The Aging Microenvironment Shapes Alveolar Macrophage Identity in Aging

Home , Alveolar macrophage

bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Alexandra C. McQuattie-Pimentel*1, Ziyou Ren*1, Nikita Joshi1, Satoshi Watanabe1,8, Thomas

Stoeger2, Monica Chi1, Ziyan Lu1, Lango Sichizya1, Raul Piseaux1, Ching-I Chen1, Saul

Soberanes1, Paul A. Reyfman1, James M. Walter1, Kishore R. Anekalla1, Jennifer M. Davis1,

Kathryn A. Helmin1, Constance E. Runyan1, Hiam Abdala-Valencia1, Kiwon Nam1, Angelo Y.

Meliton3, Deborah R. Winter4, Richard I. Morimoto5, Gökhan M. Mutlu3, Ankit Bharat6, Harris

Perlman4, Cara J. Gottardi1, Karen M. Ridge1, Navdeep S. Chandel1, Jacob I. Sznajder1, William

E. Balch7, Benjamin D. Singer1,5, Alexander V. Misharin*#1, GR Scott Budinger*#1

*These authors contributed equally.

1Department of Medicine, Division of Pulmonary and Critical Care Medicine, Northwestern

University, Chicago, IL, 60611, USA.

2Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL,

60208, USA.

3Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of

Chicago Hospitals, Chic

4Department of Medicine, Division of Rheumatology, Northwestern University, Chicago, IL,

60611, USA.

5Department of Molecular Biosciences, Rice Institute for Biomedical Research, Northwestern

University, Evanston, IL, 60208, USA. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

6Department of Surgery, Division of Thoracic Surgery, Northwestern University, Chicago, IL,

60611, USA.

7The Scripps Research Institute Department of Chemical Physiology, La Jolla, CA, 10550, USA.

8Kanazawa University Graduate School of Medical Sciences, Department of Respiratory

Medicine, Kanazawa, Ishikawa 920-8641, Japan

#Corresponding authors:

GR Scott Budinger, MD

Pulmonary and Critical Care Medicine

Northwestern University

303 E Superior Street, Simpson-Querrey Research Building, 5-522

Chicago, IL 60611

Phone: 312-908-8163

Fax: 312-908-4650

Email: [email protected]

Alexander V. Misharin, MD, Ph.D.

Pulmonary and Critical Care Medicine

Northwestern University

303 E Superior Street, Simpson-Querrey Research Building, 5-522

Chicago, IL 60611

Phone: 312-908-8163 bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Fax: 312-908-4650

Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Abstract

A dysfunctional response to inhaled pathogens and toxins drives a substantial portion of the

susceptibility to acute and chronic lung disease in the elderly. We used transcriptomic profiling

combined with genetic lineage tracing, heterochronic adoptive transfer, parabiosis and treatment

with metformin to show that the lung microenvironment defines the phenotype of long-lived

alveolar macrophages during aging. While tissue-resident alveolar macrophages persist in the

lung without input from monocytes over the lifespan, severe lung injury results in their

replacement with monocyte-derived alveolar macrophages. These monocyte-derived alveolar

macrophages are also shaped by the microenvironment both during aging and in response to a

subsequent environmental challenge to become transcriptionally and functionally similar to

tissue-resident alveolar macrophages. These findings show that changes in alveolar macrophage

phenotypes during injury and aging are not cell autonomous but instead are shaped by changes in

the aging lung microenvironment.

Introduction

Alveolar macrophages are derived from fetal monocytes that originate in the yolk sac or fetal

liver. Shortly after birth these fetal monocytes rapidly differentiate into a stable, self-renewing

population of “tissue-resident alveolar macrophages” (TRAM) that persist without input from

bone marrow-derived monocytes for prolonged periods of time (Guilliams et al., 2013;

Hashimoto et al., 2013; Janssen et al., 2011; Yona et al., 2013). TRAM reside in the ambient air

and epithelial surface lining fluid, adhering to the lung epithelium via integrins on their surface

where they are responsible for sampling, responding to, and clearing pathogens and particulates

that reach the alveolar space (Watanabe et al., 2019). If TRAM are depleted then monocytes are

recruited to the alveolar space where they differentiate into “monocyte-derived alveolar

macrophages” (MoAM) through a progressive reshaping of their epigenome (Gibbings et al.,

2015; Lavin et al., 2014).

Aging is the most important risk factor for the morbidity and mortality attributable to acute and

chronic lung diseases (Budinger et al., 2017). Indeed, age-related lung diseases including

pneumonia, Chronic Obstructive Pulmonary Disease and pulmonary fibrosis are among the most

common causes of death in the developed world (WHO, 2018). Alveolar macrophages have been

implicated as a key cell population in the pathobiology of all of these disorders (Hussell and

Bell, 2014). The identification of ontologically distinct populations of TRAM and MoAM raises

important questions about their function during aging. First, if TRAM persist in the alveolus

without monocyte input over the lifespan, does their phenotype change with aging? Second, are

age-related changes in TRAM cell-autonomous or driven by changes in the local

microenvironment? Third, if MoAM are recruited to the lung in response to early life insults and bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

persist, do they respond differently to a subsequent environmental challenge? To address these

questions, we generated a genetic lineage tracing system to follow TRAM and MoAM over the

lifespan and used transcriptomic identity to determine the role of the microenvironment and

ontogeny in shaping their response to aging and repeated environmental stress. These results

suggest that while ontogeny plays a major role in acute stress, its role in aging or chronic stress is

minimal. Instead, our results highlight the importance of the lung microenvironment in shaping

immune responses in the aging lung.

