Trypanosoma brucei triggers a broad immune response in the adipose tissue

RESEARCH ARTICLE Trypanosoma brucei triggers a broad immune response in the adipose tissue Henrique Machado 1☯ , Tiago Bizarra-RebeloID 1☯ , Mariana Costa-SequeiraID 1 , Sandra TrindadeID 1 , Ta ˆnia CarvalhoID 1 , Filipa Rijo-FerreiraID 2 , Barbara RentroiaPachecoID 1 , Karine Serre 1‡ *, Luisa M. FigueiredoID 1‡ * 1Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal, 2Department of Neuroscience, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, Texas, United States ☯These authors contributed equally to this work. ‡ KS and LMF also contributed equally to this work. *[email protected] (KS); [email protected] (LMF) Abstract Adipose tissue is one of the major reservoirs of Trypanosoma brucei parasites, the causative agent of sleeping sickness, a fatal disease in humans. In mice, the gonadal adipose tissue (AT) typically harbors 2–5 million parasites, while most solid organs show 10 to 100-fold fewer parasites. In this study, we tested whether the AT environment responds immunologically to the presence of the parasite. Transcriptome analysis of T.brucei infected adipose tissue revealed that most upregulated host genes are involved in inflammation and immune cell functions. Histochemistry and flow cytometry confirmed an increasingly higher number of infiltrated macrophages, neutrophils and CD4+ and CD8+ T lymphocytes upon infection. A large proportion of these lymphocytes effectively produce the type 1 effector cytokines, IFN-γand TNF-α. Additionally, the adipose tissue showed accumulation of antigen-specific IgM and IgG antibodies as infection progressed. Mice lacking T and/or B cells (Rag2 -/- , Jht -/- ), or the signature cytokine (Ifng -/- ) displayed a higher parasite load both in circulation and in the AT, demonstrating the key role of the adaptive immune system in both compartments. Interestingly, infections of C3 -/- mice showed that while complement system is dispensable to control parasite load in the blood, it is necessary in the AT and other solid tissues. We conclude that T.brucei infection triggers a broad and robust immune response in the AT, which requires the complement system to locally reduce parasite burden. Author summary African trypanosomiasis is a neglected disease with significant socio-economic burden in sub-Saharan Africa. The protozoan parasite Trypanosoma brucei, a causative agent of African trypanosomiasis, can be found in the blood and extra-vascular spaces of the infected host. For an unknown reason, T.brucei accumulates in adipose tissue (AT) in very high numbers. Here we used a multidisciplinary approach to assess whether an immune response was mounted in AT during a T.brucei infection. We found that as PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 1 / 26 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Machado H, Bizarra-Rebelo T, CostaSequeira M, Trindade S, Carvalho T, Rijo-Ferreira F, et al. (2021) Trypanosoma brucei triggers a broad immune response in the adipose tissue. PLoS Pathog 17(9): e1009933. https://doi.org/10.1371/ journal.ppat.1009933 Editor: Stefan Magez, Vrije Universiteit Brussel, BELGIUM Received: April 14, 2021 Accepted: August 31, 2021 Published: September 15, 2021 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.ppat.1009933 Copyright: ©2021 Machado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: RNA-Seq data is publicly available in the ArrayExpress database under the accession number E-MTAB-4061 and EMTAB-7596. infection progresses, a broad variety of immune cells and antibodies accumulate in the AT. We also found that this broad immune response is partially able to control parasite numbers in the AT. Our study provides evidence that T.brucei parasites present in the AT are subjected to immune surveillance. The reason why T.brucei accumulates to such a high extent in AT remains to be elucidated. Introduction Trypanosoma brucei is an extracellular protozoan parasite that causes sleeping sickness in humans and nagana in cattle, diseases that still hold a significant socio-economic impact in sub-Saharan Africa[1,2]. T.brucei transmission to a mammalian host occurs upon the bite of an infected tsetse fly (Glossina spp). During such blood meal, parasites are released into the circulation and quickly differentiate into bloodstream forms (BSFs) that proliferate and invade the interstitial spaces of many organs[3]. In primate hosts, most African trypanosome species are rapidly eliminated by an innate immune trypanosome lytic factor (TLF)[4,5]. This TLF is delivered by germline-encoded antibodies[6] and promotes complete parasite elimination before the onset of disease. In non-primate mammalian hosts, or in primates infected TLF-resistant T.brucei, most parasite elimination does not occur through direct humoral mediated lysis and instead requires internalization by the host’s phagocytes, mainly monocytes and macrophages. Optimal phagocytosis of T.brucei requires antibody and complement mediated opsonization[7]. These antibodies are produced by B cells, particularly plasmocytes (plasmablasts and plasma cells), activated during infection and are directed primarily at the parasite’s variant surface glycoprotein (VSG)[7,8]. This allows for direct Fc receptor-mediated phagocytosis and for the classical activation of the complement system, followed by phagocytosis. Activation of both macrophages/monocytes and B cells is promoted by a T cell response comprising CD4+ T cells and CD8+ T cells[9]. Macrophages/monocytes respond to cytokines produced by T cells, such as interferon gamma (IFN-γ) and tumour necrosis factor alpha (TNF-α), by increasing phagocytic capacity, phagosome acidification and production of microbicidal reactive nitrogen species[10,11]. Additionally, CD4+ T cells play an important role in B cell survival and maturation, which allows for the production of higher affinity antibodies via B cell class switching and affinity maturation[12]. Although this immune response is capable of largely limiting the number of parasites, it does so at the cost of severe immunopathology. To limit this deleterious effect, the immune system partially downregulates itself through the production of anti-inflammatory cytokines such as interleukin (IL)-10 which is highly expressed by regulatory T (Treg) cells[13,14]. These host-parasite interactions, together with the capacity of parasites to switch VSGs by antigenic variation[15] and to undergo terminal differentiation to transmissible forms[16], leads to the characteristic