B, 1D, and S1C) that may have been driven by the wide range of intervals between infection and vaccination (59-601 days: Tables 1 and  S1). This led us to consider whether the interval between infection and vaccination may have contributed to the differences in antibody and B cell responses observed within this group.
Notably, in individuals whose infection occurred more than 180 days prior to receiving their booster vaccine, secreted spike-binding antibodies increased between baseline and day 60 ( Figure S5A), as observed for most individuals in the uninfected and all in the post-infected group ( Figure S4A). In contrast, all decreases in secreted antibodies between baseline and day 60 were among the six individuals whose breakthrough infection occurred less than 180 days from vaccination, henceforth referred to as a recent infection ( Figure S5A).
To determine whether the reduced antibody secretion in the prior-infected group reflected lower B cell proliferation and differentiation in vitro, we cultured carboxyfluorescein succinimidyl ester (CFSE)-labeled baseline and day 60 PBMCs of the six individuals whose prior infection was <180 days from vaccination and of six uninfected individuals matched for demographics and baseline B cell spike-binding frequencies. After 4 days in culture under the same conditions used to induce antibody secretion, the cells were stained with RBD and S1 tetramers and CFSE dilution was measured among spike-positive (RBD + S1 + ) and spikenegative (RBD À S1 À ) IgG-expressing B cells (Figures 4A and S5B). The division index, a measure of cell division among both proliferating and non-proliferating cells, did not significantly differ at baseline and day 60 within and between the two groups for both RBD + S1 + and RBD À S1 À B cells, although there was a clear downward trend among RBD + S1 + B cells of five of the six individuals in the prior-infected group ( Figure 4B). This trend translated into a ratio of day 60 to baseline indices for RBD + S1 + B cells that was significantly lower in the prior-infected compared with the uninfected group, while ratios among RBD À S1 À B cells remained close to 1.0 for both groups and for RBD + S1 + B cells of the uninfected group ( Figure 4C). Notably, the individual in the prior-infected group with an RBD + S1 + B cell ratio above 1.0 in Figure 4C (denoted by gray circles) was one of the two, and the higher of the Culture supernatant titers represent spike-binding IgG per total IgG. Horizontal lines represent geometrical means and coloring scheme matches groups described in Figure 1A. Statistical analyses by mixed-effects model (A), Kruskal-Wallis followed by pairwise Mann-Whitney tests (B), or Wilcoxon signed rank test (C). **p % 0.01, ***p % 0.001, and ****p < 0.0001. See also Figure S4 and Tables S5 and S6. Article two in the subgroup, whose secreted spike-binding antibodies increased between baseline and day 60 ( Figure S5A). We then considered whether the relative reduction in responsiveness of cultured spike-specific B cells in the prior infection subgroup was intrinsic to the B cells. While there are many assays to assess B cell responsiveness, including intracellular calcium flux and signaling responses following B cell receptor (BCR) stimulation, only the latter can adequately capture rare antigenspecific events within the confines of cells available in clinical trials and the narrow timeframe needed to detect bound antigen. Accordingly, we stimulated the B cells with anti-BCR and measured phosphorylation of tyrosine kinase Syk and phospholipase Cg2 (PLCg2), which lies downstream of Syk (Packard and Cambier, 2013), and both have been shown to undergo rapid and strong induction following antigen binding and BCR aggregation (Glass et al., 2020). The phosphorylation of Syk and PLCg2 was clearly discernable among the RBD-binding B cells following anti-BCR treatment ( Figure 5A). In the uninfected group, BCR signaling led to similar levels of phosphorylated (p-) Syk, p-PLCg2 and combination of p-Syk/p-PLCg2 RBD + B cells at baseline and day 60, while in the prior-infected group, levels for p-Syk and p-Syk/p-PLCg2 decreased between baseline and day 60, and baseline levels were higher than in the uninfected group for all three measurements ( Figure 5B). Among RBD À B cells, there was a downward trend in the prior-infected group between baseline and day 60 that was significant for p-PLCg2, although there were no differences between the groups ( Figure S6A). These temporal dynamics translated into ratios of day 60 to baseline p-Syk and p-Syk/p-PLCg2 that were close to 1.0 among RBD À and RBD + B cells of the uninfected group while they were below 1.0 among RBD + , and to a lesser extent, RBD À B cells of the prior-infected group and significantly lower than those of the uninfected group ( Figure 5C). For p-PLCg2, ratios were close to 1.0 and not significantly different between the groups ( Figure S6B). Furthermore, within the prior-infection group, the interval between infection and vaccination was strongly correlated with the p-Syk ratio in that the shorter the interval the lower the induction of p-Syk at day 60 relative to baseline ( Figure 5D).
Elevated basal p-Syk and reduced induction following BCR stimulation are hallmarks of certain chronic conditions such as autoimmunity and persisting pathogens, such as HIV, and are generally associated with distinct B cell populations, many of which express reduced levels of CD21 (Schrezenmeier et al., 2019). Activation of B cells in response to SARS-CoV-2 infection and vaccination is also accompanied by a reduction in the expression of CD21 on B cells (Kardava et al., 2022;Rodda et al., 2022;Woodruff et al., 2020). Antigen-experienced B cells can also express variable levels of CD27 (Baumgarth, 2021). Both markers were differentially modulated by prior infection on RBD + B cells ( Figure 6A). At baseline, the two groups had similarly low frequencies of CD21 lo B cells while the uninfected group had higher frequencies of CD21 + CD27 lo B cells (Figure 6B). Between baseline and day 60, frequencies of CD21 lo B cells increased in both groups but the increase was greater in the uninfected group ( Figure 6B). In contrast, frequencies of CD21 + CD27 lo B cells decreased between baseline and day 60 in the uninfected but not prior-infected group and frequencies were similar at day 60 in the two groups. Finally, we considered whether CD27 lo among CD21 + B cells were less responsive to BCR stimulation than their CD27 + counterparts, and possibly explaining the lower baseline p-Syk and p-PLCg2 induction in the uninfected compared with the prior-infected group ( Figure 5B). At baseline in the uninfected group, levels of BCR-induced p-Syk among RBD + B cells were lower for the CD27 lo compared with the CD27 + fraction ( Figure 6C). Collectively, these findings suggest that prior to booster vaccination, spike-specific B cells of recently infected individuals are more poised to respond to stimulation than those of uninfected individuals yet are refractory to further stimulation following vaccination.