Results

Tissue-resident alveolar macrophages persist in the lung without input from bone marrow-

derived monocytes during normal aging. To determine whether the ontogeny of TRAM changes

during aging, we used thoracic shielding and a conditioning regimen that includes both radiation

and systemic administration of busulfan to generate 4 month-old chimeric mice

(CD45.1/CD45.2) with complete chimerism in monocytes and ~80% preservation of TRAM

(Misharin et al., 2017). We then allowed these animals to age without interventions up to 24

months (Figure 1A). If MoAM were less capable of self-renewal when compared with TRAM,

the small population of MoAM recruited during the generation of chimeras would decline as a

percentage of total cells during aging as MoAM are replaced by TRAM. However, both

populations were stable over the lifespan (Figure 1B). Furthermore, MoAM showed comparable

levels of EDU incorporation as TRAM (Figure 1C). These results suggest that MoAM and

TRAM are similarly long-lived and capable of self-renewal, as has been previously reported (van

de Laar et al., 2016). Nevertheless, the total number of alveolar macrophages was reduced in

aged compared with young mice (Figure 1D).

Laboratory mice are housed in facilities where unusual steps are taken to limit exposure to

inhaled pathogens and environmental particulates. In previous studies, investigators have shown

that inhaled carbon-based particles are cleared by alveolar macrophages, which eventually are

eliminated via mucociliary clearance (Semmler-Behnke et al., 2007). To determine whether this

clearance of inhaled particulates is sufficient to induce the replacement of TRAM with MoAM,

we exposed mice to concentrated urban particulate matter air pollution <2.5 µm in diameter at a

dose we have previously reported to induce the release of IL-6 from alveolar macrophages and bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

enhances the susceptibility to thrombosis (~10 fold the concentration observed outside our

laboratory in Chicago) for 6 hours per day on three consecutive days (Chiarella et al., 2014;

Soberanes et al., 2019). We found that exposure to these typically encountered levels of airborne

particles does not induce recruitment of MoAM to the lung, or deplete TRAM (Figure 1D and

Fig. S1D). In contrast, the induction of severe lung injury with either intratracheal administration

bleomycin or infection with sublethal dose of influenza A virus resulted in the recruitment of

MoAM that persisted in the lung 60 days after the injury (Figure 1F,G). Notably, both

bleomycin-induced lung injury and infection with influenza A virus induce rapid depletion of

TRAM (Misharin et al., 2013). bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A B CD45.2 C s CD45.1 CD45.2 120 10 100 8 CD45.1 80 6 Chimera 60 4 Harvest 40

20 % of EdU+ cells 2 0 3 6 9 12 15 18 21 24 0 0

Age (months) Percent alveolar macrophage 4 mo 12 mo 18 mo 24 mo CD45.2 CD45.1 Recipient Donor PHK26+ D E F PKH26+ s 8 PHK26- Inhalational exposure to PKH26-

e 100 s ** concentrted particulate matter

ou 6 air pollution < 2.5 μm 75 r m Saline or LPS

pe 4 50 * AM 2 5 25 0 Harvest 1 0 4 18 0 -24 -12 0 12 24 36 48 60 72 Percent alveolar macrophage Saline LPS PM Time hours

CD45.2

G s H CD45.1 100 CD45.2 * * CD45.1 80 60 Influenza A or Chimera Bleomycin Harvest 40 20 0 1 2 3 4 5 6 0

Age (months) Percent alveolar macrophage Control Influenza A Bleomycin

Figure 1. In the absence of severe injury, tissue-resident alveolar macrophages persist over

the lifespan without input from bone marrow-derived monocytes.

(A) Bone marrow chimeras with thoracic shielding were generated in which 100% of circulating

monocytes were CD45.1 and ~80% of tissue-resident alveolar macrophages were CD45.2. These

mice were allowed to age in a barrier facility and harvested at the indicated times.

(B) The percentage of monocyte-derived (CD45.1) or tissue-resident (CD45.2) alveolar

macrophages over the lifespan was measured. Differences in the percentage of cells between

ages were not significant (one-way ANOVA with Bonferroni correction, n=3-5 per time point). bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

pulse of EDU was administered. Mice 4 months of age, n=4.

(D) Total numbers of alveolar macrophages in young (4 months) and old (18 months) wild-type

C57BL/6 mice. Student t-test, ** P = 0.001, n=5 mice per group.

