Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a major global health problem. Lung granulomas are organized structures of host immune cells that function to contain the bacteria. Cytokine expression is a critical component of the protective immune response, but inappropriate cytokine expression can exacerbate TB. Although the importance of proinflammatory cytokines in controlling M. tuberculosis infection has been established, the effects of anti-inflammatory cytokines, such as IL-10, in TB are less well understood. To investigate the role of IL-10, we used an Ab to neutralize IL-10 in cynomolgus macaques during M. tuberculosis infection. Anti–IL-10–treated nonhuman primates had similar overall disease outcomes compared with untreated control nonhuman primates, but there were immunological changes in granulomas and lymph nodes from anti–IL-10–treated animals. There was less thoracic inflammation and increased cytokine production in lung granulomas and lymph nodes from IL-10–neutralized animals at 3–4 wk postinfection compared with control animals. At 8 wk postinfection, lung granulomas from IL-10–neutralized animals had reduced cytokine production but increased fibrosis relative to control animals. Although these immunological changes did not affect the overall disease burden during the first 8 wk of infection, we paired computational modeling to explore late infection dynamics. Our findings support that early changes occurring in the absence of IL-10 may lead to better bacterial control later during infection. These unique datasets provide insight into the contribution of IL-10 to the immunological balance necessary for granulomas to control bacterial burden and disease pathology in M. tuberculosis infection.

This article is featured in In This Issue, p.475

Tuberculosis (TB) continues to be a major cause of death worldwide, with 1.6 million deaths from TB in 2017 alone (1). Despite decades of research, the nuances of the complex relationship between the host response and Mycobacterium tuberculosis remain unclear. In the best-case scenario, host immune cells coordinate their responses to contain the bacteria in a delicate balance of killing M. tuberculosis or halting bacterial spread without causing excessive disease pathology. After M. tuberculosis bacilli are inhaled, they are phagocytosed by alveolar macrophages, and a subsequent influx of other innate immune cells, such as neutrophils and dendritic cells, are recruited to the site of infection in the lung parenchyma to assist in containing M. tuberculosis (25). Once adaptive immune responses are initiated in the lung-draining lymph nodes (LNs), T cells and B cells traffic to the site of infection to assist innate immune responses (610). Together, these host immune cells form an organized structure of a granuloma that acts to kill or contain the bacteria, preventing further bacterial spread. Both innate and adaptive immune cells and their cytokine responses have been established to be critical for control and clearance of M. tuberculosis (reviewed in Refs. 1114). However, not all granulomas are successful at killing or containing the infection, and there is extensive variability among immune responses and infection outcome at the granuloma level, even in a single host (15).

The host immune responses early in infection are critical for eventual disease outcome. Humans infected with M. tuberculosis have clinical signs of early initial inflammatory responses (initial fevers and elevated erythrocyte sedimentation rates) and LN involvement (hilar adenopathy) usually within the first 2 mo of infection (16, 17). Studies in nonhuman primates (NHPs) and computational modeling also support the importance of these early immune events on eventual disease outcome, with robust bacterial killing in granulomas only after ∼10 wk of infection (1820). Many host immune cells and their cytokine responses have been important for control of M. tuberculosis infection. In particular, proinflammatory cytokines produced by T cells, such as IFN-γ, TNF, and IL-17, have been shown in humans, NHPs, and mice to be critical for optimal control of M. tuberculosis (13, 14, 2132). However, uncontrolled inflammatory cytokine responses from these immune cells can also cause damage to tissues, leading to increased inflammation and exacerbation of TB disease.

The immune response has many checks and balances to counter a proinflammatory response and prevent damage. One major factor that can prevent tissue damage is IL-10, a cytokine with anti-inflammatory properties (33). IL-10 can be produced by many immune cells, including monocytes, macrophages, dendritic cells, B cells, NK cells, mast cells, and nearly all T cell subsets (34). Dysregulation of IL-10 has been associated with negative disease outcomes in bacterial and viral infections and autoimmune or inflammatory diseases (33, 35, 36).

Several studies have investigated the role of IL-10 in M. tuberculosis infections, but the cumulative results are inconclusive. Elevated levels of IL-10 in patients have been suggested to be associated with more active or severe cases of TB (3739), and blocking IL-10 improved Th1 and macrophage function in in vitro studies (40, 41). Mice impaired for IL-10 expression or function have improved bacterial control and enhanced survival during M. tuberculosis infections (4246), whereas overexpressing IL-10 increased bacterial burden and exacerbation of the disease (4749). In contrast, other studies in mice indicated that lack of IL-10 had insignificant effects on the bacterial burden during M. tuberculosis infections but resulted in detrimental immunopathology and decreased survival in some studies (5053). Although many of these studies indicate that IL-10 during M. tuberculosis infection may be restricting Th1 function and preventing a sufficient immune response that reduces bacterial burden, some of these studies also implicate IL-10 in preventing or reducing immunopathology. These differences may depend on the relative susceptibility and intensity of IL-10 response to M. tuberculosis infection in the mouse strains used in these studies. Further contributing to these contradictory results, some studies suggest that IL-10 may have differential effects depending on the stage of M. tuberculosis infection. Blocking IL-10 later in infection (after 3 mo) partially reduced bacterial burden (42), but transiently blocking IL-10 during the first month of infection had even greater effects on the long-term control of M. tuberculosis infection (43). Most of these studies were based on whole-organ or whole-animal outcomes, and there are few studies of individual TB granulomas. Computational modeling of TB granulomas indicated that IL-10 within the granulomas was necessary to reduce excessive inflammation (20). In granulomas from M. tuberculosis–infected macaques, T cells expressing IL-10 in conjunction with T cells expressing proinflammatory cytokines, such as IL-17, were associated with improved sterilization of M. tuberculosis (15).

To address the role of IL-10 in an animal model that recapitulates all aspects of human M. tuberculosis infection, including pathology, we used a macaque-specific, anti–IL-10–neutralizing Ab to manipulate the levels of active IL-10 in vivo during M. tuberculosis infection of cynomolgus macaques. This provided the opportunity to address the role of IL-10 at critical, early time points postinfection as the immune cells establish in the lungs and form granulomas. We found that IL-10 neutralization reduced early inflammation, as assessed by positron emission tomography–computed tomography (PET-CT) imaging, in a subset of granulomas, which resulted in lower bacterial burden in that granuloma subset. However, total thoracic bacterial burden was not significantly different between IL-10–neutralized and control animals. IL-10 neutralization also resulted in increased levels of some cytokines in lung granulomas and thoracic LNs at 4 wk of M. tuberculosis infection. However, by 8 wk postinfection, the cytokine responses in lung granulomas and LNs decreased or stabilized. IL-10 neutralization also increased collagenization and fibrosis in lung granulomas by 8 wk postinfection. Using computational modeling of TB granulomas to extend the time frame of our study, we determined that these early cytokine changes and increased collagenization may lead to improved bacterial control during long-term M. tuberculosis infection. Thus, IL-10 neutralization influenced the cytokine response in lung granulomas and LNs early in M. tuberculosis infection but did not affect overall bacterial burden or disease. However, reduced IL-10 may set the stage for better bacterial control over a longer infection period.