DISCUSSION
Booster vaccines against SARS-CoV-2 play an essential role in protecting against severe COVID-19 (Bhattacharya, 2022). Booster mRNA vaccines induce potent antibody and memory B cell responses , although both are modulated by infection histories (Reynolds et al., 2022). In this study, we investigated the effect of SARS-CoV-2 infection on antibody and B cell responses to mRNA booster vaccines in a longitudinal cohort that included subjects who remained uninfected throughout the study period, as well as subjects who were infected prior to study and subjects who experienced a breakthrough infection during the study period. Collectively, our data identified prior infection, and more specifically, recent prior infection with SARS-CoV-2 to be associated with a diminished response to the booster vaccine when compared with uninfected and post-boost infected subjects. The diminished response in the prior-infected group was most notable for the fact that neutralizing antibodies and B cell responses to the vaccine boost did not increase between baseline and study endpoint (day 60). Antibody and B cell responses tended to be higher at baseline in the prior-infected group compared with the two other groups, consistent with the effects of hybrid (A) Representative baseline and day 60 CFSE-dilution plots of RBD + S1 + and RBD À S1 À IgG-expressing B cells among stimulated PBMCs. Numbers represent percentages. (B) Division indices of RBD + S1 + and RBD À S1 À IgG-expressing B cells. (C) Ratio of day 60 to baseline division indices of RBD + S1 + and RBD À S1 À IgG-expressing B cells. CFSE-labeled PBMCs were stimulated for 4 days then stained with anti-IgG, B.1 S1, and RBD tetramers and other markers shown with gating strategy in Figure S5B. Division indices were measured for RBD + S1 + and RBD À S1 À IgG-gated B cells. Horizontal lines represent medians and coloring scheme matches groups described in Figure 1A. Gray circles refer to subject 00065 R in Table S1 and Figure S5A. Statistical analyses by Wilcoxon signed rank (B) or Mann-Whitney test (C). *p % 0.05. See also Figure S5. (legend on next page) ll immunity (Reynolds et al., 2022;Rodda et al., 2022); however, they were similar to or lower than the other two groups at endpoint. Given that vaccine-specific antibody titers and B cell frequencies were generally similar between the uninfected and prior-infected group, our data do not suggest diminished immunity in prior-infected individuals but rather that the booster PBMCs were stained with B.1 RBD tetramer, IgD, and CD27 for gating on non-naive B cells and other markers (see Figure S3A) and incubated at 37 C for 2 min with or without anti-BCR, then stained for phosphorylated markers shown. Horizontal lines represent medians and coloring scheme matches groups described in Figure 1A. Gray circles refer to subject 00065 R in Table S1 and Figure S5A. Statistical analyses by Wilcoxon signed rank test (B for baseline versus day 60), Mann-Whitney test (B for uninfected versus prior-infected and C), or Spearman rank order correlation (D). *p % 0.05, **p % 0.01. See also Figure S6. Populations shown in (A) and frequencies measured in (B) were from the CD19 + gate shown in Figure S3A and those in (C) further gated on CD21 + B cells.
Horizontal lines represent medians and coloring scheme matches groups described in Figure 1A.
Gray circles refer to subject 00065 R in Table S1 and Figure S5A. Statistical analyses by Wilcoxon signed rank (B for baseline versus day 60 and C) or Mann-Whitney test (B for uninfected versus prior-infected). *p % 0.05, **p % 0.01.
vaccine was ineffective when administered after a recent infection. We found strong concordance between the antibody and B cell responses after boosting, both in terms of kinetics, response to variants, and differences in responses between the groups. The patterns of fold-change between baseline and day 60 were almost identical from one assay to another, whether serum binding or neutralizing antibody titers or frequencies of spike-specific B cells and their secreted antibodies were measured: the post-infected group experienced the largest foldchange, as might be expected, while the prior-infected group had the lowest foldchange. The temporal dynamics were also similar across the different assays, and these were clearly influenced by infection status, although differences were more pronounced between prior-infected and both uninfected and post-infected groups than between the latter two groups.
Despite the many similarities between the assays, there were notable distinctions. Serum binding antibodies in the prior-infected group did increase after boosting and remained higher at day 60 than baseline, in contrast to all other measurements ll Cell 185, 4333-4346, November 10, 2022 4343 Article of antibody and B cell activity that were unchanged between the two time points. It is unclear why the increase was limited to binding antibodies, but this may be related to the induction of short-lived plasmablasts that are more likely to produce binding but not more matured neutralizing antibodies (Bhattacharya, 2022). It is noteworthy that binding antibody titers against ancestral and most variants declined between day 30 and 60 in the prior-infected group, but not the uninfected or post-infected groups, consistent with a short-lived plasmablast-mediated rise in the prior-infected group. The temporal dynamics were also different between spike-binding antibodies, which were stable or increased from day 30 to day 60 in groups without prior infection, and spike-binding B cells, which declined during this period in the uninfected group and marginally increased against RBD in the post-infected group. Similar findings have been reported by others and in our previous study Kardava et al., 2022), likely reflecting peak B cell activation that occurs between 2 and 4 weeks after a second or third dose of vaccine.
Booster vaccination and infection also differentially impacted immune responses to viral variants. As expected, responses to the BA.1 variant in the post-infected group, all of whom were confirmed or suspected to have been infected with BA.1, were strong. However, except for neutralizing antibody titers, the increase in response to the BA.1 spike in the BA.1-infected group was similar in magnitude to responses to ancestral and the other variants spike antigens. The somewhat modest response to BA.1 may be explained by several factors, including vaccinal imprinting (Reynolds et al., 2022), the relatively mild clinical nature of BA.1 (Sigal, 2022), and the proximity of day 60 to the breakthrough infection, a median of 29 days with a range of 12-44 days. Beyond the effect of BA.1 infection on the overall immune response to a range of variants, we noted that antibodies secreted from cultured PBMCs, which most likely originated from memory B cells, had greater breadth compared with serum antibodies, which most likely originated from lymphoid tissue resident plasma cells. These data are consistent with previous animal and more recent COVID-19 vaccine studies (Muecksch et al., 2022;Purtha et al., 2011), highlighting the critical role of the cellular immune response in a pandemic driven by rapidly evolving variants. We also noted that while both uninfected and post-infected groups increased their post-boost ratio of B cells that bound both B.1 and BA.1 RBD and NTD relative to B.1 alone, the increase was restricted to RBD in the prior-infected group, and the ratio at day 60 was significantly lower compared with the other two groups, suggesting a more limited cellular immune response to certain variants.
While all fold changes between day 60 and baseline revealed a muted post-boost response in the prior-infected group, we were struck by the dichotomous nature of responses within this group and the significantly lower day 60 secreted spike-binding antibody titers despite similar spike-binding B cell numbers when compared with the uninfected group. We determined that the dichotomous response was largely driven by the interval between infection and booster vaccination, where those who were most recently infected had the highest spike-specific baseline antibody titers and B cell frequencies, yet the weakest postboost responses. Given the relatively lower secretion of spike-binding antibodies from in vitro stimulated B cells compared with frequencies of spike-binding B cells, we initially considered that recent infection may be causing functional non-responsiveness or post-activation anergy (Schrezenmeier et al., 2019). However, taken collectively, our data suggest a more nuanced effect of recent infection on response to booster vaccination. While spike-specific B cells of recently infected individuals clearly underwent fewer in vitro cell divisions at day 60 compared with baseline with a corresponding decrease in BCR signaling, cell division indices and levels of anti-BCR-induced phosphorylation were not significantly different at day 60 between the six recently infected and six uninfected individuals. The main differences between these two groups were at baseline, where signaling responses to BCR stimulation were stronger in the recently infected group. We might have expected CD21 lo B cells to be responsible for the differences in BCR signaling, given their elevated state of activation/signaling in chronic conditions (Jenks et al., 2018;Kardava et al., 2018), and following vaccination/infection, including in patients with COVID-19 (Castleman et al., 2022;Kardava et al., 2022;Rodda et al., 2022;Woodruff et al., 2020). However, frequencies of CD21 lo B cells among spike-specific B cells were found to be similarly low in both groups at baseline and increased at day 60 post-boost in uninfected but not recently infected subjects, consistent with the muted response of the latter group to vaccination. Instead, we found a higher frequency of CD27 lo among spike-specific CD21 + B cells in the uninfected compared with recently infected group and a corresponding lower BCR signaling response among CD27 lo compared with CD27 + B cells. Resting CD21 + CD27 lo B cells have been shown to be more durable than other memory B cell populations following influenza vaccination (Andrews et al., 2019) and were found to increase over time after two doses of the BNT162b2 mRNA vaccine (Kotaki et al., 2022). We speculate that despite being in a resting state, CD27 lo B cells are better poised than other memory B cells to respond robustly upon re-encounter with antigen. For the poor responders among the prior-infected group, their infection induced a robust response, as evidenced by the high baseline titers and responding B cells; however, the interval between infection and vaccination was likely insufficient for their B cells to return to an optimal resting state and respond efficiently to the booster.
In summary, we have shown that antibody and B cell responses to SARS-CoV-2 booster vaccination are impacted by infection status, where prior SARS-CoV-2 infection is associated with a muted response, the extent of which is dictated by the interval between infection and vaccination. When the interval is too short, the response induced by the recent infection appears to prevent B cells from responding to the subsequent booster vaccine. As a growing number of people are infected and re-infected with SARS-CoV-2, these findings may help provide guidance for future recommendations on how to establish booster vaccine schedules that account for infection histories.