(E) Mice were intratracheally administered PKH26 fluorescent dye 24 hours before exposure to

concentrated ambient particulate matter air pollution < 2.5 µm in diameter for 6 hours daily on

three consecutive days. As negative and positive controls for recruitment of monocytes into the

alveolar space mice received, PBS or LPS (1 mg/kg, intratracheally), respectively. See Figure S1

for gating strategy.

(F) Percentage of alveolar macrophages that were labeled (PHK26+) or unlabeled (PHK26-)

after PBS, LPS or exposure to concentrated ambient particulate matter air pollution (PM) via

inhalation 6 hours daily for three consecutive weekdays. All mice were harvested after three

days. One-way ANOVA with Bonferroni correction, n=5 per group, * indicates P<0.05 for

comparison with control.

(G) Shielded chimeric mice (4 months of age) were treated with influenza A virus or

intratracheal bleomycin and then harvested 60 days later.

(H) MoAM (CD45.1) and TRAM (CD45.2) were quantified by flow cytometry 60 days after

infection with influenza A virus (A/WSN/33) or intratracheal administration of bleomycin.

Control mice were not treated. * indicates P <0.05 for comparison with untreated mice. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

262.1 5 A 10 Gate 2 97.43% 196.6 4 10

3 131.1 10 SSC-A

2 Gate 1 FSC-H (x 1000) 65.5 10 45.46%

1 10 0 0 65.5 131.1 196.6 262.1 0 65.5 131.1 196.6 262.1 FSC-A (x 1000) FSC-A (x 1000)

5 5 5 10 10 10

4 4 4 10 10 10

3 3 3 10 10 10 SSC-A SSC-A SSC-A

2 Gate 3 2 Gate 4 2 Gate 5 Gate 6 10 10 10 95.78% 89.86% 1.91% 95.90% 1 1 1 10 10 10

1 2 3 4 5 2 2 3 4 5 1 2 3 4 5 -10 10 10 10 10 -10 10 10 10 10 10 10 10 10 10 640nm_780/60 (APC-Cy7)-A 640nm_660/20 (APC)-A 561nm_582/15 (PE)-A

Control B 5 5 5 10 10 10

4 4 4 10 10 10

3 3 3 10 10 10 SSC-A SSC-A SSC-A

2 Gate 3 2 Gate 4 2 Gate 5 Gate 6 10 10 10 96.87% 89.31% 3.56% 93.04%

1 1 1 10 10 10

1 2 3 4 5 2 3 4 5 1 3 4 5 -10 10 10 10 10 -10 0 10 10 10 -10 10 10 10 640nm_780/60 (APC-Cy7)-A 640nm_660/20 (APC)-A 561nm_582/15 (PE)-A LPS

5 5 5 10 10 10

4 4 4 10 10 10

3 3 3 10 10 10 SSC-A SSC-A SSC-A

2 Gate 3 2 Gate 4 2 Gate 5 Gate 6 10 10 10 23.25% 3.80% 23.33% 72.59%

1 1 1 10 10 10

1 2 3 4 5 2 3 4 5 1 2 3 4 5 -10 10 10 10 10 -10 10 10 10 -10 10 10 10 10 640nm_780/60 (APC-Cy7)-A 640nm_660/20 (APC)-A 561nm_582/15 (PE)-A PM exposure 3 days

5 5 5 10 10 10

4 4 4 10 10 10

3 3 3 10 10 10 SSC-A SSC-A SSC-A

2 Gate 3 2 Gate 4 2 Gate 5 Gate 6 10 10 10 95.78% 89.86% 1.91% 95.90%

1 1 1 10 10 10

1 2 3 4 5 2 2 3 4 5 1 2 3 4 5 -10 10 10 10 10 -10 10 10 10 10 10 10 10 10 10 640nm_780/60 (APC-Cy7)-A 640nm_660/20 (APC)-A 561nm_582/15 (PE)-A

C 1. All events 2. Singlets 3. Not CD45/CD31

4. Epithelial 5. Epithelial subsets SPC H2B-GFP expression

R1 6%

R2 20%

R3 96%

Figure S1. Gating strategy for labeling alveolar macrophages with PKH26 and isolation of

alveolar type II cells via flow cytometry.

(A) Alveolar macrophages were labeled by intratracheal administration of PKH26 in vivo. After

24 hours, animals were treated intratracheally with PBS (50 µL), LPS (1 mg/kg in 50 µL PBS)

or were exposed to concentrated particulate matter air pollution (6 hours of exposure ~100-120

µg/m3, 6 hours daily for 3 consecutive days). Concentrations of particles are estimates based on

particle measures from the inlet and outlet of the concentrator and reported ambient PM2.5

measures at nearby monitoring stations. Alveolar macrophages were collected via

bronchoalveolar lavage 72 hours after the first exposure and stained with the following reagents

for flow cytometry: APC/Cy7 anti-mouse Ly-6G, Alexa Fluor 647 anti-mouse F4/80 and Helix

NP Green live/dead.

(B) Representative flow plots for a single animal in each experimental group.

suspensions were prepared from the lungs of BAC transgenic mice expressing GFP driven by the

Surfactant C (SFTPC) promoter (Tg-SFTPC-H2B-GFP) mice and analyzed via flow cytometry.