All experimental manipulations, protocols, and care of the animals were approved by the University of Pittsburgh School of Medicine Institutional Animal Care and Use Committee (IACUC). The Division of Laboratory Animal Resources and IACUC adheres to national guidelines established in the Animal Welfare Act (7 U.S. Code Sections 2131–2159) and the Guide for the Care and Use of Laboratory Animals (Eighth Edition) as mandated by the U.S. Public Health Service Policy. All macaques used in this study were housed at the University of Pittsburgh in rooms with autonomously controlled temperature, humidity, and lighting. Animals were singly housed in caging at least 2 m2 apart that allowed visual and tactile contact with neighboring conspecifics. The macaques were fed twice daily with biscuits formulated for NHPs, supplemented at least 4 d/wk with large pieces of fresh fruits or vegetables. Animals had access to water ad libitum. Because our macaques were singly housed because of the infectious nature of these studies, an enhanced enrichment plan was designed and overseen by our NHP enrichment specialist. This plan has three components. First, species-specific behaviors are encouraged. All animals have access to toys and other manipulata, some of which will be filled with food treats (e.g., frozen fruit, peanut butter). These are rotated on a regular basis. Puzzle feeders, foraging boards, and cardboard tubes containing small food items also are placed in the cage to stimulate foraging behaviors. Adjustable mirrors accessible to the animals stimulate interaction between animals. Second, routine interaction between humans and macaques are encouraged. These interactions occur daily and consist mainly of small food objects offered as enrichment and adhere to established safety protocols. Animal caretakers are encouraged to interact with the animals (by talking or with facial expressions) while performing tasks in the housing area. Routine procedures (e.g., feeding, cage cleaning) are done on a strict schedule to allow the animals to acclimate to a routine daily schedule. Third, all macaques are provided with a variety of visual and auditory stimulation. Housing areas contain either radios or TV/video equipment that play cartoons or other formats designed for children for at least 3 h/d. The videos and radios are rotated between animal rooms so that the same enrichment is not played repetitively for the same group of animals. All animals are checked at least twice daily to assess appetite, attitude, activity level, hydration status, etc. Following M. tuberculosis infection, the animals are monitored closely for evidence of disease (e.g., anorexia, weight loss, tachypnea, dyspnea, and coughing). Physical exams, including weights, are performed on a regular basis. Animals are sedated prior to all veterinary procedures (e.g., blood draws) using ketamine or other approved drugs. Regular PET-CT imaging is conducted on most of our macaques following infection and has proved very useful for monitoring disease progression. Our veterinary technicians monitor animals especially closely for any signs of pain or distress. If any are noted, appropriate supportive care (e.g., dietary supplementation and rehydration) and clinical treatments (analgesics) are given. Any animal considered to have advanced disease or intractable pain or distress from any cause was sedated with ketamine and then humanely euthanatized using sodium pentobarbital.

The neutralizing anti–IL-10 Ab was constructed using the H chain and L chain variable regions of the humanized, anti-human IL-10 Ab hu12G8 based on sequences from (54). This Ab showed similar affinity for human and macaque IL-10 (54). A macaque recombinant form of this Ab was expressed in Chinese hamster ovary cells as full-length Ig with rhesus IgG1 and κ constant regions. A silencing mutation, L235A, L236A, was introduced into the rhesus H chain C region to limit the Ab’s effector function to cytokine neutralization.

The Ab was manufactured by shake flask production using stable-expressing Chinese hamster ovary cells in chemically defined, animal component–free medium and purified using protein A affinity chromatography. Cells were grown in 3-l shake flasks using proprietary media from Life Technologies. For each cell line, the following specifications were followed: 37°C and 110 RPM for agitation (pH 7). The glucose concentration was monitored and maintained at a glucose concentration of 4.1 g/l. The glucose was monitored with Nova bioanalyzer 400 (Nova Biomedical, Deeside, U.K.). A proprietary feed was given every day starting on day 3 to 14. On day 14, the flasks were harvested. Clarification Depth Filters were used to clarify the 10-l cultured volume. The coarse filter used was Millistak D0HC (0.6–9-μm nominal pore size) and size of 0.053 m2. The fine filter used was ×0HC (<0.1-μm nominal pore size; MilliporeSigma) and size of 0.054 m2. Depth filters were assembled in series and flushed with PBS (pH 7.4). Ten liters of culture was loaded through the filters, and pressure was recorded. The pressure limit cutoff was 25 PSI for the whole system in series. Finally, the clarified material was filtered with AcroPak 1500 Capsules with Supor Membrane 0.8/0.2 μm. For protein A affinity chromatography, using an AKTA pure 25 l, the anti–IL-10 Ab was purified. A 150-ml MAbSelect–packed column was used for the purification process. The column was equilibrated with binding buffer PBS (pH 7.4). The clarified material was loaded through the column. The column was then washed with PBS (pH 7.4) for five column volumes (750 ml). Finally, the column was eluted with 20 mM citrate (pH 3) for ∼2 column volumes (300 ml). To neutralize the eluate, 10% of the total volume was used. The neutralization buffer added was Trizma hydrochloride solution (pH 8) (1 M; BioReagent, Sigma Life Science). For ultrafiltration and diafiltration, purified Ab was concentrated and buffer exchanged using Pellicon XL50 with Biomax 30 kDa Membrane (A screen, 50 cm2). The product was concentrated up to 10 mg/ml and diafiltrated five times to a final formulation buffer of 20 mM citrate and 150 mM NaCl (pH 6). The concentration was measured using A280 spectrum with NanoDrop 2000/2000c Spectrophotometers. The extinction coefficient used at 280 nm for IL-10 Ab was 1.44. For quality control, Ab was measured for percentage monomer using Superdex 200 size-exclusion column with UV detection at 280 nm. A >90% monomer was verified and tested for endotoxin. The final purified material was tested for endotoxin using Endosafe Portable Testing System (Charles River Laboratories). The final product passed the internal criteria of less than 5 endotoxin units/kg, which is a level adopted from Food and Drug Administration guidelines. The final product was formulated in 20 mM sodium citrate buffer (pH 6), 150 mM sodium chloride, and 0.02% Tween 80.

D36 mouse mast cells (American Type Culture Collection, Manassas, VA) were cultured in IMDM (Sigma-Aldrich, St. Louis, MO) containing 10% FBS, 1% HEPES, 1 mM sodium pyruvate, and 1% l-glutamine in the presence of 1 ng/ml IL-3 and 1 ng/ml IL-4. Cells were washed in PBS, counted by trypan blue exclusion, and resuspended in RPMI media (Sigma-Aldrich) containing 1% HEPES and 1% l-glutamine before resting for 3 h at room temperature. Following rest, cells were counted again by trypan blue exclusion and aliquoted in sterile, 96-well, flat-bottom plates at 100,000 cells per well in 100 μl. Rhesus macaque IL-10 (kindly provided by Dr. F. Villinger) was added to appropriate wells at a concentration of 20 ng/ml. Macaque IL-10–specific Ab (prepared as described in the previous section) was added to appropriate wells at varying concentrations (1–100 μg/ml). Samples were incubated for 18 h at 37°C. Tritiated thymidine (PerkinElmer, Waltham, MA) was diluted in AIM V Medium (Life Technologies, Gaithersburg, MD) to a concentration of 20 μCi/ml and added to all samples before incubation for another 6 h at 37°C. Cells were harvested onto glass fiber filters (PerkinElmer) before incorporated radioactivity was determined by liquid scintillation counting (TopCount; PerkinElmer).