Limitations of the study
There are several limitations to our study, most notably the small number of subjects in the prior-infected and post-infected groups and the limited number of individuals in the former group ll whose booster vaccine was administered within 180 days of infection. These are inevitable limitations of human studies, yet future studies will be needed to confirm and extend our findings and to determine whether additional ancestral and/or variant boosters will lead to similar outcomes in the absence or presence of breakthrough infections. Our study did not consider other likely contributors to outcomes, including the effect of T cells, both in vivo and in vitro, where they-along with other lineages and corresponding soluble factors-likely modulated B cell division, differentiation, and antibody secretion. We also did not directly address the possibility that the prior-infected group expanded memory B cell responses to the S2 subunit of the spike protein following booster vaccination. Finally, our study did not consider Omicron subvariants BA.4/5, which have been widely circulating since the spring of 2022 and were included in the fall 2022 bivalent mRNA vaccines produced by Pfizer-Biontech and Moderna. Current studies are ongoing to evaluate immune responses to BA.4/5 infection and/or vaccination.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:

DECLARATION OF INTERESTS
The authors declare no competing interests. Article Data and code availability All data reported in this paper will be shared by the lead contact upon request. The original code generated in this study is available at https://doi.org/10.5281/zenodo.7097099. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Ethics statement
Participants were enrolled in the Vaccine Responses to SARS-CoV-2 and Other Emerging Infectious Diseases (COVAC) study (ClinicalTrials.gov identifier NCT05078905) at the NIH Clinical Center. The protocol was approved by the NIH Institutional Review Board and written informed consent was obtained from all study subjects.
Human study subject recruitment, visits, and clinical data Sixty-six adults who were scheduled to receive their third dose of the Pfizer-BioNTech (BNT162b2) or Moderna (mRNA-1273) mRNA vaccine were recruited to the COVAC study. Blood samples were collected at baseline (day 0) prior to (either on the day of or the day before) vaccination, and on day 30 and day 60 post-vaccination. Eleven subjects had COVID-19 prior to and another eleven after receiving their booster vaccine. Details of demographics, dates of infection, methods of diagnosis, exact visit timepoints, and dates and type of vaccine for the three doses are provided in Table S1 and summarized in Table 1. Forty-four subjects remained uninfected throughout the study period, verified by serum nucleocapsid antibody testing by the Bio-Rad Platelia and a second in-house assay described below.

METHOD DETAILS
Blood sample collection, processing, and storage PBMCs were isolated by Ficoll density gradient centrifugation from whole blood collected in ethylenediaminetetraacetic acid Vacutainer tubes. Cells were frozen at -80 o C and stored in liquid nitrogen. Serum was isolated by centrifugation of clotted whole blood collected in serum separation transport Vacutainer tubes and stored at -80 o C.

In vitro differentiation of memory B cells into antibody-secreting cells
PBMCs were thawed, seeded at 1x10 6 per ml in complete media (RPMI containing 25mM Hepes and L-glutamine + 10% FCS +1% Pen/Strep) and stimulated with 1000 U/ml recombinant human IL-2 (StemCell Technologies) and 2.5 mg/ml R848 (Invivogen) for 7 days at 37 o C. On day 4, half the media was removed and replaced with fresh media containing 1X IL-2/R848. Day 7 culture supernatants were collected, spun down to remove cellular debris, assayed for total IgG concentration, and stored at -80 o C. Total IgG was measured by cytometric bead array (CBA) assay (BD Biosciences) using a FACS Lyric instrument (BD Biosciences), according to the manufacturer's instructions. Data were analyzed using BD FACSuite software v1.2.1.5657 (BD Biosciences).

Serum and culture supernatant antibody titers
Serum samples were heat-inactivated at 56 o C for 60 minutes. A 10-plex IgG spike-binding assay was performed on serum and culture supernatant samples with an electrochemiluminescence immunoassay analyzer (ECLIA) developed by Meso Scale Discovery (MSD), as previously described (Kardava et al., 2022), with the following modification. The assays were performed using the MSD V-PLEX SARS-CoV-2 panel 25 kit. The in-house serum nucleocapsid antibody testing was performed using an enzyme-linked immunosorbent assay as previously described (Kalish et al., 2021), with the following adjustments. Nucleocapsid proteins were synthesized as described (Esposito et al., 2020;Mehalko et al., 2021), and longitudinal quality control and assay stability was ensured by the inclusion of recombinant human antibodies on each plate (anti-nucleocapsid IgG from ThermoFisher).

Lentiviral pseudovirus neutralization
Heat-inactivated serum samples were assayed for SARS-CoV-2 neutralization with a lentiviral pseudovirus-based assay and reported as reciprocal 50% inhibitory dilution (ID 50 ) with a value of 20 plotted for samples that did not neutralize, as previously described (Gagne et al., 2022).

Biotinylated NTD proteins
The NTD subunit of the spike protein of WA-1 strain (referred to as B.1 in this study) and BA.1 variant were cloned, expressed and biotinylated as previously described .

Proliferation assay
PBMCs resuspended in PBS were labeled with 0.5 mM CFSE (CellTrace CFSE cell proliferation kit, ThermoFisher) at room temperature for 8 minutes, followed by addition of RPMI 1640-10% FBS and extensive washing as described (Kardava et al., 2018). The cells were then cultured with 2.5 mg/ml R848 and 1000 U/ml recombinant human IL-2 for 4 days at 1 X 10 6 cells per well of a 48-well flatbottom plate. The cells were collected and stained with mAbs against CD19, CD20, CD3, CD27, CD21, IgD, CD38, and fluorochromeconjugated spike proteins RBD-B.1 and S1-B.1, fixed (Lysing Solution, BD Biosciences), permeabilized (Permeabilizing Solution 2; BD Biosciences) and stained with mAbs against IgG, IgA, IgM. The cells were acquired on an Aurora cytometer and the effect of stimulation on cell division was evaluated by analysis using FlowJo v10. The division index, a measure of the overall proliferative response, is the average number of divisions undergone per cell in the total population, including cells that have not undergone division.