Epithelial cells were identified as singlets/live/CD45-negative/CD31-negative/EpCAM-positive

cells and further subdivided based on EpCAM and MHC II expression. R1: EpCAM-high MHC

II-negative, R2: EpCAM-high MHC II-positive and R3: EpCAM-intermediate MHC II-positive.

Over 95% of cells in gate R3 were positive for surfactant protein C-GFP reporter and this gate

was used to identify alveolar type II cells during conventional flow sorting. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Aging is associated with reproducible changes in the transcriptome of tissue-resident alveolar

macrophages driven by the microenvironment of the aging lung.

Having established that TRAM are not replaced by MoAM over the lifespan, we explored

whether TRAM change at the level of the transcriptome during normal aging using RNA-Seq.

From the same mice, we flow sorted alveolar epithelial type II cells (Fig. S1C), with which

alveolar macrophages are known to interact. We found significant differences in the

transcriptome of both TRAM and alveolar type II cells from 18 month-old compared with 4

month-old animals (Figure 2A), which were similar in multiple independent cohorts of mice and

were similar to transcriptomic changes in aging from another group (Wong et al., 2017) (Figure

S2A-C). Consistent with previous studies of aging, immune system processes including genes

related to interferon signaling were upregulated in aged compared with young animals, while

genes related to cell cycle were downregulated (Figure 2A). To determine whether the

transcriptomic changes in aged TRAM were cell-autonomous or were driven by factors in the

local microenvironment, we performed heterochronic adoptive transfer of 1.5x105 alveolar

macrophages using 6 and 18 month-old mice as donors and recipients. Attempts to

intratracheally adoptively transfer mature TRAM into untreated mice resulted in extremely poor

engraftment (<5%) (Figure S2D-H), however, opening the alveolar niche by depleting TRAM

via administration of liposomal clodronate before adoptive transfer allowed for engraftment rates

up to 30% without the recruitment of neutrophils to the lung (Figure 2C, S2D-H). Interestingly,

the rate of engraftment of young TRAM into older mice was significantly lower than the rate of

engraftment of old TRAM into young mice (Fig. 2D,E). The overall structure of the

transcriptomic data visualized by principal component analysis suggested that heterochronic

transfer altered gene expression of adoptively transferred TRAM toward the age of the recipient bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(Fig. 2F,G). In contrast, heterochronic adoptive transfer of alveolar macrophages did not alter the

transcriptome of alveolar type II cells (Fig. 2H,I). To exclude the possibility that the detected

changes were due to the differences between CD45.1 and CD45.2 strains, we compared

transcriptome of naïve TRAM from these strains and found that only 221 genes were

differentially expressed (FDR q<0.05). Moreover, there was little overlap between genes

differentially expressed with aging and those differentially expressed between TRAM from

CD45.1 and CD45.2 mice (Fig. S2I, J).

A C Young Clo-lip Old TRAM transfer AM Young CD45.1 3 months

Old Clo-lip Young TRAM transfer Old 18 months AT2 CD45.2

B OD>YR YD>OR E GO processes 100 Up in young AM: 961 Up in aged AM: 1372 D Genes Genes GO processes 60 Cell Cycle E2f1 H2−Aa Cxcl2 Inflammatory response p=0.0286 TranslationCdkn3 Ciita Cxcl3 Chemotaxis Regulation Cd63 Cd209b Eif3f H2-DMb1 Il1b Cell Adhesion of cell proliferation Itga6 Cd74 40 ApoE Ecm1 Mmp14 Response to stress val Slc30a4 ATP5a1 H2−Ab1 Axl Positive regulation Car4 Itgav of endocytosis

−LogFDR 20 Pparg Emp1 Chil1 Sirt1 Gmfg CD74 AM % of donor Gmpr Adoptive transfer Ndufa1 Hif1a Mfge8 0 O>Y Y>O 0 20 40 60 80

−15 −10 −50 510 15

logFC

Up in young AT2: 661 Up in aged AT2: 753

GO processes Genes Nr1d2 Genes GO processes Cell Adhesion Cd200 Slc6a20a Jak3 Immune response Pigr Ppp2r2c Conrol

Extracellular matrix Igf1 0 50 60 Tlr3 Regulation of biological quality 4 organization Mmp2 Nrn1 Cxcl5 Regulation of ion transport val Scgb1a1

Locomotion Sod3 0 Pim3 Muc5b Lipid metabolic process 3 Gp2 Cell-cell adhesion Nox4 Lyz1 Pdk4

−LogFDR Gpm6b

Fgf10 0 Il6 Pdgfa 2 Pparg Umod Pdgfb Ecel1 H2-K1 Tgfbr1 Ighv1-53 Wnt4 Plekhs1 Ppp2r2c 0 10 Igkv10-96 Ighg2b