Excised lung granulomas from cynomolgus macaques (Macaca fascicularis) designated for other studies were homogenized into a single-cell suspension as previously described (55). Cell pellets were used for flow cytometry, and supernatants were stored at −80°C until used for ELISAs. Cell pellets were resuspended in assay media (RPMI 1640 with 1% HEPES, 1% l-glutamine, and 10% human AB serum) and equally divided into two wells in a sterile, 96-well, round-bottom plate. Samples were washed with additional assay media. Sample halves designated for anti–IL-10 Ab neutralization treatment were resuspended in media containing previously described anti–IL-10 Ab diluted in assay media for concentration of 10 μg/ml (IL-10R1–LALA; Massachusetts Biotechnology Council). Corresponding sample halves designated as media controls were resuspended in equal volumes of assay media. Samples were incubated overnight at 37°C. Following overnight incubation, samples were stimulated with peptide pools of M. tuberculosis–specific Ags ESAT-6 and CFP-10 (10 μg/ml of each peptide) and brefeldin A (GolgiPlug; BD Biosciences, San Jose, CA) for 3.5–4 h at 37°C. Samples were then stained for viability (Invitrogen), cell surface markers, and intracellular cytokine markers. Cells were identified as T cells, with Abs against CD3 (clone SP34-2), CD4 (clone L200), and CD8 (clone SK1). Following permeabilization using Fixation/Permeabilization Solution (BD Biosciences), cytokine and cellular response were identified using Abs against IFN-γ (clone B27), IL-2 (clone MQ1-17H12), TNF (clone MAB11), IL-17 (clone eBio64CAP17), IL-10 (clone JES3-9D7), and Ki67 (clone B56). Data acquisition was performed using an LSR II (BD Biosciences) and analyzed using FlowJo software v.9.7 (Tree Star, Ashland, OR).

Stored supernatants were thawed and filtered using 0.22-μm syringe filter into ∼1-ml aliquots immediately before assay to prevent freeze–thaw cycles. Concentrations of IL-10 were detected using an NHP-specific IL-10 ELISA (U-CyTech Biosciences, Utrecht, the Netherlands) according to the manufacturer’s directions. Samples were measured at OD 450 on a spectrophotometer (SpectraMax; Molecular Devices, San Jose, CA).

Fourteen male cynomolgus macaques (M. fascicularis) between 5.7 and 8 y of age, with starting weights of 3.9–6.9 kg (Valley Biosystems, Sacramento, CA) were housed within Biosafety Level 3 facilities as previously described (8, 28, 56) (IACUC protocol 16017309). Additional historical control samples from male animals from a previous study (57) (IACUC protocol 15066174) of similar age and weight infected with M. tuberculosis and necropsied at 4 wk were also used in our analyses and differentiated as gray markers when included.

To assess the role of IL-10 in M. tuberculosis infections in our NHP model of M. tuberculosis infections, we designed an in vivo study using our anti–IL-10–neutralizing Ab. Macaques were sedated and given acetaminophen and diphenhydramine ≥30 min prior to infusion in both anti–IL-10 treatment and saline control groups. To neutralize IL-10 before the M. tuberculosis infection, macaques that were randomly designated for the anti–IL-10 treatment group were then infused with anti–IL-10 Ab (see above in 2Materials and Methods) diluted in saline at 15 mg/kg over the course of 20–30 min, with predosaging, interim, and postdosaging vitals recorded, starting 1 d before M. tuberculosis infection. We chose a dosage of 15 mg/kg of anti–IL-10 Ab based on unpublished data from a similar human Ab in cynomolgus macaques, which indicated effectiveness without adverse side effects (David Sacks, personal communication). In two uninfected animals, infusion of 15 or 30 mg/kg once resulted in anti–IL-10 Ab levels in serum at 15 d of 29 and 24 μg/ml, respectively. Following the initial infusion prior to M. tuberculosis infection, to maintain IL-10 neutralization, macaques were infused with 15 mg/kg of Ab every 10 d (±3 d) using the same procedure methods until necropsy (4–8 wk after initial infusion). Macaques randomly designated for the control group were sedated and treated the same as the anti–IL-10 treatment group, except infusions with 50 ml of saline over 20–30 min. Three macaques were designated for the anti–IL-10 Ab treatment, and one macaque was designated for the saline control group for the short 4-wk study; four historical controls were included in the analysis. Five macaques were designated for the anti–IL-10 Ab treatment, and five macaques were designated for the saline control group for the longer 8-wk study.

Animals were infected with 9–19 CFU of M. tuberculosis Erdman strain to the lower lung lobe by bronchoscopic instillation. Infection was confirmed, and serial clinical, immunologic, and radiographic examinations were conducted during the course of the study as previously described (8, 18, 19, 55, 58, 59). Specifically, blood was serially drawn immediately prior to infusions, and TB granuloma formation and progression were tracked by serial 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) PET-CT imaging and their relative metabolic activity (FDG activity) as a proxy for inflammation measured as 18F-FDG standard uptake values (SUV) normalized to muscle (59).

After 4 or 8 wk of infection and infusions, necropsy of study animals was performed as previously described (8, 28, 55, 56, 58). In summary, 1–3 d prior to necropsy, individual granulomas were identified by 18F-FDG PET-CT scans. At necropsy, NHPs were maximally bled and humanely sacrificed using pentobarbital and phenytoin (Beuthanasia; Schering-Plough, Kenilworth, NJ). During necropsy, gross disease pathology was quantified by a scoring system of number, size, and pattern of lung granulomas and extent of disease involvement in lung lobes, mediastinal LNs, and visceral organs, as previously described (8). LNs and PET-CT–matched lung granulomas were excised for bacterial burden, immunological studies, and histological analyses. Representative sections of LNs and lung granulomas were homogenized into single-cell suspensions for bacterial quantification, flow cytometry, and multiplex immunoassays. Representative sections of lung granulomas were also formalin-fixed for immunohistochemistry and histology.

Bacterial burden in individually excised granulomas, LNs, representative lung lobe tissue, spleen, and liver were quantified by serial plating onto 7H11 medium and CFU enumerated after 21 d of incubation at 37°C. Total thoracic CFU is calculated from summation of all lung, lung granuloma, and thoracic LN–plated samples (60).

Single-cell suspensions of homogenized tissues were incubated in the presence of brefeldin A (GolgiPlug; BD Biosciences) for 2.5–3 h at 37°C. Samples were washed with PBS and stained for viability (Invitrogen), immune cell subsets, and intracellular cytokine markers. Cells were identified as T cells: CD3 (clone SP34-2), CD4 (clone L200), and CD8 (clone SK1); B cells: CD20 (clone 2H7); and macrophages and neutrophils: CD11b (clone ICRF44), CD163 (clone eBioGH1/61), and calprotectin (clone 27E-10). Because of limitations in the panel, CD3+CD4 cells were used as an approximation for CD3+CD8+ T cells. Following permeabilization using FOXP3 staining kit (eBioscience), cytokine and cellular response were identified using Abs against CD69 (clone TP1.55.3), IFN-γ (clone B27), IL-2 (clone MQ1-17H12), TNF (clone MAB11), IL-17 (clone eBio64CAP17), IL-10 (clone JES3-9D7), IL-6 (clone MQ2-6A3), and FOXP3 (clone PCH101). Data acquisition was performed using a LSR II (BD Biosciences) and analyzed using FlowJo software v.9.7 (Tree Star). Gating strategy is found in Supplemental Fig. 1A.