QUANTIFICATION AND STATISTICAL ANALYSIS
Two different sets of analyses were performed. The first set consisted of non-parametric comparisons: testing between groups and between timepoints within groups were performed by two-tailed non-parametric Mann-Whitney or Wilcoxon signed rank test respectively using Prism 9.4 (GraphPad) software. Comparisons between three timepoints were first performed by the Friedman test and if significant, pairwise Wilcoxon signed rank testing was then performed. Comparisons between the three groups were first performed by the Kruskal-Wallis test and if significant, pairwise Mann-Whitney testing was then performed. Associations between continuous variables were evaluated by Spearman rank order correlation. Comparisons between the three groups for gender and vaccine source were performed by the Fisher's exact test. No adjustments for multiple comparisons were made for these analyses. Only significant P values (<0.05) are shown. Unless otherwise stated, the N for all data presented were 44 in the uninfected group and 11 each in the prior-infected and post-infected groups.
The second set consisted of parametric analyses using a linear mixed model regression applied to the base 10 logarithm of each outcome. The mixed model included fixed effects for group (uninfected, prior-infected, and post-infected), days (treated as a factor variable), and a group by time interaction, and allowed participants to have different intercepts. The fixed effects component of the model is specified by Here, y is the log-transformed value of the outcome variable for a given subject and EðyÞ is the mean value in the given group and day, Iðgroup = prior infectedÞ and Iðgroup = post infectedÞ are indicator variables that the subject was prior-infected and that the subject was post-infected, Iðday = 30Þ and Iðday = 60Þ are indicators of day 30 and day 60, and the other terms are products of group and day indicators representing group by day interactions. In this parametrization, b 0 is the mean in the reference group (uninfected) on day 0. The random effects component of the model included a subject-specific deviation from the average value in the corresponding group and day. We first simultaneously tested the group by time interaction terms to assess whether the patterns over time differed across groups. If there was a significant group by time interaction, we made pairwise comparisons by eliminating a group or day and re-running the above model. For example, for pairwise comparisons of days 0 and 60, we eliminated day 30 and re-ran the model; for comparisons of uninfected with post-infected, we eliminated the prior-infected group and re-ran the above ll e4 Cell 185, 4333-4346.e1-e5, November 10, 2022 model. The mixed model approaches adjusted for the number of variants within a given data type (e.g., flow cytometry) using the Holm sequentially-rejective method. Holm's method with 10 variants multiplies the smallest p-value by 10. Provided that the result is less than 0.05, the second smallest p-value is multiplied by 9. Provided that that result is less than 0.05, the third smallest p-value is multiplied by 8, and so on. It should be noted that, although we formally adjusted only for the number of variants, we effectively incorporated more multiple comparison adjustment by virtue of performing stratified analyses by days and groups only if the group by time interaction was statistically significant. The purpose of those further analyses was to understand the different patterns over time in different groups that were established from the statistically significant group by time interactions.  Figure S1. Demographics and serum antibody response to booster vaccination, related to Figure 1 (A) Comparison between the groups for gender, vaccine, age, and interval between dose 2 and booster vaccination; see Table S1 for details. (B) Serum IgG spike-binding titers in Figure 1B shown by group.
(C) Serum IgG spike-binding titers for additional variants, expressed as in Figure 1B.
(D) Comparison between the groups at baseline and day 60 of serum IgG spike-binding titers in Figure 1B. Horizontal lines represent geometrical means and coloring scheme matches groups described in Figure 1A. Statistical analyses by Fisher's exact test (A: two left graphs), Kruskal-Wallis followed by pairwise Mann-Whitney tests (A: two right graphs and D), Friedman followed by pairwise Wilcoxon signed rank tests (B), mixed-effects model (C) or by Wilcoxon signed rank test (E). *p % 0.05, **p % 0.01, ***p % 0.001, and ****p < 0.0001.   Figure 1D shown by group.
(B) Comparison between the groups at baseline and day 60 of serum neutralizing titers in Figure 1D. Horizontal lines represent geometrical means and coloring scheme matches groups described in Figure 1A. Statistical analyses by Wilcoxon signed rank test (A) or by Kruskal-Wallis followed by pairwise Mann-Whitney tests (B). *p % 0.05, **p % 0.01, ***p % 0.001, and ****p < 0.0001.   Figure 2B shown by group. (C) Comparison between the groups at baseline and day 60 of frequencies of B.1 and BA.1 RBD and NTD tetramer-binding B cells in Figure 2B. (D) As in Figure 2D but for NTD. Horizontal lines represent geometrical means and coloring scheme matches groups described in Figure 1A. Statistical analyses by Friedman followed by pairwise Wilcoxon signed rank tests (B), Kruskal-Wallis followed by pairwise Mann-Whitney tests (C, D right graph) or by Wilcoxon signed rank test (D for baseline versus day 60). *p % 0.05, **p % 0.01, ***p % 0.001, and ****p < 0.0001.   Figure 3A shown by group. (B) Secreted IgG spike-binding titers for additional variants, expressed as in Figure 3A.
(C) Comparison between the groups at baseline and day 60 of secreted IgG spike-binding titers in Figure 3A.
Horizontal lines represent geometrical means and coloring scheme matches groups described in Figure 1A. Statistical analyses by Wilcoxon signed rank test (A), mixed-effects model (B) or by Kruskal-Wallis followed by pairwise Mann-Whitney tests (C). *p % 0.05, **p % 0.01, ***p % 0.001, and ****p < 0.0001.  Figure S5. In vitro proliferation studies, related to Figure 4 (A) Secreted antibodies in Figure S4B for the prior-infected group against B.1 D614G spike shown color-coordinated by time of infection relative to booster vaccination. PBMCs of the six individuals with interval < 180 days were stimulated to evaluate cell division by CFSE dilution.  Figure S6. In vitro BCR signaling studies, related to Figure 5 (A) As in Figure 5B but for RBD À B cells.
(B) As in Figure 5C but for single PLCg2. Horizontal lines represent medians and coloring scheme matches groups described in Figure 1A. Gray circles refer to subject 00065 R in Table S1 and Figure S5A. Statistical analyses by Wilcoxon signed rank test (A, for baseline versus day 60), Mann-Whitney test (A for uninfected versus prior-infected, B). *p % 0.05.

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Article


s2fieldsofstudy: Biology Medicine

text:
Erythrophagocytosis by Microglia/Macrophage in Intracerebral Hemorrhage: From Mechanisms to Translation

Intracerebral hemorrhage (ICH) is a devastating condition characterized by hematoma related mass effect. Microglia/macrophage (M φ) are rapidly recruited in order to remove the red blood cells through erythrophagocytosis. Efficient erythrophagocytosis can detoxify hemolytic products and facilitate neurological recovery after ICH. The underlying mechanisms include modulation of inflammatory response and oxidative stress, among others. It is a dynamic process mediated by a cascade of signal transduction, including “find-me” signals, “eat-me” signals and a set of phagocytotic receptors-ligand pairs that may be exploited as therapeutic targets. This review summarizes mechanistic signaling pathways of erythrophagocytosis and highlights the potential of harnessing M φ-mediated phagocytosis for ICH treatment.

INTRODUCTION
In intracerebral hemorrhage (ICH), the rupture of brain vessels results in the accumulation of millions of red blood cells (RBCs) within brain parenchyma. Surgical excavation of hematoma is not recommended for most ICH cases due to questionable clinical benefits and adverse effects of surgery (Hemphill et al., 2015). Hemolysis within the hematoma may cause significant secondary injuries and irreversible neurological deficits due to the toxic properties of hemolytic products (Ziai, 2013). Microglia and monocyte-derived macrophage (M ϕ) are rapidly recruited at the bleeding site and may aid hematoma resolution by phagocytosing RBCs through erythrophagocytosis (Zhao et al., 2009;Chang C. F. et al., 2018;Jing et al., 2019). This detoxication process helps alleviate the brain injuries results from secondary detrimental process such as neuroinflammation and oxidative stress. Increasing number of studies have investigated the mechanistic signaling and beneficial effects of erythrophagocytosis in ICH (Chang C. F. et al., 2018;Chang et al., 2020;Xu et al., 2020). However, this endogenous erythrophagocytosis tends to be incomplete, and hemolytic productstriggered brain damage remains common and detrimental to recovery after ICH. Thus, enhancing endogenous erythrophagocytosis is an important strategy for ICH treatment.
In this review, we highlight the therapeutic values of targeting erythrophagocytosis in ICH. Firstly, we introduce the neurotoxicity of hematoma and the role of M ϕ-mediated erythrophagocytosis in hematoma clearance and ICH recovery. Then, we describe the dynamic process of phagocytosis, focusing on the essential membrane receptors in M ϕ. We further depict how erythrophagocytosis may be modulated by microenvironmental factors, including inflammatory cytokines and oxidative stress. Finally, we summarize the critical nuclear factors regulating erythrophagocytosis that could serve as druggable targets. The aim is to inform future pre-clinical and clinical studies on accelerating hematoma resolution as a means of improving patient outcomes in ICH.