−10 −5 0 5 10

logFC F G Young AM Old AM Young AM Old AM Young Naive Young Mouse Young Mouse Old Mouse Old Mouse Old Naive Naive Young Alveolar Macrophages Transferred Young Alveolar Macrophages Transferred Old Alveolar Macrophages ) Naive Old Alveolar Macrophages

Alveolar genes) (221 I 1.5 1.5 Macrophage 1.0 1.0 0.5 0.5 0.0 ) 0.0 -0.5 -0.5 Average Z-Score Average Z-Score II (171 genes) (171 II -1.0 -1.0 -1.5 -1.5

) Young Microenvironment Old Microenvironment Young Microenvironment Old Microenvironment

III (77) III Cluster I Cluster II

H I Old AM Young AM Young Naive Young Mouse Old Mouse Old Naive Naive Young Alveolar Type II Cells

Young AT2 after transfer of Old Alveolar Macrophages ) Old AT2 after transfer of Young Alveolar Macrophages

Alveolar genes) (472 I Naive Old Alveolar Type II Cells Type II 1.5 1.5 Cell 1.0 1.0 0.5 0.5 ) 0.0 0.0

II (492 genes) (492 II -0.5 -0.5 Average Z-Score Average Z-Score -1.0 -1.0 -1.5 -1.5 ) Young Microenvironment Old Microenvironment Young Microenvironment Old Microenvironment III (214) III Cluster I Cluster II

Figure 2. Age-related transcriptomic changes in tissue-resident alveolar macrophages are

driven by the local microenvironment.

(A) TRAM and alveolar epithelial type II cells were isolated via flow cytometry cell sorting from

lung tissue from naïve C57Bl/6 mice (NIA NIH colony) at 4 (young) and 18 months (old) of age bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

and analyzed using RNA-Seq (see also Figure S1C for genetic validation of the gating strategy

for alveolar type II cell isolation).

(B) Differentially expressed genes (FDR q<0.05) between TRAM and alveolar type II cells from

young and old mice are shown with representative genes and GO biological processes (See Table

S1 for complete list of genes and GO processes).

as indicated (see also Figure S2).

(D) Representative flow cytometry plots showing engraftment of TRAM from old and young

donors into young and old mice, respectively. Harvest was performed 60 days after the adoptive

transfer. All mice received liposomal clodronate (25 µL) intratracheally 72 hours prior to the

adoptive transfer (also see Figure S2).

(E) Percentage of engraftment for young and old TRAM, as in panel D, 4 mice per group. Mann-

Whitney t-test.

(F) Heatmap showing the results of k-means clustering of the differentially expressed genes

(FDR q < 0.05 in ANOVA-like test) in TRAM 60 days after heterochronic adoptive transfer into

young or old mice. Columns from left to right show (1) TRAM from young naïve mice, (2)

TRAM in young recipient mice, (3) old TRAM adoptively transferred into young mice, (4)

young TRAM adoptively transferred into old mice, (5) TRAM from old recipient mice, and (6)

TRAM from naïve old mice (Figure S2 shows minimal overlap with differentially expressed

genes in TRAM from CD45.1 compared with CD45.2 mice, see full list of genes in Table S2).

(G) Average Z scores for the genes in Cluster I (genes up with aging) and Cluster II (genes down

with aging) for each of the columns in (F). bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(H) Heatmap showing the results of k-means clustering of the differentially expressed genes

(FDR q < 0.05 in ANOVA-list test) in alveolar type II cells 60 days after the heterochronic

adoptive transfer of TRAM. Columns from left to right show transcripts in (1) alveolar type II

cells from young naïve mice, (2) alveolar type II cells after adoptive transfer of young TRAM

into old mice, (3) alveolar type II cells after adoptive transfer of old TRAM into old mice, and

(4) alveolar type II cells from naïve old mice (see full list of genes in Table S2).

(I) Average Z scores for the genes in Cluster I (genes up with aging) and Cluster II (genes down

with aging) for each of the columns in (H).

A Alveolar Macrophages D Adoptive Transfer Naïve Aging

P=4.27 E-163 Clo-lip (i.t.)

B Alveolar Type II cells E F Transferred Adoptive Transfer Engrafted 4.9 35.5 Naïve Aging 1.6 1.4 P=1.99 E-49 1.2

94.8 63.7 1.0 C 0.8 0.6 CD45.1 (Donor) CD45.1 (Donor) Cells 10^5 0.4 GO Processes 0.2 CD45.2 (Recipient) CD45.2 (Recipient) Cell cycle 0.0 Leukocyte chemotaxis Naïve Liposomal ResponseAging overlap to stress clodronate 0.8 0.8 G 0.8 H Siglec Fhigh TR-AM Siglec Flow Mo-AM Neutrophils 0.8 Siglec Fhigh TR-AM 0.8 Siglec Flow Mo-AM 0.8 Neutrophils Wong et al 2017 0.6 0.6 0.6 0.6 0.6 0.6 * * Naïve Aging PBS * * PBS /mouse ** /mouse