oscillating waves of parasitemia displayed throughout the disease[17]. Most studies on the immune response against T.brucei have focused on blood and lymphoid organs. However, T.brucei occupies several extra-vascular tissues, including the brain, skin[18], testis[19,20] and adipose tissue (AT)[21], which may present distinct immune responses[22] and even be immune privileged sites. A study from our laboratory showed that the AT is not only one of the major parasite reservoirs, reaching levels comparable to the blood, but also that parasites in this tissue (adipose tissue forms–ATFs) present a gene expression signature different from bloodstream forms[21]. PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 2 / 26 Funding: This work was supported by the European Research Council (FatTryp, ref. 771714) awarded to LMF, by Fundac¸ão para a Ciência e Tecnologia (CEECIND/03322/2018) awarded to LMF, (PTDC/MED-IMU/30948/2017 and CEECIND/ 00697/2018) awarded to KS, (PD/BD/128286/ 2017) awarded to HM, (SFRH/BPD/89833/2012) awarded to ST, (IMM/BI/83-2017 through PTDC/ BIM-MET/4471/2014) awarded to TB-R and by the National Institutes of Health (NIGMS K99GM132557) awarded to FR-F. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. For a long time, the role of the AT was believed to be exclusively of lipid storage, exerting important roles in energy homeostasis and thermogenesis[23]. It is now recognized that the AT is an immunologically active[24] endocrine organ[25], as adipocytes secrete a wide variety of immunomodulatory molecules[26]. T.brucei is not the only parasite that accumulates in the AT. Neospora caninum[27], Plasmodium berghei and Trypanosoma cruzi also reside in this tissue[28].Some protozoan parasites can even challenge the anti-inflammatory balance in AT or take advantage of such an environment to establish a successful infection. For instance, upon infection, N.caninum was reported to trigger a shift towards a pro-inflammatory environment, promoting pathogen elimination from AT[27]. On the contrary, T.cruzi was described to induce an anti-inflammatory macrophage polarization in a diet-induced obesity model, contributing to tissue homeostasis and parasite survival[29]. Given the importance of tissue immunity in a wide variety of conditions such as in infections[30] and cancer[31], here we compared the circulating, splenic and AT type of immune responses mounted against T.brucei. We show that the AT is a favorable location where an effective protective immune response is mounted, with both innate and adaptive immune cells that control parasite load in this tissue. We conclude that like in the blood, in the AT parasites require active mechanisms of immune evasion. Results Transcriptome of infected adipose tissue reveals a strong inflammatory response A local immune response typically consists in the accumulation of myeloid and lymphoid cells sensing determinants of inflammation and/or determinants specific to the pathogen in order to control the infection and regulate the immune response itself. Given that the signature profile of immune cells is unique and different from all other resident cells of AT (adipocytes, endothelial cells, and mesenchymal stromal cells)[32,33], we postulated that a transcriptome analysis of infected versus non-infected AT should provide a first glimpse of the type of immune response that is mounted in this tissue. We performed RNA sequencing (RNA-Seq) analysis of mice gonadal AT depots at early and late stages of infection. Total RNA was extracted from this tissue at day 0 (n = 3), 6 (n = 3) and 26 (n = 2) post-infection. Most sequence reads mapped to the mouse genome (between 86% and 96%) and the 1%-10% of reads that mapped to the parasite were not considered in this analysis (S1 Table). Unbiased clustering of the expression profiles of the analysed samples showed that noninfected and infected AT clustered separately. Furthermore, the cluster of infected AT was divided in 2 sub-clusters separating samples of early and late infection time points (Fig 1A). This indicates significant alterations of transcript abundances in AT upon and during a T.brucei infection. To identify the genes differentially expressed, we used three distinct algorithms. Genes significant in at least 2 of them (adjusted p-value <0.01) and having a fold-change higher than 2 were considered differentially expressed. At day 6 post-infection, 2678 genes were differentially expressed compared with non-infected AT. From these, 1770 genes were upregulated while the remaining 908 were downregulated at day 6 post-infection (S1C File). When comparing AT at day 26 post-infection with non-infected AT, 3684 genes were differentially expressed, from which 2264 were upregulated and 1420 downregulated (S1D File). Between AT at day 6 and day 26 post-infection, we identified 941 differentially expressed genes, 503 upregulated and 438 downregulated at day 26 post-infection (S1E File). Gene ontology (GO) enrichment analysis was conducted to determine which GO terms were over-represented among the differentially expressed genes (Fisher’s exact test, p-value <0.01). Enrichment tests on the upregulated genes in infected AT (both on day 6 PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 3 / 26 and day 26 versus day 0) revealed a strong inflammatory profile, with 84.7% of the top 150 (127/150) most significant Biological Process GO-terms related to immunity (Fig 1B and S2 File). Also, the median fold-change of the genes associated to the top 20 Biological Process GO-terms in infected versus non-infected AT (p-value <1x10 -5 ) increased from day 6 to day 26 (Fig 1C), suggesting that the immune response taking place in AT increases in magnitude as infection progresses. Conversely, most downregulated genes were involved in metabolite biosynthesis (e.g. fatty acid biosynthetic processes) and other metabolic changes (e.g. fatty acid beta-oxidation) (S1 and S2D and S2E Files). Interestingly, differential expression analysis showed a marked and significant upregulation of T helper 1 (Th1) signature genes (e.g.Tbx21,Eomes,Il12r1b,Ifng,Tnf,Nos2,Stat1,Stat4) both on day 6 and day 26 versus day 0 (S1A Fig and S1C and S1D File), which are associated with a protective immune response against T.brucei. This profile was not observed for non-protective Th2 response associated genes (e.g.Gata3,Il4,Il5,Il13,Stat5,Stat6), which were either not detected or Fig 1. Transcriptome of T.brucei infected AT shows a strong immune response. (A) Heat map of hierarchical clustering of Spearman