NEUROTOXICITY OF HEMATOMA
The extravasated blood plays a critical role in ICH pathology. In acute ICH, the blood cells and other blood components from the ruptured vessels rapidly accumulate in the brain parenchymal, forming a hematoma with mass effect that can destroy the cerebral architecture (mass effect) (Xi et al., 2006). Subsequently, hemolysis occurs, releasing neurotoxic degradation products (hemoglobin, heme, and iron). These substances could cause detrimental effects on brain tissue (Figure 1; Wagner et al., 1996;Nakamura et al., 2005;Xue and Yong, 2020): -Inflammation is represented by the rapid recruitment of immune cells and the inflammatory cytokines in perihematomal regions (Loftspring et al., 2009;Zia et al., 2012). The hemolysis products, especially hemoglobin and heme, may function as the ligands of Toll-like receptor 4 (TLR-4), which could activate proinflammatory M ϕ and elevate the levels of proinflammatory cytokines (Figueiredo et al., 2007;Kwon et al., 2015;Lan et al., 2017). -Oxidative stress is featured by the accumulation of reactive oxygen species (ROS), which oxidize DNA, protein, and lipid, causing tissue damage (Marnett et al., 2003). Iron, generated from the heme degradation, can catalyze the well-studied Haber-Weiss reaction, yielding overwhelming ROS and resulting in oxidative stress (Wu et al., 2003;Nakamura et al., 2005). -Edema develops as early as hours and peaks at a range of 10-20 days in patients after ICH (Xi et al., 2006). At the early phase (< 3 days), thrombin and serum proteins extruded from the hematoma are the leading cause of edema. At the later stage (> 3 days), the hemolysis products precipitate delayed edema (Wagner et al., 1996;Urday et al., 2015). Of note, the TLR-dependent inflammation by hemoglobin and heme, as well as the Matrix metallopeptidase 9 (MMP-9) activation and the oxidative stress by iron, could compromise blood-brain barrier integrity and aggravate edema (Katsu et al., 2010;Urday et al., 2015).
Post-ICH hematoma expansion and rebleeding occur commonly in patients, suggesting a continuous enlargement of blood burden within the brain (Brott et al., 1997;Morgenstern et al., 2001). To counteract it, erythrophagocytosis by M ϕ occurs at both the edge and the center of hematoma, resulting in hematoma reduction (Cao et al., 2016). Timely clearance of hematoma can maintain homeostasis in cerebrovascular units and allows neurological recovery after ICH (Zhao et al., 2009). As such, shrinking hematoma via boosting the endogenous removal of RBCs, the source of hemolysis, has attracted increasing scientific interest in the past decade.

MACROPHAGE/MICROGLIA: THE MAJOR CELL PERFORMING ERYTHROPHAGOCYTOSIS
At onset, ICH rapidly recruits microglia, neutrophil, monocytes, and T lymphocytes, successively . For hematoma clearance, microglia and monocyte-derived macrophage are 'professional' phagocytes uptaking the damaged cells, including RBCs, in ICH Li Q. et al., 2021). Though some 'non-professional' cells, such as endothelial cells, are also involved in erythrophagocytosis in cerebral microbleeds, their roles in ICH remain unknown (Chang R. et al., 2018). Microglia are the brain resident macrophages, mediating diverse functions critical to brain development and injury, such as synaptic pruning and phagocytosis (Paolicelli et al., 2011). For macrophage, their precursor cells -monocyte -are thought to enter the brain as a component of blood at ICH onset while later migrate into the brain via cell adhesion pathways (Engelhardt, 2008;Mracsko et al., 2014). These peripheral monocytes might undergo in situ differentiation into mature macrophages in the ischemic brain (Gliem et al., 2012;Miro-Mur et al., 2016). Interestingly, a recent study found that skull and vertebral bone marrow also supplied monocytes, which infiltrated the brain border (e.g., the dura matter) and developed into macrophages in inflamed brains (Cugurra et al., 2021). Given the limited evidence on ICH, a review on the dynamic infiltration of monocytederived macrophage in the ischemic brain might shed light on future studies . Moreover, perihematomal M ϕ might follow the route of an available scaffold -white matter fibers -and migrate into the hematoma core, aiding the hematoma clearance . In addition to the white matter, molecules from the scar tissue are also considered able to support M ϕ activity, including phagocytosis, in brain injuries (Rolls et al., 2009).
Notwithstanding the diverse functions of macrophage and microglia observed in other types of strokes, most ICH studies could not distinguish macrophage from microglia due to the obstacles in differentiating between the two cell types in vivo (Zarruk et al., 2018). Fortunately, with the help of more specific cell markers (e.g., Tmem119 for microglia) and multichannel flow cytometry (Bennett et al., 2016;Li et al., 2018), some researchers had begun to study the two cell types separately in ICH (Chang C. F. et al., 2018;Li Q. et al., 2021). Due to the inconsistent gating strategy applied by these two studies, it remains inconclusive as to how the role of macrophage differs from that of microglia. Therefore, in this review, we use M ϕ to denote the two cell types except when discussing studies that clearly distinguish between the two. Figure 2 summarizes the key findings on erythrophagocytosis after ICH.
FIGURE 1 | Neurotoxicity of hematoma. After ICH, red blood cells (RBCs) would undergo hemolysis and degrade into hemoglobin, heme and iron consequently. These hemolysis products mediate cascades of inflammation, oxidative stress, and perihematomal edema, causing detrimental injury to the brain.

MEMBRANE RECEPTORS INITIATE ERYTHROPHAGOCYTOSIS
Exogenous stimulus, such as inflammation, could impair erythrocytes integrity and predispose them to become apoptosislike cells (Lang et al., 2014). The apoptotic erythrocytes are then recognized and phagocytosed by M ϕ, a process termed efferocytosis (Trahtemberg and Mevorach, 2017). Erythrophagocytosis in ICH is a type of efferocytosis. Blocking the M ϕ receptors for recognizing the apoptotic markers can impair the erythrophagocytosis and impede hematoma resolution. Efficient efferocytosis is important for tissue homeostasis by reducing exposure to toxic components of hemolysis and self-antigens which can substantially induce tissue damage and autoimmune response (Sisirak et al., 2016;Herzog et al., 2019). In ICH, efficient efferocytosis leads to controlled clearance of damaged erythrocyte before injuries are inflicted by uncontrolled hemolysis (Chang C. F. et al., 2018).
Of note, the current literature on erythrophagocytosis mainly focuses on the Step 3; the other steps remained largely unexplored in the context of ICH. Current studies have shown the therapeutic value of targeting the "don's eat me signal" from erythrocytes (CD47) and the surface phagocytosis receptors from M ϕ (CD36 and TAM family).

CD47
CD47 is an integrin-associated transmembrane protein ubiquitously expressed in many cell types including erythrocytes (Olsson et al., 2006). It regulates immune cell infiltration, phagocytosis, and the production of proinflammatory mediators and trophic factors by interacting with integrins and extracellular ligands (Brown and Frazier, 2001;Zhang et al., 2015). For phagocytosis, CD47 on erythrocytes acts as a "don't eat me" signal to block phagocytosis by binding to signal regulatory protein α (SIRPα) on macrophage (de Back et al., 2014). Preclinical studies have demonstrated the role and therapeutic value of CD47 in ICH. The perihematomal level of CD47 increases within hours but decreases subsequently, accompanied by M ϕ infiltration and erythrophagocytosis (Zhou X. et al., 2014;Cao et al., 2016). This explains the reverse correlation between CD47 and M ϕ-mediated immune response. Intracranial injection of CD47 knock-out blood resulted in quicker hematoma resolution and milder brain edema (Ni et al., 2016). This effect was reduced by intracranial injection of clodronate liposomes, a specific phagocytes depletion drug. In all, CD47 may serve as an inhibitory signal in M ϕ-mediated hematoma resolution. Inspiringly, CD47 blocking antibody significantly enhanced hematoma removal after ICH, rendering CD47 a promising and draggable target in ICH treatment (Jing et al., 2019;Tao et al., 2020).

CD36
Upon activation by "eat-me" signals, macrophage upregulates several membrane scavenger receptors to direct the ingestion process, including CD36 (Silverstein and Febbraio, 2009). The upregulated precursor intracellular CD36 undergoes glycosylation in the endoplasmic reticulum, followed by transportation to the cell membrane (Alessio et al., 1996;Roszer, 2017). Membrane CD36 cooperates with the αvβ3 receptor to engage with the eat-me signal thrombospondin (TSP) on apoptotic cells, inducing the internalization and digestion of target cells (Savill et al., 1990(Savill et al., , 1992. In ICH, CD36-mediated apoptotic cells clearance is essential for hematoma resolution. Its transcription was upregulated in the erythrocytes-treated microglia culture as well as the perihematomal region after ICH (Zhao et al., 2007b). Both genetic deletion and antibody blocking of CD36 impeded the phagocytosis of erythrocytes by microglia (Zhao et al., 2007b;Fang et al., 2014). As M ϕ-mediated removal of erythrocytes is required for clot clearance, the roles and therapeutic values of CD36 in hematoma development has attracted considerable attention. For example, CD36 knock-out mice was found to have slower hematoma resolution and aggravated deficits when compared to wild-type mice after ICH (Fang et al., 2014). What's more, the same study also found that patients with CD36 deficiency showed impaired hematoma resolution and poorer clinical outcome. Consistently, the upregulation of CD36 generates faster speed of erythrophagocytosis and hematoma resolution (Zhao et al., 2007b;Flores et al., 2016;. Enhancing the upstream regulatory mechanism of CD36, including peroxisome proliferator-activated receptor γ (PPAR γ) and the nuclear factor erythroid 2-related factor 2 (Nrf2), is a potential approach to promoting CD36-mediated hematoma clearance.