6 ** 2665 849 1477 Clo-Lip 50 ul 6 0.4 0.4 0.4 0.4 0.4 0.4 Clo-Lip 50 ul P=2.19142e-17 0.2 0.2 ** 0.2 0.2 0.2 ** 0.2 Cells 10 Cells 10 0.0 0.0 0.0 0.0 0.0 0.0 0 1 3 10 18 0 1 3 10 18 0 1 3 10 18 0 1 3 10 18 0 1 3 10 18 0 1 3 10 18

CD45.1 vs CD45.2 mouse DEG with FDR <0.01 I #Downregulated:48 #Upregulated:64 60 80 CD45.2 CD45.1

Ctse Kif21b -LogFDRval

Ppp1r12b Dbi 0 20 40

-10 -5 0510

logFC J Alveolar Macrophages De Gene w/ FDR<0.01 overlap 36 Adoptive Transfer CD45.1 v.CD45.2

918 76 P=2.78 E-14

Figure S2. Heterochronic adoptive transfer of TRAM using CD45.1/CD45.2 mice. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(A) Venn diagram shows overlap between genes differentially expressed in TRAM from naïve

young (4 months of age) and old (18 months of age) mice and in adoptive transfer experiments

(Refers to Figure 2A and Figure 2F).

(B) Venn diagram shows overlap between genes differentially expressed in alveolar type II cells

from naïve young and old mice and in adoptive transfer experiments (Refers to Figure 2A and

Figure 2H).

TRAM in this study (Fig. 2A) and those reported by Wong et. al.

(D) Schematic design of heterochronic adoptive transfer experiments with or without

intratracheal liposomal clodronate.

(E) Representative flow cytometry plots from CD45.2 mice treated without (left panel) or with

(right panel) liposomal clodronate (25 µL, intratracheally) before the adoptive transfer of 1,5x105

TRAM from CD45.1 mice.

(F) Quantification of adoptively transferred TRAM 60 days after adoptive transfer (n=3 mice per

group).

(G) Mice were treated with liposomal clodronate 25 µL intratracheally and the number of

neutrophils was measured in the single cell suspension prepared from the lung using flow

cytometry.

(H) Mice were treated with intratracheal liposomal clodronate 25 µL intratracheally and the

number TRAM (SiglecFhigh) was measured using flow cytometry.

(I) TRAM were isolated from the lungs of CD45.1 (Jax 002014) or CD45.2 mice (Jax 000664)

via flow sorting, RNA was isolated and analyzed using RNA-Seq. Differentially expressed genes

(FDR < 0.01) are shown (see also Table S3 for full list of genes). bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(G) Venn diagram shows overlap between differentially expressed genes in CD45.1/CD45.2

TRAM and genes differentially expressed in heterochronic adoptive transfer experiments (Refers

to Figure 2F).

Heterochronic parabiosis does not revert age-related changes in tissue-resident alveolar

macrophages. Heterochronic parabiosis, in which circulating factors and myeloid cells from old

or young mice are shared with age mismatched partners has long been recognized to reverse and

induce age-related phenotypes in aged and young mice, respectively (Conboy et al., 2013).

Accordingly, we used parabiosis to determine whether circulating factors or circulating cells

from young animals (6 months of age) could reverse the environmentally-driven age-related

transcriptomic changes in TRAM from old mice (18 months) (Figure 3A). We generated

heterochronic and isochronic parabiont pairs and flow-sorted TRAM 60 days later, after

parabionts established stable chimerism in the peripheral blood (Figure 3B). As alveolar type II

cells express molecules necessary for the maintenance of TRAM, we simultaneously flow-sorted

them and performed transcriptomic profiling via RNA-Seq (Figure 3H). Principal component

analysis showed clustering of the samples according to age of the host irrespective of the age of

the parabiont pair in both TRAM and alveolar type II cells (Figure 3C, 3I). Comparison of

TRAM transcriptomes in young/young with old/old parabiont pairs revealed 866 differentially

expressed genes (FDR < 0.05) (Figure 3E), 438 of which were also identified as differentially

expressed in our independent aging dataset (Figure S3A, P=3.4 E-89). Comparison of TRAM in

young/young pairings with TRAM from the aged environment in young/old pairings identified

474 differentially expressed genes, 288 of which overlapped with those differentially expressed

during aging. In stark contrast, only 0 and 5 genes met criteria for differential expression

between young/young and the young or old TRAM, respectively, from the heterochronic pairing

(Figure 3F,G). Although the number of differentially expressed genes in alveolar type II cells

was smaller (379), there was almost no change with heterochronic parabiosis (Figure 3I-M). bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

These results suggest that transcriptional changes in TRAM and alveolar type II cells with aging

are independent of circulating cell populations or signaling factors.