correlations of Reads per kilobase per million mapped reads (RPKM) levels from non-infected (D0), n = 3 and infected AT at early (D6), n = 3 and late (D26), n = 2 time points. (B) Pie chart of most significant biological processes GO term families. (C) Heat map view of median fold change (FC) of genes associated to the top 20 Biological Process GO terms in day 6 and day 26 of infection versus non-infected AT (D6 vs D0 and D26 vs D0, respectively) (D) Prediction of immune cell distribution based on immuCC RNA-seq deconvolution. https://doi.org/10.1371/journal.ppat.1009933.g001 PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 4 / 26 not differentially expressed in all conditions assessed (S1B Fig and S1 File). In addition to Th1 signature genes, the infected AT showed an overall upregulation of pro-inflammatory genes such as Gzma,Gzmb,Il1a and Il6 (S1A Fig). In turn, this was accompanied by the upregulation of genes such as Il10,Il10ra,Ctla4,Foxp3 and Pdcd1 which are associated with the suppression of an active immune response (S1B Fig). To assess whether the changes in transcriptomic profile of the AT could be due to an alteration of the cellular makeover of the tissue, we performed a cellular deconvolution analysis using the immuCC algorithm[34]. This analysis predicted that during a T.brucei infection the relative proportions of innate and adaptive immune cells were variable (Fig 1D). Specifically, these data predicted a relative increase of macrophages and CD8+ T cells between days 0 and 6 post-infection, while the prediction for monocytes and B cells was a relative decrease in the same period. Lastly, by day 26 post-infection this deconvolution model predicted a sizeable increase of the CD4+ T cell response, with a predicted 3 to 4-fold relative increase when compared to days 0 and 6 post-infection. Overall, the transcriptome of the AT of infected mice revealed signs of intense inflammation and an ongoing active immune response. Adipose tissue shows a gradual accumulation of immune cells during infection Bulk RNA-seq provides a strong indication of broad changes in the inflammatory profile immune cell milieu of the AT, however it does not allow to identify the absolute accumulation of immune cells and determine their selective in situ effector functions. To investigate this, and to compare the extent of systemic to AT immune responses, we quantified the number of parasites and immune cells in the spleen and the AT. The spleen was used as a proxy for the systemic immune response as it filters bloodborne pathogens and is a major site for lymphocyte activation and proliferation. Quantifications were performed at key time-points of the murine T.brucei infection, encompassing the formation and resolution of the first and second peaks of parasitemia as well as the chronic stage of infection. In the blood, the progression of parasitemia throughout infection exhibited its characteristic pattern (Fig 2A): a first peak around day 5 post-infection; undetectable parasitemia between days 9 and 13 post-infection; followed by a fluctuating number of parasites that differs in each mouse until day 28 post-infection. In the spleen, the number of parasites reached the peak at day 5 postinfection and dropped 32-fold from 6 to 9 days post-infection, when the lowest parasite load was detected (Fig 2B). From day 14 post-infection, the number of parasites fluctuated at around 10 5 parasites per organ. Remarkably, in the gonadal AT, the number of parasites peaked at day 6 post-infection (one day later than in blood/spleen) and it suffered only a 6-fold reduction from 6 to 9 days post-infection (Fig 2B). From day 14 post-infection onward, the number of parasites fluctuated at around 10 6 parasites, which is an approximate constant 10-fold more than in the spleen or any other organ assessed, as we have previously reported[21]. At the same key time-points of infection, immune cells from spleen and AT were analysed by flow cytometry. Using an anti-CD45 antibody to gate all immune cells (S2 Fig), we observed a striking gradual accumulation in the gonadal AT with a 16-fold increase in the number of immune cells per tissue from days 0 to 28 post-infection (Fig 2C). Conversely, and despite a large increase in spleen mass (S4A Fig), no significant changes were observed in the total number of splenic immune cells. This suggests that the increase in spleen weight may be largely due to the accumulation of damaged red blood cells undergoing eythrophagocytosis[35], which is known to occur during T.brucei-induced anemia[36]. Interestingly, while the number of parasites in the AT peaks on day 6 post-infection and oscillates afterwards, the immune cells are recruited and/or proliferate in AT with different dynamics, PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 5 / 26 increasing steadily as infection progresses. The fact that the spleen has a higher proportion of immune cells than the AT is not surprising due to its nature as a secondary lymphoid organ. To assess the distribution of the inflammatory cell infiltrates and the morphological changes of the AT, we performed immunohistochemistry for parasites (anti-VSG antibody), macrophages (anti-F4/80 antibody) and T cells (anti-CD3 antibody) in sections of gonadal AT on days 6 and 26 post-infection. Consistent with the analysis by flow cytometry, a significant increase was observed in the number of infiltrating macrophages and T cells, mainly during the later time-points of infection (Fig 3A). Inflammatory cells were distributed diffusely in the tissue, often associated with parasites or parasite debris. By transmission electron microscopy (TEM), parasites were also detected intracellularly, in the cytoplasm of phagocytes (most likely macrophages) (Fig 3B). These intracellular parasites were often surrounded by membranous whorls, suggesting they had been phagocytosed. In conclusion, our analysis shows that the AT is not an immune silent tissue, but instead that it actively responds to a T.brucei infection. It is important to mention that these data were acquired in an intraperitoneal infection model and that Tsetse fly transmitted or intradermally injected T.brucei may lead to significant differences in immune response. Adipose tissue is populated by effector innate and adaptive immune cells To experimentally characterize the immune response that is mounted against T.brucei in the AT, we isolated gonadal fat pads and spleen of infected animals at different time-points of the infection, and we identified and quantified the main