Axl/Mertk
TAM -namely, Tyro3, Axl and Mertk -is a group of receptor tyrosine kinases functioning as macrophage scavenger receptor (Lemke and Burstyn-Cohen, 2010). Tyro3 is more highly expressed on neuron rather than on M ϕ in the brain, whereas Axl and Mertk are more abundant on M ϕ which makes them more relevant to erythrophagocytosis in ICH (Fourgeaud et al., 2016;Chang C. F. et al., 2018). Axl and Mertk participate in cell survival, migration, and phagocytosis by engaging with the ligands, growth arrest specific 6 (Gas6) and protein s (Pros1) (Shafit-Zagardo et al., 2018). Gas6/Pros1 acts by bridging Axl/Mertk with the "eat-me" signal (PtdSer) on apoptotic cells, initiating the phagocytosis process (Tondo et al., 2019).
In ICH, Axl, and Mertk might be required for erythrophagocytosis. In a murine model, the transcriptional level of Axl, Mertk and Gas6 ligand are increased within 24 h after ICH (Tong et al., 2017); deficiency of Axl/Mertk resulted in defective erythrophagocytosis by macrophage in ICH (Chang C. F. et al., 2018). However, results from double knockout Axl/Mertk were inconclusive as to whether both or either of them was responsible for these findings, it is likely that Mertk and Axl may have diversified functions, with Mertk playing important roles in homeostasis and Axl being more involved in inflammatory conditions (Zagorska et al., 2014). Further studies are necessary to define whether Axl or Mertk alone is indispensable for the removal of erythrocytes in ICH. Interestingly, Axl/Mertk also modulates M ϕ phenotypes and alleviates neuroinflammation in addition to its effects in phagocytosis. In ICH, Toll-like receptors (TLRs) polarizes M ϕ toward proinflammatory phenotype (M1), as opposed to anti-inflammatory (M2) phenotype (Lan et al., 2017). Axl/Mertk can activate the TLRs suppressors, SOC1 and SOC3, thereby inhibiting M1-like M ϕ activation and suppressing inflammatory response in a range of disease including in ICH (Rothlin et al., 2007;Tong et al., 2017;Chang C. F. et al., 2018;Wu et al., 2021).
From a therapeutic standpoint, exogenous ligands (e.g., recombinant Gas6) can be used to target Axl/Mertk-mediated beneficial effects. In inflammatory conditions, Axl/Mertk is prone to be cleaved from the cell membrane, generating the soluble but unfunctional Axl/Mertk (sAxl/sMertk). sAxl/sMertk competitively occupies the endogenous ligands (Gas6 and Pros1), resulting in the lack of ligands for regulating homeostasis in inflammatory conditions (Cai et al., 2016;Chang C. F. et al., 2018). Therefore, exogenous ligands, such as recombined Gas6 can compensate this insufficiency, serving as the druggable target in augmenting the effects of TAM (Di Stasi et al., 2020). For example, recombined Gas6 promoted inflammation resolution via Axl-dependent manner in experimental ICH (Tong et al., 2017). However, whether the exogenous ligands could facilitate the Axl/Mertk-mediated hematoma clearance warrants further investigation.

MICROENVIRONMENTAL FACTORS ORCHESTRATE THE ERYTHROPHAGOCYTOSIS
Efficient phagocytosis requires phagocytes to digest multiple cells continuously, especially in acute inflammation where the apoptotic cells-to-phagocytes ratio is high (Doran et al., 2020). The significance of erythrophagocytosis in this context also depends on whether the M ϕ can remove such a tremendous amount of erythrocyte before irreversible hemolytic-induced brain injury commences (Bulters et al., 2018). Of relevance are microenvironmental factors, including neuroinflammation and oxidative stress, that may alter phagocytic function and therefore serve as viable therapeutic targets.

Inflammatory Mediators
The correlation between M ϕ phenotypes and phagocytosis is complex. Erythrophagocytosis skewed M ϕ toward the M2 phenotype, which reciprocally facilitated the removal of dying cells (Bensinger et al., 2009;Roszer, 2017;Chang C. F. et al., 2018). It possibly explains for the protective roles of M2-M ϕ observed in both preclinical and clinical studies of stroke (Chernykh et al., 2016;Min et al., 2016;Bai et al., 2020). The cytokines involved in M1-and M2-M ϕ activation could modulate erythrophagocytosis in ICH.
Interleukin-4 (IL-4) is the canonical activator of signal transducer and an activator of transcription 6 (STAT6), which is essential to the activation of M2 phenotype (Lawrence and Natoli, 2011). In ICH, exogenous IL-4 activated STAT6 and enhanced erythrophagocytosis in animal after ICH . The article revealed two potential mechanisms. Firstly, IL-4/STAT6 was observed to upregulate CD36, the scavenger receptor initiating phagocytosis. This may result from the direct binding of STAT6 to the promotor regions of CD36 gene (Szanto et al., 2010). Secondly, the study proved IL-1 receptor like 1 (ST2) was required for IL-4/STAT6-mediated clearance of erythrocyte. As ST2 promoted phagosome maturation (Buckley et al., 2011), it is likely that IL-4/STAT6 regulated phagosome infusion and thus enhancing phagocytosis in ICH. It is important to note that IL-4/STAT6 transcriptionally upregulates the antiinflammatory cytokines, which is likely to contribute to IL-4/STAT6 mediated pro-phagocytosis effect. For example, Arg1 is required for STAT6-mediated pro-phagocytosis by M ϕ in ischemic brain (Cai et al., 2019). The mechanism may link to the enzymatic role of Arg1 in arginine metabolism, which increased the M ϕ communication and resulted in continual phagocytosis .
In the contrary, cytokines involved in M1-M ϕ activation are potential to inhibit the removal of apoptotic cells. Amongst, TNF-α is the potential cytokine for targeting. TNF-α stimulates the M1 phenotype and is also regarded as a marker of M1-M ϕ (Lan et al., 2017). In ICH, TNF-α downregulated CD36 in microglia and impaired its function in erythrophagocytosis (Fang et al., 2014). What's more, TNF-α upregulated the "don't eat-me" signal CD47 in vascular smooth muscle cells and renders less phagocytosis (Kojima et al., 2016). Thus, reverse the inhibition of TNF-α in erythrophagocytosis may be a potential approach to promote hematoma clearance. It is important to note that TNF-α inhibitors have been approved in the treatment of many diseases, such as ankylosing spondylitis and Crohn disease (Gerriets et al., 2021). However, whether these inhibitors can facilitate erythrophagocytosis and perform therapeutic effects remain unproved in ICH.