PCA plot parabiosis AM

A C D

Alveolar 20

Macrophage pair

Heterochronic 60 days Isochronic

age 0 Old 199 genes 199 PC2: 15.5% variance

Young

Old cells −20 Young Cells

−20 −10 0 10 20 PC1: 24.1% variance B 78 Young-Young Young-Old Old-Old

100 E F G

Percent 50 0 0 2 3 298 568 genes genes genes genes genes genes 0 Young Old Parabiont Parabiont

PCA plot parabiosis AT2 H Alveolar I J Type II Cell 60 days 20

pair

0 Heterochronic

Isochronic

age genes 48 −20 Old PC1: 55.4% variance

Young

−40

−60 12 −10 0 10 PC2: 11.5% variance

Young-Young Young-Old Old-Old K L M

83 296 genes genes 0 0 0 0 genes genes genes genes

Figure 3. Heterochronic parabiosis does not reverse age-related transcriptomic changes in

tissue-resident alveolar macrophages or alveolar type II cells.

(A) Parabionts were generated from young (6 months, green) and aged (18 months, blue) pairs

and TRAM were harvested after 60 days. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(B) Percentage of circulating CD45+ cells from the young or old parabiont pair determined by

flow cytometry using CD45.1/CD45.2.

heterochronic parabiont pair.

(D) Heatmap shows k-means clustering of differentially expressed genes inTRAM (FDR <0.01

in ANOVA-like test) between old and young mice with isochronic or heterochronic parabiont

pairs (see also Table S4).

(E) Volcano plot showing differentially expressed genes in TRAM from young/young versus

old/old parabiotic pairs (FDR <0.05) (see also Figure S3 and Table S5).

(F) Volcano plot showing differentially expressed genes in TRAM from old/old parabionts

versus the old parabiont from young/old pairings (FDR <0.05) (see also Table S6).

(G) Volcano plot showing differentially expressed genes in TRAM from young/young parabionts

versus the young parabiont from young/old pairings (FDR <0.05) (see also Table S7).

(H) Parabionts were generated from young and aged pairs and alveolar type II cells were

harvested after 60 days.

(I) PCA plot of alveolar type II cells harvested from young or old mice linked to a isochronic or

heterochronic parabiont pair.

(J) Heatmap shows k-means clustering of differentially expressed genes in alveolar type II cells

(FDR <0.01 in ANOVA-like test) between old and young mice with isochronic or heterochronic

parabiont pairs (see also Table S8).

(K) Volcano plot showing differentially expressed genes in alveolar type II cells from

young/young versus old/old parabiotic pairs (FDR <0.05) (see also Figure S3 and Table S9). bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(L) Volcano plot showing differentially expressed genes in alveolar type II cells from old/old

parabionts versus the old parabiont from young/old pairings (FDR <0.05) (see also Table S10).

(M) Volcano plot showing differentially expressed genes in alveolar type II cells from

young/young parabionts versus the young parabiont from young/old pairings (FDR <0.05) (see

also Table S11).

Aging signature overlap Aging signature overlap Parabiosis A Parabiosis B Naïve Aging Naïve Aging Parabiosis Parabiosis 2008 325 149 P=8.9 E-127 1924 438 428 P=3.4 E-89

C D 2.0 AM AT2 P=0.13 2.0 P=0.09 Parabiosis Old in old- 1.5

CD45.1 v.CD45.2 1.5 old pair vs young

condition condition 1.0 old in old−old vs young oldOld in old−old in vs youngold- density density 1.0 old in old−young vs young old in old−young vs young P=6.1 E-5 Density young pair vs young

0.5 0.5

0.0 0.0

−2 0 2 4 −2 −1 0 1 2 logFC logFC logFC logFC

Figure S3. Parabiosis does not reverse age-related transcriptional changes in the lung.

(A) Venn diagram showing overlap of differentially expressed genes in young/young compared

with old/old parabiont pairs and differentially expressed genes in TRAM identified between 4

month and 18 month old mice aged in our colony (from Figure 2).

(B) Venn diagram showing overlap of differentially expressed genes in young/young compared

with old/old parabiont pairs and differentially expressed genes in alveolar type II cells identified

between 4 month and 18 month old mice aged in our colony (from Figure 2).

in young/young compared with old/old pairs with differentially expressed genes (with FDR

<0.01) in TRAM from CD45.1 and CD45.2 alveolar macrophages.

(D) Distribution of log-fold change showing no significant difference (p>0.05 by Wilcoxon rank

sum test) for differentially expressed genes in TRAM and AT2 identified between 6 month and

18 month-old mice aged in our colony (from Figure 2) and heterochronic parabiont pairs. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Chronic administration of metformin induces only small changes in the aging transcriptome of

tissue-resident alveolar macrophages and alveolar type II cells. Metformin has been shown to

extend lifespan and improve healthspan in mice (Martin-Montalvo et al., 2013). We have

previously reported that the short-term administration of metformin resulted in few changes in

baseline gene expression in TRAM but enhanced the expression of chaperone after exposure to

particulate matter air pollution (Soberanes et al., 2019). To determine whether chronic treatment

with metformin could reverse age-related changes in gene expression in TRAM or alveolar type

II cells, we treated young (4 month) or old (18 month) mice with metformin for 60 days in the

drinking water and flow-sorted TRAM and alveolar type II cells for RNA-Seq (Figure 4B-G).