Total cytokine concentrations in lung granulomas and LNs were quantified using multiplex immunoassays. Supernatants from homogenized excised tissues stored at −80°C until time of assay. Supernatants were filtered using a 0.22-μm syringe filter into ∼1-ml aliquots immediately before storage at −80°C or immediately before time of assay to minimize freeze–thaws. Cytokine and chemokine levels in thawed samples were measured by multiplex immunoassays specific for NHPs (ProcartaPlex; Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instructions. Immunoassay results were collected and analyzed using Bio-Rad Bio-Plex 200 (Bio-Rad Laboratories, Hercules, CA).

A subset of tissues excised during necropsy, including lung granulomas and LNs, were fixed in 10% neutral-buffered formalin before placing in histology cassettes and paraffin-embedding. Sections of 5-μm-thick tissues were cut and mounted on SuperFrost Plus Slides (Thermo Fisher Scientific) and stained with H&E by the University of Pittsburgh’s in situ histology laboratory. Structural makeup of lung granulomas and extent of TB disease in LNs were examined and recorded by a veterinary pathologist who was blinded to treatment groups. Granulomas with histopathologic characteristics described with the terms “collagen,” “fibrosis,” or “fibroblasts” were scored as fibrotic.

For collagen and fibrosis staining, a subset of granulomas was randomly selected for staining and analysis. Formalin-fixed, paraffin-embedded tissue sections from animals included in this study were deparaffinized and rehydrated in sequential incubations in xylene, 95% ethanol, and 70% ethanol. Tissue sections were then incubated in a pressure cooker containing Tris-EDTA (pH 9) Ag retrieval buffer for 6 min under pressure. Samples were then depressurized and allowed to cool slowly over 30 min before washing in PBS. Samples were incubated in blocking buffer (1% BSA in PBS) for 15–30 min in a dark humidified chamber before addition of primary Ab cocktails diluted in blocking buffer to sections for overnight incubation at 4°C in a dark humidified chamber. Primary Abs that required biotinylated secondaries for detection were incubated in blocking buffer (1% BSA in PBS) for 15 min, followed by incubation in an endogenous avidin-biotin blocking kit according to the manufacturer’s instructions (Abcam, Cambridge, MA).

Abs used in this study against CD3 (clone CD3-12, 1:200 dilution), collagen I (rabbit polyclonal, 1:100 dilution), SMAD3 (phospho S423 + S425) (clone EP823Y, 1:100 dilution), and STAT6 (phospho Y641) (rabbit polyclonal, 1:50 dilution) were from Abcam; CD11c (clone 5D11, 1:30 dilution) from Leica Microsystems (Buffalo Grove, IL); CD20 (clone L26, 1:100 dilution) from Agilent Dako (Santa Clara, CA); IL-10Rα (polyclonal, 1:150 dilution) from MilliporeSigma (Temecula, CA); vimentin (chicken polyclonal, 1:100 dilution) from Novus Biologicals (Centennial, CO); CD68 (clone KP1, 1:30 dilution), α–smooth muscle actin (α-SMA; clone 1A4, 1:100 dilution), and CD163 (clone 10D6, 1:30 dilution) from Thermo Fisher Scientific. After incubation with primary Abs, sections were washed four times in PBS and incubated with secondary Abs for 1 h at room temperature in a dark humidified chamber. Secondary Abs against primary host species were purchased from Jackson ImmunoResearch Laboratories (West Grove, PA) or Thermo Fisher Scientific. Zenon label kits (Thermo Fisher Scientific) were used according to the manufacturer’s instructions and biotinylated secondary Abs were from BD Biosciences. Specificity of Abs were confirmed using no-primary controls using the same staining and imaging protocol on serial sections. After secondary Ab incubation, slides were washed four times in PBS and mounted using ProLong Gold mounting medium with DAPI (Life Technologies, Eugene, OR). Slides were cured at room temperature for at least 24 h and subsequently stored at −20°C. Slides were imaged using a Nikon immunofluorescent microscope with a scanning stage and saved as TIFF Format images.

Statistical analyses were conducted in GraphPad Prism 8 (GraphPad Software, San Diego, CA). Data were tested for normality by D’Agostino–Pearson omnibus normality test. Because data analyzed in this study were not normally distributed, Mann–Whitney U tests were used to compare two independent groups or Kruskal–Wallis tests with Dunn multiple comparison adjustment were used to compare three or more groups. For multiple comparisons, comparisons were made between the following groups: 4-wk anti–IL-10 versus 4-wk control, 8-wk anti–IL-10 versus 8-wk control, 4-wk anti–IL-10 versus 8-wk anti–IL-10, and 4-wk control versus 8-wk control. Wilcoxon signed-rank test was used to analyze matched pairs data. Fisher exact test was used to analyze contingency tables. Any p values ≤ 0.05 were considered significant; p values ≤ 0.1 or those with statistically significant Kruskal–Wallis p values were noted in individual graphs.

All simulations use our two-dimensional hybrid agent-based model (ABM), GranSim (61, 62). The model captures environmental, cellular, and bacterial dynamics across molecular, cellular, and tissue-scale events. GranSim has been calibrated extensively to data from an NHP model of TB and used to simulate a range of outcomes at the granuloma scale (20, 63, 64). At the molecular scale, GranSim incorporates cytokine and chemokine diffusion, secretion, and degradation. GranSim also tracks individual immune cells on a two-dimensional simulation grid of microcompartments, including four macrophage states (resting, activated, infected, and chronically infected) and T cell types (cytotoxic, IFN-γ producing, and regulatory), fibroblasts, and myofibroblasts. Granuloma formation at the tissue level is an emergent behavior of GranSim. See http://malthus.micro.med.umich.edu/GranSim for full model details and an executable file. We previously published model of fibrosis in the granuloma (64). In this study, we updated the fibrosis model to include a new rule regarding collagen, which is only produced by myofibroblasts. In this version of GranSim, collagen blocks the migration and recruitment of cells into that specific grid compartment to more accurately capture the effect of fibrosis in the granuloma. The model was recalibrated to exhibit the expected phenomena with this new rule in place.

GranSim is constructed through use of the C++ programming language, Boost libraries (distributed under the Boost software license; https://www.boost.org), and the Qt framework for visualization (distributed under General Public License). The ABM is cross-platform (Macintosh, Windows, and Unix) and runs with or without visualization software. GranSim model simulations were performed on the Extreme Science and Engineering Discovery Environment supercomputer (see the title page footnote about grant support).

We used uncertainty and sensitivity analysis techniques to explore model parameter space. In particular, we used Latin hypercube sampling (LHS) (65, 66) to generate 1000 parameter sets by varying a range of input parameters that are listed in Supplemental Table II. We then simulated the model with each parameter set for three replications with IL-10 present and three with IL-10 removed from the system for a total time of 150 d, yielding 6000 virtual granulomas, 3000 with IL-10 and 3000 without. The parameter sets resulted in a range of biologically feasible IL-10 concentrations in lungs as well as a virtual library of in silico granulomas that are representative in the range of what we see in vivo (reducing the total number to 636 granulomas). We performed virtual IL-10 depletion experiments (IL-10 knockouts [KO]) by setting all of the secretion rates of IL-10 to zero at the start of the simulation run. In our analysis, simulations were grouped by the concentration of IL-10 on the grid at the 150-d time point using quartiles. To identify important mechanisms driving the outcomes of interest we performed a sensitivity analysis using partial rank correlation coefficients (PRCCs). Correlations were calculated longitudinally to assess the correlation between parameters and outcomes of interest using the spartan package for R (67, 68). PRCCs represent correlations between −1.0 and 1.0, with higher magnitudes representative of higher correlations.