immune cell subsets by flow cytometry. On the one hand we followed the dynamics of neutrophils, monocytes, and macrophages to assess the innate myeloid immunity branch. On the other hand, to assess the adaptive immunity branch, we followed T cell subsets (helper T cells (CD4+) and killer T cells (CD8+)) and evaluated whether these T cells were activated and responding to the infection by assessing their expression of two main pro-inflammatory cytokines, IFN-γand TNF-α. Additionally, we analysed the frequency of Treg cells, a key immunosuppressive subset. The gating strategies for all the populations are presented in the S2 Fig. As infection progressed, while the total number of immune cells of all subtypes assessed increased in the AT, the same was not observed for the spleen (Fig 4). As expected for the innate immune response, neutrophils (Fig 4A) and monocytes (Fig 4B) are the first to be Fig 2. Dynamics of T.brucei parasites and immune cells throughout infection. (A) Number of T.brucei parasites per mL of blood of a single representative mouse, quantified using a hemocytometer. Dashed line represents the detection limit (3.75x10 5 parasites/mL of blood). (B) Total number of T.brucei parasites in the spleen and gonadal AT, quantified by qPCR. (C) Number of live immune cells in spleen and gonadal AT. (B-C) Error bars represent the standard error of the mean (SEM) (n = 2–6 mice per group). Statistical analysis was performed with a two-way analysis of variance (ANOVA) using Sidak’s test for multiple comparisons. �refers to statistical differences within the groups in each time-point and the non-infected group. �, P<0.05; ��, P<0.01; ���, P<0.001; ����, P<0.0001. https://doi.org/10.1371/journal.ppat.1009933.g002 PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 6 / 26 Fig 3. Inflammatory cell response in the adipose tissue. (A) Immunohistochemistry reveals a large number of T.brucei parasites (antiVSG antibody) accompanied by marked infiltration by macrophages (anti-F4/80 antibody) and moderate infiltration by T cells (antiCD3 antibody), at a later stage post-infection (day 26). (n = 4–6 per time-point); DAB counterstained with hematoxylin, original magnification 40x (Scale bar, 50μm). (B). Representative electron micrograph of the perirenal adipose tissue of the mouse, 26 days postinfection, showing various extracellular tangential and cross-sectional profiles of trypanosomes (arrowhead) and also intracellular parasites, in the cytoplasm of a phagocyte [most probably a macrophage (inside dashed line)]. Asterisk, vessel; ec, endothelial cell; n, nucleus, f, flagellum. B-1, B-2. Insets of the phagocytized trypanosomes, with well-defined nucleus (black arrowhead) and pseudopodia (white arrowhead), consisting of multiple membranous whorls extended by the phagocyte around the parasite. https://doi.org/10.1371/journal.ppat.1009933.g003 PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 7 / 26 recruited to the AT, presenting an increase of 68-fold and 49-fold from days 6 to 9 post-infection, respectively. Few neutrophils were detected in the gonadal AT prior to infection with 8.45x10 2 cells, however they increased nearly 100-fold by day 28 post-infection (Fig 4A). Similarly, the number of neutrophils in the spleen increased significantly, presenting a 38-fold increase from days 0 to 28 post-infection (Fig 4A). Prior to infection, few monocytes were present in the spleen and AT. During T.brucei infection, monocytes accumulated in both tissues, increasing 39-fold in the spleen and 12-fold in the gonadal AT by day 28 post-infection (Fig 4B). Macrophages were the most abundant myeloid population in both tissues in control mice. Interestingly, this population was kept at relatively stable numbers throughout infection in the spleen, as the only significant change observed was a moderate 2.7-fold increase by day 28 post-infection (Fig 4C). In the gonadal AT, the number of macrophages was significantly increased from day 20 post-infection onward, presenting a 10-fold increase by day 28 postinfection (Fig 4C). CD4+ and CD8+ T cell subsets showed a similar overall increase in the AT, while the spleen showed no changes in CD4+ T cells and an overall decrease for CD8+ T cells (S3B and S3C Fig). Within the total pool of T cells, we assessed the fraction that differentiated into effector T cells expressing IFN-γ, TNF-αor both pro-inflammatory cytokines. During the infection, we observed that the number of effector CD4+ T cells presented a modest incremental trend in the spleen, reaching a 3.8-fold increase by day 28 post-infection (Fig 4D). A higher relative increase in effector CD4+ T cells was observed in gonadal AT, with significant accumulation from day 14 post-infection onwards, showing a 14.6-fold increase by day 28 post-infection (Fig 4D). A distinct profile between the spleen and gonadal AT was observed for effector CD8 + T cells. There was a trend for a modest reduction in number of this effector cell in the spleen (Fig 4E). In contrast, a significant increase in the number of effector CD8+ T cells in the gonadal AT was observed from day 16 post-infection onwards, reaching a 8.7-fold increase by day 28 post-infection. This profile of effector T cells is in agreement with the high expression of TNF-αand IFN-γin gonadal AT revealed at the transcriptomic level (S1 Fig and S1C and S1D File), confirming the accumulation of effector T cells in the AT and propagating a Th1 type of response. The number of Treg cells showed an overall decrease in the spleen while increasing 4-fold in gonadal AT (Fig 4F), which is in agreement with the increased expression of FOXP3, CTLA-4 and IL-10 at the transcriptomic level (S1 Fig and S1C and S1D File). This suggests that Treg cells recruited to AT may contribute to control an excessive inflammatory response and could favor the persistence of T.brucei parasites in AT. This flow cytometry analysis in the AT is mostly consistent with changes observed in transcriptomic data (Fig 1C) and predicted by immuCC (Fig 1D). Specifically, we confirmed a similar relative variation for many immune subsets such as CD4+ T cells, CD8+ T cells, monocytes and neutrophils. Macrophages were the only population for which flow cytometry did not confirm the immuCC predictions (S2 Fig). This overestimation of the macrophage population by immuCC could be due to the fact that immune activation of adipocytes unlocks a gene expression signature that highly resembles that of macrophages[37]. Overall, during a T.brucei infection the AT accumulates cells of