Oxidative Stress
Oxidative stress is one of the major contributors toward pathological injury in ICH. The source of oxidative stress in ICH includes hemolytic products, mitochondria dysfunction, and M ϕ (Hu et al., 2016). Hemolytic products, particularly iron, catalyze a sequence of reactions known as the Haber-Weiss reaction, yielding highly reactive oxygen species (ROS) . Mitochondria dysfunction allows abnormal leakage of electrons from electron transport chain, overwhelming antioxidant system and leading to accumulation of ROS. M ϕ also generates ROS by inhibiting oxidative metabolism  and processing large quantities by-products from the cell corpse (Splettstoesser and Schuff-Werner, 2002).
Oxidative stress have profound effects on M ϕ (Vernon and Tang, 2013). In M ϕ, ROS is essential for bactericidal effects, whereas it can kill the phagocytes when the levels becomes overwhelming (Morrow et al., 2007;van-Charvet et al., 2010). What's more, the sudden onset of oxidative loading allows the transcription nuclear factor (NF)-κB to transfer from cytoplasm to nucleus, initiating the transcription of proinflammatory mediators including TNF-α (Brigelius-Flohe and Flohe, 2011; Sivandzade et al., 2019). As previously discussed, TNF-α is potential to block phagocytosis. Moreover, oxidants may alter cell structures or destroy the signals required for phagocytes (Anderson et al., 2002;Schrijvers et al., 2005). In all, oxidative stress may be a detrimental factor dampening normal function of M ϕ and compromising the removal of apoptotic cells.
Thus, the restriction of oxidative stress serves as a potential approach to improve M ϕ viability and facilitate erythrophagocytosis in ICH. To achieve this, strengthening the self-defense ability of M ϕ is a reasonable direction. To counteract the oxidative stress, M ϕ has developed the selfdefense mechanisms with the mainstay represented by Nrf-2 (Virag et al., 2019). Treating microglia with the activator of Nrf-2 showed faster erythrophagocytosis speed (Zhao et al., 2015a). The roles of other self-defense mechanisms warrants more investigation. Besides, some substances that could sequestrates iron, the source of ROS, are also potent targets in facilitating erythrophagocytosis, such as lactoferrin. As a glycoprotein of transferrin family, lactoferrin was found to limit oxidative stress and promote microglia-mediated phagocytosis (Actor et al., 2009;Zhao et al., 2021). Lastly, Arg1, the M2 marker which is essential for arginine metabolism, could alleviate the oxidative stress by competing with inducible nitric oxide synthase (iNOS) for the arginine substrate (Corraliza et al., 1995;Morris and Jr, 2007). Given the role of arginine metabolism in phagocytosis , this somehow reveals the internal links among inflammation, metabolism and oxidative stress in modulating phagocytosis.

UPSTREAM REGULATORS FOR ERYTHROPHAGOCYTOSIS
To initiate the erythrophagocytosis, the scavenger receptors in M ϕ drive the cell-to-cell interaction. These receptors are under controlled by by liver X receptor (LXR) and PPARs (α, β/δ and γ isotypes) (Roszer, 2017). Amongst, PPAR γ is the most studied regulator in ICH. Moreover, modulating the microenvironment factors shapes the function of M ϕ and enhances the phagocytosis efficacy. Nrf-2, as a powerful regulator of oxidative stress, has shown great therapeutic value in facilitating hematoma resolution and treating ICH. Last, energy metabolism is a critical component of efficient phagocytosis (Jiang et al., 2013). The regulatory role of AMPK, the energy sensor, in phagocytosis was also reviewed.
PPAR γ PPAR γ transcriptionally regulates genes that are critical to brain tissue repairment (Cai et al., 2018). Upon activation, PPAR γ heterodimerizes with retinoid X receptor (RXR) and subsequently engages with the conserved DNA sequences, namely peroxisome proliferator response elements (PPREs). PPREs is located in the promoter regions of cytoprotective genes, including the scavenger receptors and catalase, with the latter is essential to minimize oxidative injury (Cai et al., 2018). Thus, PPAR γ directly modulates phagocytosis and alleviates the oxidative stress, rendering it to be the most potent target in driving erythrophagocytosis and hematoma resolution in ICH.
In ICH, the activation of PPAR γ could upregulate the scavenger receptors and facilitate erythrophagocytosis. Generally, the scavenger receptors for clearing apoptotic cells are regulated by liver X receptor (LXR) and PPARs (α, β/δ and γ isotypes) with various combination patterns (Roszer, 2017): CD36 solely by PPAR γ, Axl by PPARs, and Mertk by PPARs and LXR. These patterns reflected the indispensable role of PPARs, especially PPAR γ, in regulating phagocytosis, which was supported by a number of ICH studies. Zhao et al. were the first to demonstrate that PPAR γ agonist could upregulate CD36 in microglia, thereby facilitating erythrophagocytosis in vitro (Zhao et al., 2007b). This pioneering work further proved that PPAR γ activation enhanced hematoma resolution and functional recovery in ICH. It paved the way for targeting M ϕ-mediated hematoma resolution in ICH (Zhao et al., 2009). Subsequently, activation of PPAR γ was observed to upregulate other scavenger receptors, Axl and Mertk, and expedite hematoma resolution in ICH (Zhuang et al., 2020). Moreover, the effects of PPAR γ activation in erythrophagocytosis were also verified in other types of hemorrhagic stroke Flores et al., 2016). By activating PPAR γ, several pharmacological agents were found to confer protective effects in experimental ICH Fu et al., 2020;Zhao et al., 2020;Zhuang et al., 2020). Taken together, PPAR γ activation is one of the mainstays of phagocytosis modulator in facilitating hematoma clearance after ICH.
To magnify PPAR γ-mediated protective effects, both the endogenous and exogenous stimulators are viable targets. PPAR γ is initially activated by the cell corpse-derived lipids in M ϕ when phagocytosis commences (Kourtzelis et al., 2020). Interestingly, the PPAR γ activity of M ϕ could also be modulated by other engulfed elements in addition to apoptotic cells. For instance, M ϕ could uptake the functional mitochondria and humanin released by astrocytes, which in turn upregulated the expression of PPAR γ and its mediated phagocytosis in ICH . In addition, activating PPAR γ-targeted genes and PPAR γ-mediated hematoma resolution could also be achieved by activating its transcription partner, i.e., RXR (Chang et al., 2020;Ting et al., 2020). Lastly, a clinical trial using pioglitazone, a known PPAR-gamma agonist approved by FDA, has been conducted in ICH patients, aiming to reduce hematoma burden and improve outcomes (NCT00827892). Other nuclear receptors may also play a role in clearing apoptotic cells, such as LXR, retinoic acid receptor (RAR), and glucocorticoid receptor (GR) (Roszer, 2017). Their roles in ICH warrants further investigation.

Nrf-2
Nrf-2 is the principal transcriptional factor protecting cells from endogenous and exogenous stress . Upon activation, Nrf2 heterodimerizes with the Maf family and binds to the antioxidant response elements (AREs) located in the regulatory regions of cytoprotective genes. These genes include antioxidant agents, such as catalase, HO-1, SOD, etc. (Zhao and Aronowski, 2013). Growing evidence supports the beneficial roles of Nrf2 in ICH. For instance, the absence of Nrf2 in rodents resulted in worse oxidative injury and more neurological deficits while the activation of Nrf2 reversed these effects in ICH (Wang et al., 2007;Zhao et al., 2007a;Iniaghe et al., 2015;Cheng et al., 2021).
Nrf-2 activation has also been proved to enhance the erythrophagocytosis by microglia and hematoma clearance in ICH (Zhao et al., 2015a). The effect of Nrf2 in promoting phagocytosis is partly CD36-dependent. The modulation of Nrf2 on CD36 may be PPAR γ-dependent or PPAR γ -independent, but this has not yet been confirmed (Ishii et al., 2004;Zhao et al., 2015c). Moreover, Nrf-2 regulates the genes responsible for detoxifying the blood products, including genes of ferritin, hemopexin and haptoglobin (Zhao and Aronowski, 2013;Deng et al., 2020;Liu et al., 2021) Nrf2 also regulates NF-κB pathway and alleviates inflammation in ICH, rendering targeting Nrf2 as a highly promising approach to augmenting hematoma resolution (Cheng et al., 2021). Indeed, agonists of Nrf2, such as sulforaphane, dimethyl fumarate and others, have been shown to accelerate hematoma reduction (Zhao et al., 2015a,b).