The role of IL-10 in M. tuberculosis infections has been difficult to discern because of experimental model variability and technical difficulties in studying IL-10 at the local sites of infection in TB patients. To better understand IL-10 in TB lung granulomas, the primary site of infection, we used a cynomolgus macaque model of M. tuberculosis that recapitulates granuloma structure and disease pathology observed in humans. We developed an Ab to specifically bind to macaque IL-10 (see 2Materials and Methods), which neutralizes active IL-10 in vitro (Fig. 1A). As an assay for IL-10 activity, we used D36 mouse mast cells that are IL-10 dependent for their proliferation (i.e., the addition of 20 ng/ml rhesus macaque IL-10 increases cell proliferation) (69). The anti–macaque IL-10 Ab added to the rhesus macaque IL-10 at varying concentrations reduced D36 mast cell proliferation to baseline levels, demonstrating neutralization of macaque IL-10 by this Ab. We used this anti–IL-10 Ab for all experiments in this study to investigate whether IL-10 had an effect on immune cell function in TB granulomas.

Immune responses in individual granulomas, even from the same macaque, are extremely variable (15) and not all have detectable levels of IL-10 (Fig. 1B); thus, we first aimed to understand whether neutralization of IL-10 affects T cell responses in lung granulomas ex vivo. Single-cell suspensions of individual granulomas were either divided and treated with anti–IL-10 Ab or used as a media only control. In granulomas with detectable levels of IL-10 by ELISA, incubation with anti–IL-10 Ab ex vivo significantly increased the frequency of CD3+ T cells producing IL-2 compared with the media control (Fig. 1C), with a trend toward increased IFN-γ and IL-17 T cell responses, suggesting that IL-10 in granulomas may suppress proinflammatory cytokine responses.

To confirm and expand upon our ex vivo IL-10 neutralization results, we conducted an in vivo IL-10 neutralization study (Fig. 1D). We neutralized IL-10 during either the first 4 or 8 wk of infection, beginning 1 d before M. tuberculosis infection and maintaining IL-10 neutralization with infusions of anti–IL-10 Ab every 10 d. A dosage of 15 mg/kg was chosen based on unpublished data from a similar human Ab in cynomolgus macaques, which indicated effectiveness without adverse side effects (David Sacks, personal communication). Lack of IL-10 can cause intestinal inflammation and colitis in human genetic studies and animal models (7072). The M. tuberculosis–infected cynomolgus macaques treated with the anti–IL-10 Ab in this study did not have changes in stool, and histologic examination of intestines revealed no abnormal findings.

We tracked infection progression and inflammatory response in the lungs over the course of M. tuberculosis infection using PET-CT imaging with 18F-FDG as a marker of inflammation (59). Overall lung inflammation (total FDG activity) was similar between the anti–IL-10–treated animals and the untreated control animals (Fig. 2A), and a similar number of granulomas were found by PET-CT at 4 wk postinfection in both groups, indicating no difference in establishment of infection. Although both groups had a range of FDG avidity in individual granulomas, a significantly higher frequency of granulomas from the IL-10–neutralized animals had very low FDG avidity (<5 SUV ratio [SUVR]) at the 3–4-wk time points (anti–IL-10: 11/58 [19.0%] FDG < 5 SUVR granulomas in the 8 wk infection cohort; versus control: 3/54 [5.6%]; Fisher exact test p = 0.0445) (Fig. 2B). By 8 wk postinfection, the control granulomas had returned to lower FDG activity values, more comparable to the granulomas in the anti–IL-10 group, which did not change substantially between 4 and 8 wk postinfection (Fig. 2B). Because 18F-FDG is a marker of metabolic activity and a general indicator of inflammation (58, 73), these results suggest that IL-10 may be indirectly or directly influencing inflammation at an early time point in a subset of granulomas.

We assessed the granulomas with early (3–4 wk) low SUVR (Fig. 2B) for bacterial burden at 8 wk (necropsy) and found a significantly lower CFU/granuloma in the subset of low SUVR granulomas in both IL-10–neutralized and control groups as compared with granulomas with SUVR ≥ 5 (Fig. 2C). This suggests that IL-10 neutralization had early effects on reducing inflammation in granulomas, which resulted in lower CFU in that subset of lesions by 8 wk. However, these observed differences in a subset of individual granulomas did not translate to differences in overall infection outcome. Anti–IL-10–treated and –untreated animals had similar gross pathology at 4 and 8 wk postinfection (Fig. 2D). Total thoracic (lung plus thoracic LN) bacterial burden, as well as bacterial burden within all individual granulomas, was also similar between the two groups at both 4 and 8 wk postinfection (Fig. 2E, 2F).

Frequencies of cell populations and functionalities in lung granulomas were assessed by flow cytometry to determine whether neutralization of IL-10 influenced the local immune environment. Overall, the cell populations within granulomas were similar between the two groups at 4 and 8 wk postinfection (Fig. 3). There were no differences between IL-10–neutralized and control granulomas in terms of regulatory T cell populations (CD4+FOXP3+) and activated T cells (CD69+), with similar changes between 4 and 8 wk within each group.

Because neutralization of IL-10 in TB granulomas ex vivo increased T cell cytokine responses, particularly IFN-γ, IL-2, and IL-17, we assessed whether anti–IL-10 Ab infusions in vivo also increased T cell cytokine responses. Whereas Th1 (IFN-γ+ and/or TNF+) and IL-2+ T cell frequencies increased over infection time, there were no significant differences between anti–IL-10–treated and –untreated animals (Fig. 3). As we had observed ex vivo, granulomas from anti–IL-10–treated animals had higher frequencies of IL-17–producing T cells compared with granulomas from control animals 8 wk after M. tuberculosis infection. Similar to our ex vivo results, the increase in CD3+ IL-17+ frequency was not consistent for all granulomas, suggesting that IL-10 may be affecting Th17 cells in a subset of TB granulomas.

Because there were minimal differences in the T cell populations or their cytokine responses within lung granulomas (with the exception of IL-17), we sought to characterize which cells in TB granulomas express IL-10R and would be responsive to IL-10. Immunohistochemistry revealed that the majority of cells in granulomas expressing IL-10R were not CD3+ T cells (Fig. 4A, 4B). Instead, IL-10R colocalized with CD11c+ macrophage clusters on the outer edge of the granuloma macrophage region and with CD20+ B cells in the lymphocyte cuff (Fig. 4A–C). Although IL-10 has been reported to have immunosuppressive effects on T cells (which may be indirect), it is possible that the T cells in granulomas are not the primary target of IL-10, because they tend to be the minor IL-10R–expressing immune cell in the granuloma. Thus, IL-10 neutralization may have a more significant role on the immune response of non–T cells in granulomas, such as macrophages and B cells.

To detect the effects of IL-10 neutralization on all cytokine-producing (innate and adaptive) cells within granulomas, total concentrations of cytokines within granuloma homogenates were measured by multiplex assay. At 4 wk postinfection, granulomas from anti–IL-10–treated animals had significantly higher levels of IL-2, IL-13, IL-12p70, and IL-23 compared with granulomas from control animals (Fig. 5). These cytokines are likely produced by innate cells and T cells. Neutralization of IL-10 did not lead to increases in all cytokine responses. Granulomas from anti–IL-10–treated animals had lower levels of IL-18 compared with granulomas from control animals (Fig. 5).