the innate and adaptive immunity branches. Importantly, there is a significant increase of immune cells described as protective against a T. brucei infection (i.e. IFN-γ+CD4+Th1 cells and macrophages). Adipose tissue presents a strong humoral response Effective immunity against T.brucei requires a humoral immune response in addition to a strong cellular immune response[8]. The importance of antibody-mediated T.brucei clearance PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 8 / 26 Fig 4. Dynamics of immune cells during infection. (A-F) Number of (A) neutrophils, (B) monocytes, (C) macrophages, (D) effector CD4+ T cells, (E) effector CD8+ T cells and (F) regulatory T cells in the spleen and gonadal AT. Error bars represent the SEM (n = 2–6 mice per group). Statistical analysis was performed with a two-way ANOVA using Sidak’s test for multiple comparisons. �refers to statistical differences within the groups in each time-point and the non-infected group. �, P<0.05; ��, P<0.01; ���, P<0.001; ����, P<0.0001. https://doi.org/10.1371/journal.ppat.1009933.g004 PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 9 / 26 In summary, we show that an active immune response is mounted in the AT against T.brucei, where parasite burden is controlled using the same overarching components of the systemic immune response. Whether these host-protective components are as effective in the AT as in other tissues and the blood requires further study. Methods Ethics statement All experimental animal work was performed in accordance with the Federation of European Laboratory Animal Science Associations (FELASA) guidelines and was approved by the Animal Care and Ethical Committee of the Instituto de Medicina Molecular (under the license AWB_2016_07_LF_Tropism). RNA isolation and RNA-Seq analysis Mice infected for 0, 6 and 26 days were euthanized and perfused, gonadal fat depots were collected, RNA extracted by TRIzol (Invitrogen) and its integrity assessed by TapeStation (Agilent). Poly-A mRNA library was prepared as recommended by the manufacturer (Illumina TruSeq) and the samples were sequenced in Illumina HiSeq2000 platform (EMBL and BGI). Sequenced reads were 49bp single-end for samples D0.1, D6.1, D26.1 and D26.2 and 100bp paired-end for the remaining ones. To reduce differences between single and paired-end RNA-Seq datasets, the second read of each mate-pair in paired-end samples was discarded. Also, the first read of each mate-pair was trimmed to the first 49 bases using Trimmomatic (version 0.38)[58]. Read quality was evaluated with FastQC quality control tool (version 0.11.5)[59] and raw reads were trimmed to improve mapping with SolexaQA (version 3.1.7.1)[60]. First, reads were cropped to their longest continuous segment whose PHRED score was higher than 28 and then reads smaller than 25 bp were discarded. Trimmed reads were aligned to the T.brucei TREU927 genome (TriTrypDB version 33)[61] using HISAT2 (version 2.0.0-beta)[62] without spliced alignments. Reads mapping to T.brucei were discarded and the unmapped reads aligned to the M.musculus genome (GRCm38 release 92)[63] using HISAT2 with default parameters. Unique read counts were computed using featureCounts (version 1.6.2)[64] and lowly expressed genes were discarded by keeping genes having a minimum of 10 read counts in at least 2 replicates of the same condition (D0, D6 or D26). In total, 19,488 genes were used to perform differential expression analysis by DESeq2 (version 1.18.1)[65], edgeR (version 3.20.9)[66] and limma (version 3.34.9)[67] from Bioconductor (version 3.5)[68] in the R software environment (version 3.4.4)[69]. Genes having an adjusted p-value <0.01 in at least 2 algorithms and a fold-change >2 in all were considered differentially expressed. GO term overrepresentation on differentially expressed genes was performed with the topGO (version 2.30.1)[70] R package, using the weight01 algorithm and Fisher’s exact test (pvalue <0.01) for terms with at least 5 annotated genes. Heat maps were created using the package ComplexHeatmap (version 1.17.1)[71] and ggplot2 (version 3.0.0)[72] was used to create the remaining plots. Relative immune cell compositions in infected AT were estimated with seq-ImmuCC deconvolution tool[34]. As in differential expression analysis, only genes having a minimum of 10 read counts in at least 2 replicates of the same condition were used. Then, read counts were processed following the authors script in Github[34]: the read counts of each V, D and J gene segments in both T and B cell receptors merged and a quantile normalization performed. The quantile normalized read counts were uploaded in ImmuCC server using the SVR algorithm. PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 16 / 26 RNA-Seq data from the samples of day 6 post-infection AT were made publicly available in the ArrayExpress database under the accession number E-MTAB-4061. The remaining RNA-- Seq sequence data have also been submitted to the ArrayExpress database under the accession number E-MTAB-7596. Animals In vivo experiments were performed with male C57BL/6J mice, from Charles River Laboratories International, unless otherwise stated. Rag2-deficient (Rag2 -/- ) and Jht-deficient (Jht -/- ) mice, generated on a C57BL/6J background, were obtained from Instituto Gulbenkian de Ciência (IGC, Portugal). Ifng-deficient (Ifng -/- ), generated on a C57BL/6J background, were kindly provided by Bruno Silva-Santos laboratory from Instituto de Medicina Molecular (iMM, Portugal). C3-deficient (C3 -/- ), generated on a C57BL/6J background, were kindly provided by Miguel Prudêncio laboratory from Instituto de Medicina Molecular (iMM, Portugal). All experimental mice were 7–9 weeks old, unless otherwise stated. Jht-deficient mice were 9–21 weeks old, as well as the WT controls in such experiment. Mice were housed in a Specific-Pathogen-Free barrier facility, at iMM, under standard laboratory conditions: 21 to 22˚C ambient temperature and a 12h light/12h dark cycle. Chow and water were available ad libitum. Parasite lines Experiments were performed using parasites derived from T.brucei AnTat 1.1E, a pleomorphic clone derived from the EATRO1125 strain. AnTat 1.1E 90–13 is a transgenic cell-line encoding the tetracyclin repressor and T7 RNA polymerase[73]. AnTat1.1E 90–13 GFP:: PAD1 utr derives from AnTat1.1E 90–13 in which the green fluorescent protein (GFP) is coupled to PAD1 3’UTR. Infection T.brucei cryostabilates were thawed and parasite viability by its motility was confirmed under an optic microscope. Mice