AMP-Activated Protein Kinase
The reprograming of energy metabolic pathways is an emerging hallmark of anti-inflammatory (M2) M ϕ (Devanney et al., 2020). Particularly, these anti-inflammatory cells exhibit increased mitochondria oxidative metabolism with much lower level of glycolysis, pinpointing the demand for efficient energy production for inflammation resolution and tissue repair. AMPactivated protein kinase (AMPK) attracts particular interest in fulfilling this energy demand of M ϕ (Jiang et al., 2013). As an intracellular sensor of the "fuel status", AMPK is activated by the drop of ATP-to-ADP ratio, which occurs in conditions like mitochondria inhibition, exercise, and starvation (O'Neill and Hardie, 2013;Herzig and Shaw, 2018). To counteract energy insufficiency, AMPK switches on catabolic activities to generates energy more efficiently while dampening anabolic process that consumes ATP. Given the intimate connection between cellular metabolism and immune response, the role of AMPK in macrophage functions has been reviewed (O'Neill and Hardie, 2013).
AMP-activated protein kinase activation has been reported to skew macrophage activation toward the M2 phenotype and enhance hematoma resolution in ICH (Vaibhav et al., 2018). Several in vitro studies also demonstrated that AMPK activation contributed to the M2 M ϕ polarization and phagocytosis (Wan et al., 2013;Jin et al., 2014;Kaiser et al., 2020;Seneviratne et al., 2020). Consistently, the absence of AMPK-α1, the sole isoform expressed in M ϕ, impaired the anti-inflammatory and prophagocytic effect of M ϕ in various brain disorders (Vaibhav et al., 2018;Lv et al., 2020).
The mechanisms underlying AMPK's pro-phagocytosis effect are complex. On the one hand, AMPK cooperates with the transcriptional regulators (PPAR γ and Nrf-2), modulating their downstream scavenger receptors including CD36 and Mertk (Wan et al., 2013;Galvan et al., 2014;Kaiser et al., 2020;Lv et al., 2020). On the other hand, AMPK promotes the organization of cytoskeleton and the acidification of endosomal-lysosomal system, facilitating the ingestion and digestion of apoptotic cells (Labuzek et al., 2010;Bae et al., 2011). In sum, AMPK activation favors inflammation resolution and might enhance apoptotic cells clearance in ICH.
Pharmacological agents, such as AdipoRon and CTRP3, also showed potential in driving AMPK-mediated augmentation on phagocytosis or inflammation resolution and functional recovery in preclinical ICH studies (Wang et al., 2016;Zheng et al., 2019). Several clinical trials on RIC-mediated AMPK activation and hematoma clearance are ongoing: NCT04757597, NCT04657133, NCT03930940 and NCT03481777. However, the direct evidence on the pro-phagocytotic effects of AMPK activation warrants verification in ICH.

FUTURE DIRECTIONS
Given the limited evidence available on ICH in this context, studies in other types of stroke could inspire the future research direction in ICH. Ischemic stroke is the main type of stroke and therefore has drawn greater scientific interest than other types of strokes. Regarding the phagocytosis of apoptotic cells (including injured RBCs and neurons), the underlying mechanisms appear to be similar and comparable between the two conditions. For example, the scavenger receptor CD36, which was widely studied in ICH, has also been proved to mediate phagocytosis in ischemic stroke (Woo et al., 2016). The phagocytosis effects of other molecules, such as Arg-1 and PPAR γ, were also reported in ischemic strokes (Cai et al., 2019;Zhang et al., 2019). Lastly, metabolic modulation, the phagocyted targets and the dynamic picture of pathophysiologic response after initial erythrophagocytosis are also important areas for future investigations.

Better Understanding of the Metabolic Modulation in Erythrophagocytosis
One of the priorities of future studies was to explore the basic mechanisms of erythrocytes removal in the hemorrhagic brain. Although the basic biology of efferocytosis has been deeply studied, whether it could be applied to post-ICH erythrophagocytosis needs more investigation (Doran et al., 2020;Kourtzelis et al., 2020). Amongst, this review underlines the significance of defining the metabolic adaption of M ϕ which contributes to ongoing phagocytosis. Then, we could explore the potential targets for modulating this adaption to achieve efficient phagocytosis in ICH. Interestingly, the roles of metabolic modulation in phagocytosis might explain the neuroprotective effects of some approved agents. For instance, uncoupling protein 2 (UCP2), a mitochondria membrane protein, has been observed to improve neurological outcomes in stroke (Mattiasson et al., 2003;Nakase et al., 2007;Mehta and Li, 2009;Zhao et al., 2019). This protein maintained the membrane potential and supported continuous phagocytosis (Krauss et al., 2002(Krauss et al., , 2005Park et al., 2011). However, whether UCP2 plays a role in facilitating phagocytosis in ICH is undefined.

Better Understanding of the Phagocyted Targets
Erythrophagocytosis shares similar mechanisms in phagocytosing different apoptotic cells. Thus, enhancing the erythrophagocytosis signals can also eliminate other apoptotic cells nearby (Galloway et al., 2019). In ICH, the hematoma usually contains unvital neurons due to mechanical force and/or neurotoxicity of hematoma (Xi et al., 2006). Historically, eliminating the dying brain cells and synapses was regarded as a beneficial process since it would prevent self-antigen exposure and establish new homeostasis in the brain (review by Galloway et al. (2019)). However, the adverse effects of eliminating apoptotic neurons have recently been demonstrated in several brain disorders, including Alzheimer's disease, multiple sclerosis, and strokes (Neher et al., 2013;Hong et al., 2016;Werneburg et al., 2020). The phagocytosis of "stressed-but-viable" neurons by M ϕ results in cell death, called "phagoptosis, " which could lead to delayed neuron loss (Brown and Neher, 2012;Neher et al., 2013). In ICH, a recent study demonstrated that the Mertkdependent phagocytosis of synapse worsened the neurological outcome in animals after stroke (Shi et al., 2021). Table 2 outlines other adverse effects of phagocytosis which may be potentially contradictory to this review's central tenet and suggest the need for a more specific phagocytosis system. Moreover, a systemic upregulation of erythrophagocytosis might disturb the physical removal of RBCs by splenic red pulp macrophage, since it involves the same phagocytosis-relevant receptors (Klei et al., 2017). Thus, future studies need to search for a pro-phagocytotic drug with localized specificity.

CONCLUSION: ERYTHROPHAGOCYTOSIS AS A SPATIOTEMPORALLY DEVELOPING STORY
The foregoing review suggests that it is necessary to consider erythrophagocytosis in ICH as a spatiotemporally progressing event. There are mainly two concerns. Firstly, how could the M ϕ completely remove the damaged RBCs in a situation where the latter vastly outnumbers the former? Moreover, the population of M ϕ might be further reduced by the primary injury or the intracellular iron toxicity (van-Charvet et al., 2010;Youssef et al., 2018). To prevent the overwhelming of phagocytotic machinery, the number of M ϕ must be regulated to obtain sufficient phagocytotic capacity (Morioka et al., 2019). Therefore, therapies aimed at maximizing the number of functional phagocytes could improve RBC removal and should become a focus of future research. Inspiringly, this idea of introducing more functional M ϕ has recently been found to facilitate animal recovery from traumatic brain injury (Li Z. et al., 2021). Another layer of complexity lies in the fact that these immune cells tend to execute time-dependent effects in cerebrovascular injury, i.e., worsening the brain injury at acute phase but repairing the tissue at later stage (Mastorakos et al., 2021). One should therefore also take into consideration the optimal therapeutic time window when attempting to bring these cells into the brain. Secondly, what is the fate of M ϕ after engulfing RBCs in ICH? Although M ϕ could digest RBCs into the degradation products within themselves, could they process the blood components, especially iron, in a non-toxic way? Microglia contains the system for transporting and storing the iron in the normal brain, which is critical to the brain iron cycle and homeostasis (Winn et al., 2020). However, it remains unknown whether microglia could maintain its own homeostasis rather than undergoing iron-dependent cell death, namely "ferroptosis" in ICH (Youssef et al., 2018). Further studies are needed to achieve a better understanding of erythrophagocytosis and translate its therapeutic value to clinical practice.

AUTHOR CONTRIBUTIONS
JL and ZZ wrote the manuscript. GL supervised the drafting and revision of the manuscript. All authors contributed to the article and approved the submitted version.