The cytokine profile within granulomas changed by 8 wk postinfection. Cytokines in granuloma homogenates at 4 wk postinfection were no longer detectable or were no longer significantly different between granulomas of anti–IL-10 and untreated animals (Fig. 5). Instead, granulomas from anti–IL-10–treated animals had significantly lower levels of IL-1β, IL-1RA, IL-6, and IFN-β compared with granulomas from untreated animals. These data suggest that IL-10 effects may be temporal, with inhibition of innate and adaptive immune responses in lung granulomas early in infection but enhancement of innate cytokine responses after 8 wk of infection.

Because thoracic LNs are also infected during M. tuberculosis infection (74) and there was an increase in T cell–promoting cytokines, such as IL-2 and IL-12p70, in the absence of IL-10 at 4 wk postinfection, we investigated whether IL-10 neutralization affected the T cell response within LNs. At 4 wk postinfection, the M. tuberculosis–infected LNs of anti–IL-10–treated animals had higher levels of bacterial burden compared with control animals (Fig. 6A). Along with a higher bacterial burden, these M. tuberculosis–infected LNs had skewed CD4+/CD4 T cell ratios (anti–IL-10 median = 0.53, quartile (Q)1 = 0.48, and Q3 = 0.74; control median = 1.1, Q1 = 0.91, and Q3 = 1.49) (Fig. 6B). There were 4-fold more CD4 (likely CD8+, although an Ab for CD8 was not used) CD3+ T cells in LNs of anti–IL-10–treated animals (median = 1.3 × 107 cells, Q1 = 7.68 × 106, and Q3 = 2.57 × 107) compared with control animals (median = 3.6 × 106 cells, Q1 = 2.43 × 106, and Q3 = 5.53 × 106), whereas numbers of CD4+ T cells were similar between both groups (anti–IL-10 median = 6.4 × 106 cells, Q1 = 4.08 × 106, and Q3 = 1.8 × 107; control median = 4.1 × 106 cells, Q1 = 3.17 × 106, and Q3 = 6.95 × 106).

Although there were few effects of IL-10 neutralization on LN immune cell populations and T cell functionalities 4 wk postinfection compared with lung granulomas (Figs. 3, 6C), the LN supernatants of the anti–IL-10 animals had higher levels of T cell–promoting cytokines IL-2, IL-13, and IL-12p70 and higher levels of IL-1β, IFN-γ, and TNF compared with LNs of control animals (Fig. 6D). There were also higher levels of lymphocyte-recruiting chemokines within LNs of anti–IL-10 animals (Fig. 6E). Corresponding to increased bacterial burden, CD4 T cells, T cell–promoting cytokines, and lymphocyte-recruiting chemokines, there were more total immune cells and increased weights of LNs from anti–IL-10 animals as compared with controls (Supplemental Fig. 1B, 1C). These data suggest that IL-10 may inhibit T cell proliferation and immune cell recruitment in LNs, particularly of CD4 T cells, during M. tuberculosis infection. However, the ability of IL-10 to directly or indirectly downregulate these immune responses and reduce CD4 T cells in the LN at 4 wk may be beneficial for the initial M. tuberculosis control in LNs, as evidenced by the higher bacterial loads in LNs of anti–IL-10–treated animals at 4 wk.

After an additional 4 wk of M. tuberculosis infection, the patterns in bacterial burden and T cell ratios in LNs reversed compared with the earlier time point. At 8 wk postinfection, LN bacterial burdens were similar between anti–IL-10 and control animals (Fig. 6A). The ratio of T cell subsets in anti–IL-10 animals increased, with more CD4+ T cells relative to CD4− T cells (CD4+/CD4 ratio median = 1.9, Q1 = 1.647, and Q3 = 2.5) in the IL-10–neutralized group as compared with the control group (median = 0.98, Q1 = 0.5771, and Q3 = 1.456) (Fig. 6B). Whereas the number of CD4+ T cells in M. tuberculosis–infected LNs were similar at 4 and 8 wk, the elevated numbers of CD4 T cells observed in anti–IL-10–treated animals 4 wk of M. tuberculosis infection decreased by 8 wk (median = 2.4 × 106 cells, Q1 = 1.94 × 106, and Q3 = 4.69 × 106), increasing the CD4+/CD4 ratio.

As in lung granulomas, there are significantly higher frequencies of T cells producing IL-17 in M. tuberculosis–infected LNs from anti–IL-10–treated animals compared with untreated animals at 8 wk postinfection (Fig. 6C). However, IL-10 neutralization did not affect other types of T cells (Supplemental Fig. 1B) or total cytokine and chemokine production (Supplemental Table I) within LNs of anti–IL-10–treated animals compared with untreated controls. By 8 wk, there were also similar numbers of immune cells within LNs of anti–IL-10 treated as compared with control animals (Supplemental Fig. 1C). Taken together, these results suggest that IL-10 may help initially control bacterial growth within LNs and prevent excessive T cell proliferation and recruitment within the first 4 wk of M. tuberculosis infection but may have fewer effects on the LNs after 8 wk of infection.

Besides the immunological changes occurring in lung granulomas and M. tuberculosis–infected LNs in the absence of IL-10, there were changes in lung granuloma structure from anti–IL-10–treated animals. Blinded histopathological analysis of lung granulomas from both treatment groups revealed more evidence of fibrosis within granulomas from animals treated with anti–IL-10 compared with control animals at the time of necropsy (Fig. 7A). In general, the presence of fibrosis is relatively unusual in early infection granulomas. However, 26% of granulomas analyzed from anti–IL-10 animals showed signs of fibrosis, as compared with <5% in control animals, which suggests that IL-10 impairs healing-associated fibrosis in lung granulomas during M. tuberculosis infections.

To further understand these histopathological changes, Masson trichrome staining was used to compare collagen levels in a random subset of granulomas from the anti–IL-10–treated and –untreated animals at both time points. After 8 wk of infection, granulomas from anti–IL-10–treated animals had slightly higher collagen (blue) mean pixel intensity compared with untreated controls and compared with IL-10–neutralized granulomas at 4 wk of infection (Fig. 7B, 7C). We examined the collagen deposition in lung granulomas more closely, using immunohistochemistry staining specifically against collagen I. Although most granulomas had some collagen I, lung granulomas from anti–IL-10–treated animals, especially by 8 wk, often had more infiltrative collagen patterns into the central necrotic caseous area of the granuloma (Fig. 7C, collagen I–CD68–CD163 insets). We did not observe differences in fibroblast (vimentin positive) or myofibroblast (α-SMA positive) cells between granulomas from anti–IL-10–treated and –untreated control animals, although we identified vimentin- and α-SMA–positive cells in some of the granulomas (Fig. 7C, vimentin–CD68–α-SMA).

In other models, IL-10 has been suggested to have an indirect effect on fibrosis through other cytokine pathways; two of the best characterized cytokine mediators of fibrosis are TGF-β and Th2 cytokines IL-4/IL-13 (75). In support of this, IL-13 was increased within lung granulomas from anti–IL-10–treated animals after 4 wk of M. tuberculosis infection (Fig. 5), whereas IL-4 was not detectable (Supplemental Table I). Using phospho-SMAD3 and phospho-STAT6 to detect TGF-β or IL-4/IL-13 signaling within granulomas, respectively, we observed signaling through both pathways under both treatments at both time points, with no discernable differences based on presence of IL-10 or infection times (Fig. 7C). Thus, during M. tuberculosis infection, IL-10 may normally suppress early collagenization and fibrosis within lung granulomas, possibly through inhibition of IL-13, although the pathways leading to granuloma fibrosis during M. tuberculosis infection need further investigation.