were infected by intraperitoneal (i.p.) injection of 2,000 T.brucei parasites. At selected time-points post-infection, animals were euthanized by CO 2 narcosis and immediately perfused transcardially with pre-warmed heparinised saline (50mL phosphate buffered saline (PBS) with 250 μL of 5000 I.U./mL heparin). Organs were collected and either snap frozen in liquid nitrogen; used immediately to prepare single cell suspensions for flow cytometry staining; or immersion-fixed in formalin or glutaraldehyde for histopathology or electron microscopy, respectively. To block PD-1/PD-L1 axis, mice were injected intravenously (i.v.) with 300μg of αPD-1 (clone RMP1-14, InVivoMab, BioXcell), immediately before infection and at days 3, 5 and 7 post-infection. Histopathology and Electron Microscopy Formalin-fixed gonadal adipose tissue was paraffin-embedded and sectioned at 4 μm. Immunohistochemistry for the identification of trypanosomes and inflammatory cells (macrophages and T cells) was performed using a non-purified rabbit serum anti-T.brucei VSG13 antigen (cross-reactive with most T.brucei VSGs because it is not CRD-depleted, produced in-house), anti-F4/80 antibodies (Abcam, ab6640), anti-CD3 (Dako, A0452), and anti-CD138 (BD, 553712) following conventional protocols. Briefly, antigen retrieval slides was performed in PT Link module (DAKO) at low-Ph, followed by incubation with the primary antibodies. EnVision Link horseradish peroxidase/DAB visualization system (DAKO) was used and PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 17 / 26 counterstained with Harris hematoxylin. For Transmission Electron Microscopy, samples were fixed with a solution containing 2.5% glutaraldehyde (Electron Microscopy Sciences, EMS) plus 0.1% formaldehyde (Thermo Fisher) in 0.1 M cacodylate buffer (Sigma), pH7.3 for 1 h. After fixation, these were washed and treated with 0.1% Millipore filtered cacodylate buffered (Sigma), post-fixed with 1% Millipore-filtered osmium tetroxide (EMS) for 30 min, and stained en bloc, with 1% Millipore-filtered uranyl acetate (Agar Scientific). Samples were dehydrated in increasing concentrations of ethanol, infiltrated and embedded in EMBed-812 medium (EMS). Polymerization was performed at 60˚C for 2 days, and ultrathin sections were cut in a Reichert supernova microtome, stained with uranyl acetate and lead citrate (Sigma) and examined in a H-7650 transmission electron microscope (Hitachi) at an accelerating voltage of 100 kV. Electron micrographs were obtained using a XR41M bottom mount AMT digital camera (Advanced Microscopy Techniques Corp). Acquisition of immunohistochemistry images was done in a Nanozoomer-SQ (Hamamatsu Photonics) and analysed using NDP. view2 (Hamamatsu Photonics). Parasite quantification in blood and organs For parasitemia quantification, blood samples were taken daily from the tail vein and diluted 1:150. Parasites were counted manually in a Neubauer haemocytometer (0.1 mm 3 , detection limit is 3.75x10 5 parasites per mL of blood). When applicable, the total number of parasites was determined by multiplying by the total volume of blood, considering that a mouse has 58.5 mL of blood per kg of bodyweight. For parasite quantification in organs, genomic DNA (gDNA) was extracted using NZY tissue gDNA isolation kit (NZYTech, Portugal). The amount of T.brucei 18S rDNA was measured by quantitative PCR (qPCR), using the primers 5’-ACGGAATGGCACCACAAGAC–3’ and 5’–GTCCGTTGACGGAATCAACC–3’, and converted into number of parasites using a calibration curve, as previously described by Trindade et al. [15]. Number of parasites per mg of organ (parasite density) was calculated by dividing the number of parasites by the mass of organ used for qPCR. The total amount of parasites in the organ was estimated by multiplying parasite density by the total mass of the organ. Parasite burden normalized to tissue mass and corresponding organ masses are available in the S3 and S5 Files. Preparation of single cell suspensions Gonadal AT samples were incubated at 37˚C in Dulbecco’s Modified Eagle Medium (DMEM, GIBCO) with Collagenase I (0.4mg/mL, Whortington LS004196), Collagenase IV (1mg/mL, Whortington LS004188) and DNAse (10μg/mL) for 30 minutes, under 1100 rpm agitation. Single cell suspensions from the spleen, heart and digested gonadal AT were obtained by sieving them through a 40μm-pore-size nylon cell strainer (BD Biosciences) with a syringe plunger. Spleen cells were treated with erythrocyte lysis buffer (BioLegend 420301) to lyse red blood cells. Both spleen and gonadal AT cells were resuspended in complete Roswell Park Memorial Institute medium (cRPMI, RPMI supplemented with 1% sodium pyruvate 100mM, 1% MEM non-essential amino acids, 1% HEPES 1M, 1% Pen-Strep, 0.1% gentamycin 50mg/ mL, 0.1% β-mercaptoethanol 50mM and 10% fetal calf serum (all from Gibco)) to use for flow cytometry analysis. Live cells in single cell suspensions were counted after trypan blue staining in a haemocytometer. Flow cytometry Stainings of myeloid and lymphoid cells were performed separately. In isolated spleen cells, stainings were performed in 5x10 6 cells. Stainings of cells isolated from the gonadal AT were PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 18 / 26 performed with the highest number of cells possible, by diving the cell suspension equally between each staining (never exceeding 5x10 6 cells). For myeloid staining of surface determinants, cells were incubated for 45 minutes at 25˚C in cRPMI, in the presence of 5% normal mouse serum (NMS), with the following antibodies: F4/80-FITC (clone BM8, BioLegend), Ly6G-PerCP/Cy5.5 (clone 1A8, BioLegend), CD274-PE/Cy7 (PD-L1, clone 10F.9G2, BioLegend), CD11b-APC/Cy7 (clone M1/70, BioLegend), CD45-Brilliant Violet (BV) 510 (clone 30-F11, BioLegend) and Ly6C-BV605 (clone HK1.4, BioLegend). To stain non-viable cells, Zombie Violet Fixable Viability Kit (BioLegend) was used, incubating cells in PBS with 5% NMS, for 15 minutes at 25˚C. Finally, stained cells were resuspended in cRPMI for flow cytometry acquisition. For lymphoid staining, cells were first incubated in cRPMI with phorbol 12-myristate 13-acetate (20ng/mL, PMA) and ionomycin (1μg/mL) to stimulate cytokine production by T cells, for 1h45 at 37˚C. Next, to block cytokine secretion, brefeldin A (10μg/mL) and monensin (5μM), were added for the final 1h at 37˚C of incubation. Staining of surface determinants was performed by incubating cells for 45 minutes at 25˚C in cRPMI with 5% NMS and the following antibodies: CD3-FITC (clone 17A2, BioLegend), CD279-PE (PD-1, clone J43, eBioscience), CD45-BV510 (clone 30-F11, BioLegend), CD4-BV605 (clone RM4-5, BioLegend) and CD8-BV711 (clone 53–6.7, BioLegend). To stain non-viable cells, LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit (Invitrogen) was used, incubating cells in PBS with 5% NMS, for 15 minutes at 4˚C. For staining of intracellular antigens, fixation and permeabilization of cells was performed using Foxp3/Transcription Factor Staining Buffer Set (eBioscience) and the following antibodies: IFN-γ-PerCP/Cy5.5 (clone XMG1.2, BioLegend), Foxp3-APC (clone FJK-16s, eBioscience) and TNF-α-eFluor450 (clone MP6-XT22, eBioscience). Finally, stained cells were resuspended in cRPMI for flow cytometry acquisition. For staining of B cell surface determinants, cells were incubated for 45 minutes at 25˚C in cRPMI, in the presence of 5% normal mouse serum (NMS), with the following antibodies: CD45-BV510 (clone 30-F11, BioLegend), IgD-APC (clone 12-26c, eBioscience) and CD19-APC/Cy7 (clone 6D5, Biolegend). For staining of intracellular antigens, fixation and permeabilization of cells was performed using Foxp3/Transcription Factor Staining Buffer Set (eBioscience) and the following antibody Ki67-PE (clone 16A8, eBioscience). Samples were analysed on a BD LSRFortessa flow cytometer with FACSDiva 6.2 Software. All data were analysed using FlowJo software version 10.0.7r2. A schematic of the gating strategy used is represented in S2 Fig. Immune cell populations normalized to tissue mass and corresponding organ masses are available in the S3 and S4 Files. Soluble VSG isolation and identification Approximately 5x10 8 AnTat1.1E parasites cultured in HMI-11 were centrifuged at 2500g at 4˚C for 10 minutes and washed twice in trypanosome dilution buffer (TDB). Trypanosomes were then resuspended and incubated for 3 minutes at 37˚C in 3 mL of 10mM sodium phosphate (NaH 2 PO 4 ) buffer pH 8.0 containing a protease inhibitor cocktail (P8340, Sigma). Cells were then incubated for 3 minutes on an ice water bath, centrifuged at 4000g for 5 minutes and the supernatant purified through a diethylaminoethyl Sepharose column (GE Healthcare). Protein quantification was performed using a BCA Protein Assay Kit (A53225, ThermoFisher Scientific) according to the manufacturer’s instructions. The protein sample was run through SDS-PAGE, stained with Coomassie blue and the protein band of interest was isolated, destained, reduced, alkylated and digested with trypsin (Promega) overnight at 37˚C. The tryptic peptides were desalted and concentrated using POROS C18 (Empore, 3M) and eluted directly onto the MALDI plate using 1 μL of in 50% (v/v) PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 19 / 26 acetonitrile and 5% (v/v) formic acid. The data were acquired in positive reflector MS and MS/ MS modes using a 5800 MALDI-TOF/TOF (AB Sciex) mass spectrometer and using TOF/ TOF Series Explorer software v.4.1.0 (Applied Biosystems). External calibration was performed using a CalMix5 standard (Protea). The 25 most intense precursor ions from the MS spectra were selected for MS/MS analysis. The raw MS and MS/MS data were analysed using Protein Pilot software v.4.5 (ABSciex) with the Mascot search engine (MOWSE algorithm). For the search parameters were as follows: monoisotopic peptide mass values were considered, maximum precursor mass tolerance (MS) of 50 ppm and a maximum fragment mass tolerance (MS/MS) of 0.3 Da. The search was performed against the SwissProt protein sequence database without taxonomy restriction. Carboxyamidomethylation of cysteines was set as fixed modifications, oxidation of methionines and N-Pyro Glu of the N-terminal Q were set as variable modifications. Protein identification was accepted only when significant protein homology scores were obtained (P <0.05) and at least one peptide was fragmented with a significant individual ion score (P <0.05). The MS data were generated by the Mass Spectrometry Unit (UniMS), ITQB/iBET, Oeiras, Portugal. Antibody quantification Antigen-specific antibody titers were determined by ELISA. Assay plates (423501, BioLegend) were coated overnight with 2 μg/mL purified VSG AnTat1.1 (10 mM sodium phosphate buffer, pH 8.0) at 4˚C. Assay plates were blocked with blocking buffer (3% BSA in PBS with 0.05% Tween20). Secondary antibody solutions were prepared by diluting 1:1000 anti-mouse IgM-HRP (lab0372, Covalab), anti-mouse IgG-HRP (lab0365, Covalab) and anti-mouse IgAbiotin (clone RMA-1, BioLegend). Wells with biotinylated antibodies were incubated with SAv-HRP (BioLegend). Assay plates were developed with TMB substrate set (BioLegend), subsequently stopped with a 1M sulfuric acid solution and the OD 450 nm values were read in a TECAN Infinite M200 microplate reader using 570 nm as the reference wavelength. All washing steps were performed with PBS with 0.05% Tween20. The OD cut-off value used for antibody titer was determined according to the formula [Cut-off = X neg + 0.13(X pos )]sssss, where X neg is the average of the assay wells loaded with PBS and X pos is the average of the wells loaded with the most concentrated samples from mice infected for 9 days Antibody titers for AT, kidney and spleen were normalized to the organ masses used to prepare cell-free suspensions. Statistical analysis The values presented are mean ±SEM. Parasite and immune cell numbers were transformed into their respective Log base 10 values to achieve linearization prior to statistical analysis. Statistical differences were assessed using two-way ANOVA and one-way ANOVA with Sidak’s test for multiple comparisons. P values lower than 0.05 were considered to be statistically significant. Supporting information S1 Fig. Immune activatory and suppressive transcriptomic signature. Heat map of the differential expression of genes associated with immune response (A) activation and (B) suppression at days 6 and 26 post-infection relative to non-infected. Gene expression change in Log2 units is denoted in red for up-regulation and in blue for down-regulation. (TIF) S2 Fig. Gating strategy for flow cytometry analysis. (A) Myeloid gating strategy: live immune cells were gated based on positive expression of CD45 and absence of viability dye PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 20 / 26 signal. Single cells were then identified using SSC-W vs FSC-A gating. Total myeloid cells were selected based on CD11b expression and then subdivided into neutrophils and other myeloid cells based on Ly6G gating. Within the remaining myeloid cells, macrophages and