Although we observed immunological and structural changes in both lung granulomas and LNs during M. tuberculosis infection when IL-10 was neutralized, these changes did not alter overall disease outcomes according to our outcome measures. Because of the short nature of our in vivo study (8 wk), we used computational modeling to explore the role of IL-10 on granuloma function during longer term infections using GranSim, our next-generation ABM (76). We used LHS (see 2Materials and Methods) to simulate thousands of biologically feasible individual granulomas and simulated these in both virtual wild-type (WT; control) and IL-10–depleted (IL-10 KO) scenarios. Representative snapshots of WT and IL-10 KO granulomas, along with bar charts of cellular compositions, are shown in Fig. 8A. The parameters we varied included several that affect the concentration of IL-10 within a granuloma (Supplemental Table II shows the list of parameters and the ranges used). We first verified, using LHS, that varying selected parameters resulted in a range of IL-10 concentrations within the granuloma was qualitatively similar to those observed in vivo for WT and also created the IL-10 KO simulations (Fig. 8B, 8C).

We then performed a sensitivity analysis using PRCCs (see 2Materials and Methods) to determine the most significantly correlated parameters (mechanisms) within a granuloma environment that were affected during the simulated IL-10 KO. Supplemental Fig. 2 shows the longitudinal PRCCs for parameters that are significantly correlated with IL-10 in the granuloma environment at one or more time points (p < 0.05). Our analyses predict that early during infection, the amount of IL-10 secreted by infected macrophages, and the maximum amount of TGF-β required to inhibit macrophage phagocytic activity by 50% are the most influential mechanisms affecting the concentration of IL-10 in the granuloma environment. Starting from 6 wk postinfection, T cell–related mechanisms become the most significant of all of the varied parameters related to controlling IL-10 concentrations in a granuloma. The sensitivity analysis also suggests that granulomas with higher IL-10 are associated with fewer IFN-γ–producing T cells and more regulatory T cells. This follows from the PRCC values shown in Supplemental Fig. 2, as threshold parameters, when low, allow more recruitment (e.g., Treg: thresholdRec), and recruitment probabilities, when low, allow little to no recruitment of cells (Tgam: maxRecProb).

It should also be noted that in our model the role of TGF-β could be inflated because of the lack of IL-13 in the computational model that would complement the role of TGF-β in vivo.

Consistent with what we have previously observed using our computational model, depleting IL-10 affects the numbers of granulomas that sterilize over the course of infection (20, 77). Fig. 9A demonstrates 63% of simulated granulomas were sterilized in the IL-10 KO simulations after 150 d (dashed black line), compared with 51% of granulomas in the control group (WT, solid black line).

To further explore the role of IL-10, we grouped the WT simulations into four groups by quartiles of IL-10 concentrations at 4 wk postinfection (Fig. 9A). To eliminate bias, we first confirmed that the average time to sterilization was not less than the 4-wk time point that we used for grouping (Fig. 9B). Our results demonstrate that granulomas with the lowest IL-10 concentrations at 4 wk postinfection (<25%, blue, and 25–50%, yellow) had similar frequencies of sterilization by 20 wk as the IL-10 KO granulomas. However, granulomas in the higher IL-10 concentration groups at 4 wk postinfection (50–75%, green, and >75%, red) were less likely to sterilize at 150 d than those with lower IL-10 concentrations or the IL-10 KO (Fig. 9A). The time to sterilization was not significantly altered between IL-10 groups, except that there was a higher number of very early (<1 wk) sterilizing simulated granulomas in the lowest IL-10 group (<25%, blue) and the IL-10 KOs. These represent early clearance of M. tuberculosis before granuloma formation, suggesting animals with little or no IL-10 may have fewer granulomas as compared with animals with naturally higher IL-10 concentrations in the lung. However, these slight differences predicted from the computational model would be difficult to observe in vivo without larger numbers of animal studies.

Although proinflammatory cytokines have been demonstrated to be necessary to control M. tuberculosis replication, the role of anti-inflammatory cytokines to counteract potentially tissue-damaging cytokines have not been fully elucidated. Previous studies resulted in contradictory conclusions about the beneficial or harmful roles of IL-10 during M. tuberculosis infection (15, 4253), with some suggesting that IL-10 may have differential roles depending on stage of infection (42, 43, 49). We sought to clarify the role of IL-10 within TB lung granulomas using an NHP model of TB and an anti–IL-10 Ab to neutralize macaque IL-10. Our in vivo study neutralizing the effects of IL-10 in a cohort of NHPs demonstrated that IL-10 may increase inflammation in some lung granulomas and decrease cytokine responses within lung granulomas and LNs very early (4 wk) in M. tuberculosis infection but with reduced effects by 8 wk of M. tuberculosis infection. IL-10 may also affect the structure of lung granulomas, reducing early collagenization and fibrosis. Although IL-10 may influence the local environment of a subset of lung granulomas and LNs, the presence or absence of IL-10 did not ultimately influence the overall bacterial burden or disease pathology in the host, at least during the early stages of infection.

For technical reasons, it was only feasible to neutralize IL-10 in vivo for several weeks; thus, we were unable to investigate the effects of longer neutralization on infection outcome. Computational modeling provided the opportunity to investigate the effects of long-term depletion of IL-10 in granulomas. In previously published computational modeling studies, we demonstrated that depleting IL-10 can increase the probability that a granuloma is cleared in NHPs and identified activated macrophages as the major IL-10 producers within a TB granuloma, affecting the CFU in a granuloma (20). In this study, we extended our previous work to explore the effect IL-10 has on CFU, sterilization, and development of fibrosis by using a next-generation version of GranSim, our previously published ABM of cellular dynamics in the lung (64). Our results in this system suggest that depletion of IL-10 decreases the overall numbers of granulomas, increases the probability of a granuloma sterilizing, and contributes to increased fibrosis by enhancing fibroblast proliferation and fibroblast to myofibroblast differentiation.

A limitation to our study was that we were unable to accurately quantify the concentration of anti–IL-10 Ab in the tissues or to measure the pharmacokinetics of this particular Ab, thus our neutralization of IL-10 in the tissues may not have been optimal at all time points. Despite this, we were able to identify immunologic and pathologic effects of the Ab within the lung and LN tissues in the NHP in vivo experiments, where the anti–IL-10 Ab had the greatest effect on the host immune response to M. tuberculosis infection within the first weeks of infection. A subset of granulomas from anti–IL-10–treated animals had lower inflammation (FDG SUV < 5) early in infection (4 wk), which corresponded to significantly lower CFU 4 wk later. Anti–IL-10 treatment also led to increased cytokine response in lung granulomas and LNs at 4 wk, particularly in the proinflammatory cytokines IL-23, IL-2, IL-12p70, IFN-γ, and TNF. These results support previous in vitro and in vivo experiments that noted increased proinflammatory cytokine response, particularly in macrophages and T cells, with the blockade or removal of IL-10 (33, 35, 36). IL-10 can affect most immune cells, but it may primarily affect myeloid cells in the context of M. tuberculosis infection (46, 49). Many of the T cell–promoting cytokines and chemokines that were increased in anti–IL-10–treated animals at 4 wk postinfection are produced by myeloid cells, and there were lower concentrations of macrophage-produced cytokines (IL-1β, IL-1RA, IL-6, and IFN-β) in lung granulomas of anti–IL-10–treated animals at 8 wk. In addition, as we show in this study and others have demonstrated, the receptor for IL-10 (IL-10R) is predominantly on non–T cells, especially in TB granulomas (48, 77).