monocytes were identified using F4/80 vs Ly6C gating. (B) Lymphoid gating strategy: single live immune cells were identified as described above and then T cells were identified based on the coexpression of CD3 and CD4 or CD3 and CD8. CD4+ T cells were further subdivided into conventional CD4+ T cells or regulatory T cells based on FoxP3 expression. Effector T cells were identified by gating single or dual expression of TNF-αand IFN-γwithin CD3+CD4+FoxP3or CD3+CD8+ cells (C) B cell gating strategy: Live immune cells were identified as before and then B cells were identified based on expression of CD19 and lack of CD3 expression. Activated B cells were identified based on positive Ki67 expression and absence of IgD expression. (TIF) S3 Fig. Comparison of flow cytometry and immuCC data. Variation of immune cell subsets between days 0, 6 and 26 post-infection. Data are scaled between 0 and 1, where 1 corresponds to the highest percentual value within each group. (A) B cells, (B) CD4+ T cells, (C) CD8+ T cells, (D) Macrophages, (E) monocytes, (F) neutrophils and (G) other immune cells. (TIF) S4 Fig. Organ mass and T cell dynamics. (A) Spleen and gonadal AT mass. (B) CD4+ T cells. (C) CD8+ T cells. Error bars represent the standard error of the mean (n = 2–6 mice per group). Statistical analysis was performed with a two-way ANOVA using Sidak’s test for multiple comparisons. �refers to statistical differences between the group in each time-point and the non-infected group. �, P<0.05; ��, P<0.01; ���, P<0.001; ����, P<0.0001. (TIF) S5 Fig. Purified soluble VSG identification. (A) Coomassie stained SDS-PAGE of purified soluble VSG solution denoting a single preeminent band within the predicted size range of VSG. (B) Best peptide match within the SwissProt database (sp|P06015|VSA1_TRYBB, Variant surface glycoprotein AnTaT 1.1 OS = Trypanosoma brucei brucei OX = 5702 PE = 2 SV = 1) with matching peptides depicted in bold red. (C) Summary of protein identification. Protein score is -10�Log(P), where P is the probability that the observed match is a random event. Protein scores greater than 70 are significant (p<0.05). Protein scores are derived from ions scores as a non-probabilistic basis for ranking protein hits. (D) Measurement of anti-VSG IgM titers by ELISA depicting optical density (OD) curves and antibody titer cut off determination. (TIF) S6 Fig. Assessment of the role of PD-1 expression in parasite control. Percentage of PD1+ (A) CD4+ T cells and (B) CD8+ T cells. Effect of anti-PD-1 treatment on (C) parasitemia of mice infected with T.brucei, quantified in a hemocytometer and (D) number of T. brucei parasites quantified by qPCR in spleen, gonadal AT and heart, 6 and 9 days post-infection. Error bars represent the SEM (n = 5 mice per group). Statistical analysis was performed with a twoway ANOVA using Sidak’s test for multiple comparisons. (A-B) �refers to statistical differences between groups. �, P<0.05; ��, P<0.01; ���, P<0.001; ����, P<0.0001. (TIF) S1 Table. Mapping information of RNA-Seq reads in samples from infected AT. (DOCX) S1 File. RNA-seq raw counts and differential expression analysis. (A) Raw counts and (B) reads per kilobase per million mapped reads from RNA-seq of AT from non-infected mice PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 21 / 26 (D0), n = 3, day 6 post-infection (D6), n = 3 and day 26 post-infection (D26), n = 2. Differential expression analysis between (C) D0 and D6, (D) D0 and D26 and (E) D6 and D26. (XLSX) S2 File. Significant GO terms list. GO term analysis from RNA-seq of AT from non-infected mice (D0), n = 3, day 6 post-infection (D6), n = 3 and day 26 post-infection (D26), Up-regulated GO terms in the AT between (A) D6vsD0, (B) D26vsD0, (C) D26vsD6. Down-regulated GO terms in the AT between (D) D6vsD0, (E) D26vsD0, (F) D26vsD6. (XLSX) S3 File. Effect of tissue weight on immune cell number and parasite number determination. (A) Weight of spleens and ATs analysed. Data for entire organs and normalized to tissue weight (B-C) parasite burden, (D-E) CD45+ cells, (F-G) neutrophils, (H-I) macrophages, (J-K) monocytes, (L-M) effector CD4+ T cells, (N-O) effector CD8+ T cells and (P-Q) regulatory T cells. (PDF) S4 File. Effect of tissue weight on B cell number determination. (A) Weight of spleens and ATs analysed. Data for entire organs and normalized to tissue weight (B-C) B cells and (D-E) activated B cells. (PDF) S5 File. Effect of tissue weight on parasite number determination. Weight of organs used from infected (A) Rag2 -/- ,(B) Jht -/- ,(C) Ifng -/- ,(D) C3 -/- and (E) anti-PD1 treated mice, with respective controls. Data for parasite numbers in the entire organ or normalized to tissue weight for infected (F-G) Rag2 -/- ,(H-I) Jht -/- ,(J-K) Ifng -/- ,(L-M) C3 -/- and (N-O) anti-PD1 treated mice, with respective controls. (PDF) Acknowledgments We are grateful to Bruno Silva-Santos (iMM) for the helpful discussion and for providing experimental materials. We thank Andreia Pinto, Ana Margarida Biscaia Santos and Ana Rita Pires from the Histology and Comparative Pathology Laboratory of the iMM for expert technical assistance. We also thank the staff of the Rodent facility of the iMM and Catarina Correia and Isabel Abreu of the Mass Spectrometry Unit (UniMS) of ITQB/iBET. Author Contributions Conceptualization: Tiago Bizarra-Rebelo, Karine Serre, Luisa M. Figueiredo. Data curation: Henrique Machado, Tiago Bizarra-Rebelo, Mariana Costa-Sequeira, Barbara Rentroia-Pacheco. Formal analysis: Henrique Machado, Tiago Bizarra-Rebelo, Mariana Costa-Sequeira, Barbara Rentroia-Pacheco. Funding acquisition: Karine Serre, Luisa M. Figueiredo. Investigation: Henrique Machado, Tiago Bizarra-Rebelo, Sandra Trindade, Ta ˆnia Carvalho, Filipa Rijo-Ferreira, Karine Serre. Methodology: Tiago Bizarra-Rebelo, Mariana Costa-Sequeira, Ta ˆnia Carvalho, Barbara Rentroia-Pacheco. PLOS PATHOGENS Adipose tissue immunity PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1009933 September 15, 2021 22 / 26 Project administration: Karine Serre, Luisa M. Figueiredo. Resources: Karine Serre, Luisa M. Figueiredo. Software: Mariana Costa-Sequeira, Barbara Rentroia-Pacheco. Supervision: Karine Serre, Luisa M. Figueiredo. Validation: Henrique Machado, Tiago Bizarra-Rebelo, Karine Serre, Luisa M. Figueiredo. Visualization: Henrique Machado, Tiago Bizarra-Rebelo, Mariana Costa-Sequeira. Writing – original draft: Henrique Machado, Tiago Bizarra-Rebelo, Ta ˆnia Carvalho, Karine Serre, Luisa M. Figueiredo. 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