Although we observed increased T cell–promoting cytokines and chemokines in lung granulomas and LNs in anti–IL-10 animals, we did not observe corresponding increased T cell activity in lung granulomas; however, there were altered T cell populations in LNs, as reported in other TB studies (43, 45). Unlike Redford et al. (45), who observed increased numbers of CD4+ T cells in the draining LNs of IL-10 KO mice compared with controls, we observed more CD4 (presumably CD8+) T cells, particularly at 4 wk postinfection. Instead, our results were similar to acute lymphocytic choriomeningitis virus infections, in which CD8+ T cells increased during IL-10 blockade, particularly during the priming phase (78, 79); similar results were also seen with listeria and HIV infections (80, 81). However, the heightened LN T cell responses did not translate into improved host control of M. tuberculosis infection by 8 wk.

The absence of IL-10 in this study increased fibrosis in lung granulomas. Although some studies have suggested that IL-10 may act directly on fibroblasts to increase collagen deposition in the skin, IL-10 is generally thought to reduce fibrosis through modulation of other profibrotic cytokines (75, 8284). There were increased concentrations of IL-13 at 4 wk and increased frequencies of T cells producing IL-17 in lung granulomas of anti–IL-10–treated animals. Both IL-13 and IL-17 have been demonstrated to be critical contributors to fibrosis in other models (75, 85, 86). Other potential mediators of fibrosis, IL-12, IL-1 family (IL-1β, IL-1RA, and IL-18), IL-6, IL-23, and IFN-β (85, 8790), were also dysregulated during IL-10 neutralization in this study. Our study only provided snapshots of IL-10 neutralization during early M. tuberculosis infection; thus, we were unable to pinpoint mechanisms of increased fibrosis in this study, although our computational work indicated TGF-β as a critical factor for fibrosis in the absence of IL-10.

The role of fibrotic lesions in TB is unclear, but fibrosis tends to be associated with containment, drug treatment, and sterility (91, 92). Fibrosis is a healing response that can result in pathology but can also limit spread of infection. It is not generally observed at early time points in M. tuberculosis infection but becomes more common during longer infections. Early blockade of IL-10 during M. tuberculosis infection led to fibrotic granulomas and a complete lack of IL-10 led to granuloma structure changes in the mouse model (43, 50). Although we did not observe the same levels of bacterial control in our NHP model early in M. tuberculosis infection, neutralization of IL-10 increased the fibrosis and collagenization of lung granulomas, and our computational modeling of IL-10 KO suggested increased bacterial control over extended infection times. The mediators and significance of fibrosis in granulomas are currently unclear, but fibrosis in lung granulomas may lead to improved bacterial containment and/or modified lung pathology in the long term beyond the scope of this study. However, this raises the possibility that neutralization of IL-10 during drug treatment could be a potential host-directed therapy for increasing response to drug treatment in active TB, although this would require further testing.

The transient increases in immune responses in lung granulomas and LNs with IL-10 neutralization did not affect overall disease pathology or bacterial burden in the host during early infection as other studies had suggested (42, 43, 45), but it also did not increase disease pathology or decrease survival (50). There are several possibilities to explain these findings. First, IL-10 may not have a significant role in the host immune response to M. tuberculosis, as supported by a subset of the published murine studies (5052, 93). Second, not all granulomas may respond to IL-10 in the same manner. Previous studies from our laboratory have demonstrated the individuality of each granuloma, even from the same animal (15, 58). Our ex vivo neutralization assays also suggest that granulomas may not all be producing or responding to IL-10 during M. tuberculosis infection. Third, other host factors may compensate for the lack of IL-10. Roach et al. (53) showed a transient decrease in bacterial burden of IL-10 KO mice after 4 wk of infection, but by 8 wk postinfection, there was no difference in bacterial burden compared with WT mice. Other studies also showed that the combination of T cell and innate cell production of IL-10 were critical for M. tuberculosis protection (46, 49). In our study, we observed greater changes in the cytokine and chemokine response in anti–IL-10 animals after 4 wk of infection, which could be due to the onset of the adaptive immune response counteracting and minimizing the influence of IL-10 by 8 wk (11). The observed increase in the Th2 cytokine IL-13 with anti–IL-10 treatment at 4 wk postinfection may have helped compensate for the lack of IL-10. Although we quantified FOXP3+ regulatory T cells and assessed TGF-β signaling by immunohistochemistry and computational modeling in our study, we were unable to quantify the anti-inflammatory cytokine TGF-β. M. tuberculosis can also stimulate the production of TGF-β, which could compensate for the lack of IL-10 (77, 94). Because of the limitations of this study, we may not have assessed factor(s) that could compensate for the actions of IL-10.

Finally, the role of IL-10 during M. tuberculosis infection could be time dependent. The in vivo study was restricted to the first 8 wk of infection to capture the change in innate and adaptive immune responses. Turner et al. (47) observed overexpression of IL-10 in the mouse model of M. tuberculosis infection had no effect on the bacterial load until 5 mo postinfection. Other studies found that IL-10 had the greatest effect on the host ability to control the bacterial burden starting at least after 3 mo of infection (42, 43, 48). Moreira-Teixeira et al. (46) observed significantly lower bacterial burdens in IL-10–depleted M. tuberculosis–infected mice after 2 mo, but not within 1 mo of infection. Thus, early IL-10 neutralization may initiate the immune response for long-term control of bacterial loads outside the scope of our in vivo study, as suggested by our in silico computational modeling of IL-10 and previously by Cyktor et al. (43). Future studies investigating the role of IL-10 during chronic M. tuberculosis infections are warranted.

Overall, neutralization of IL-10 in macaques during M. tuberculosis infection altered the innate and adaptive immune responses within the local granuloma and LN environments but did not result in improved infection outcome. We also did not observe excessive inflammation in the absence of IL-10. Thus, IL-10 alone may not have a significant role in early M. tuberculosis infections, likely having greater effects in cooperation with other immune cells/cytokines (77, 94, 95). This study further demonstrates the need for a properly balanced immune response within the locally affected tissues during M. tuberculosis infection and the need to continue to identify other host factors that may inhibit effective immune responses against M. tuberculosis (96).

Rhesus IL-10 was kindly provided by Dr. Francois Villinger. We thank Dr. David Sacks (National Institutes of Health, National Institute of Allergy and Infectious Diseases) for helpful discussions regarding study design. We are grateful to the members of the J.L.F., Charles Scanga, P.L.L., and J.T.M. laboratories for advice and discussion regarding this work.

This work was supported by National Institutes of Health (NIH) R01AI123093 and R01HL110811 (awarded to D.E.K. and J.L.F.). Any simulations also use resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract ACI-1053575, and the Extreme Science and Engineering Discovery Environment, which is supported by National Science Foundation Grant MCB140228. E.A.W. was supported by NIH T32 AI060525, and S.E. was supported by an Intersect Fellowship from the American Association of Immunologists.

The online version of this article contains supplemental material.

Abbreviations used in this article:

ABM

agent-based model

18F-FDG

2-deoxy-2-[18F]fluoro-d-glucose

IACUC

Institutional Animal Care and Use Committee

KO

knockout

LHS

Latin hypercube sampling

LN

lymph node

NHP

nonhuman primate

PET-CT

positron emission tomography–computed tomography

PRCC

partial rank correlation coefficient

Q

quartile

SMA

smooth muscle actin

SUV

standard uptake value

SUVR

SUV ratio

TB

tuberculosis

WT

wild-type.

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The authors have no financial conflicts of interest.

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