We found that the administration of metformin induced relatively few changes in gene

expression in TRAM from young or old mice (130 and 134 genes, respectively, FDR q < 0.05,

Figure 4C-E). Despite the small number of genes, there was significant overlap with genes

associated with aging (37 genes, P < 0.01 by hypergeometric test, Table S12) and these genes

directionally moved toward a younger age (Table S12). The genes included some with potential

biologic relevance to aging including Il6ra, Cflar, important for monocyte to macrophage

differentiation, the calcium uniporter Mcub, and the cyclins Cdk13 and Ccng2. In alveolar type II

cells there were similar small changes in gene expression with metformin (116 and 79,

respectively, Figure 4H-J). Of these, 37 of the 97 genes overlapped with those that changed

during aging (including Socs3, Junb, and Hmox1 (P=0.01)). These results suggest that while

metformin has modest effects on basal gene expression it does alter the expression of some age-

related genes. bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A Young Naive Young Metformin

Metformin Old Naive 60 days Old Metformin

B F

Alveolar Alveolar Macrophage Type II Cell PCA plot metformin AM PCA plot metformin AT2

age age

Old 0 Old

Young Young

treatment treatment −20 Metformin Metformin PC2: 20.3% variance PC2: 17.5% variance

Naive Naive

−20 PC1: 20.3%PC1: variance Color Key 17.5%PC1: variance Color Key Metformin AM Metformin AT2 Kmean 4 ANOVA FDR<0.05 −40 Kmean 4 ANOVA FDR<0.05

−20 0 20 −30 −20 −10 0 10 20 PC1: 31.4%PC1: 31.4% variance variance PC1: 30.5%PC1: 30.5% variance variance

-2 -1 0 1 2 Y Y/Met O O/Met-2 -1 0 1 2 Value C Value G Y Y/Met O O/Met 217 49 48 114 gene 175 gene 115 205 42

D Young naive Young metformin H Young naive Young metformin vs. vs. Kmeans2$cluster X01_AT2_Y_N_R1 X02_AT2_Y_N_R2 X03_AT2_Y_N_R3 X04_AT2_Y_N_R4 X05_AT2_Y_M_R1 X06_AT2_Y_M_R2 X07_AT2_Y_M_R3 X08_AT2_Y_M_R4 X09_AT2_Y_M_R5 X11_AT2_O_N_R1 X12_AT2_O_N_R2 X13_AT2_O_N_R3 X14_AT2_O_N_R4 Kmeans2$cluster X15_AT2_O_M_R1 X17_AT2_O_M_R3 X19_AT2_O_M_R5 X01_AM_Y_N_R1 X02_AM_Y_N_R2 X03_AM_Y_N_R3 X04_AM_Y_N_R4 X05_AM_Y_M_R1 X06_AM_Y_M_R2 X07_AM_Y_M_R3 X08_AM_Y_M_R4 X09_AM_Y_M_R5 X11_AM_O_N_R1 X12_AM_O_N_R2 X13_AM_O_N_R3 X14_AM_O_N_R4 X15_AM_O_M_R1 X17_AM_O_M_R3 X19_AM_O_M_R5 10 10 Up in naive: 42 Up in metformin: 88 Up in naive: 39 Up in metformin: 77 GO proc es s es GO proc es s es Uhrf2 Tfap4 Nr4a3 Ciita Negative regulation Antigen processing and Klf6 Insig1 Tnfaip8l3 Batf3 of metabolic process 8 8 Dusp16 Cpne5 presentation Fpr1 Negative regulation Irx1 Slc7a5 Negative regulation Hbp1 of immune system process of signaling 6 6 LogFDRval LogFDRval 4 4 − − 2 2 0 0

−10 −5 0 5 10 −10 −5 0 5 10

logFC logFC Old naive Old metformin Old naive Old metformin E vs. I vs.

10 Up in naive: 28 10 Up in naive: 15 Up in metformin: 119 Up in metformin: 51 Ighm Nav2 Gramd2 Igkc Crebrf Arntl GO proc es s es Npas2 Regulation of PI3 Arl4c 8 GO proc es s es 8 Positive regulation Tmem171 kinase activity Per2 of RNA metabolic process Klhl13 Circadian rhythm Ylpm1 Positive regulation of Tnfaip8l3 6 macromolecule biosynthetic 6 process LogFDRval LogFDRval 4 4 − − 2 2 0 0

−10 −5 0 5 10 −10 −5 0 5 10 logFC logFC bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 4. Metformin results in modest differences in age-related changes in the

transcriptome of tissue-resident alveolar macrophages and alveolar type II cells.

(A) Young (4 month) and old (18 month) mice were treated with metformin in the drinking water

for 60 days and TRAM and alveolar type II cells were harvested for RNA-Seq.

(B) PCA plot of transcriptomes of TRAM from animals in the four conditions.

(FDR <0.01 in ANOVA like test) (see also table S13).

(D) Volcano plot shows differences in alveolar macrophages from young mice treated with

metformin (FDR <0.05). Individual genes are highlighted, and GO processes are shown in the

center (See table S14 for a full list of genes and enriched GO processes).

(E) Volcano plot shows differences in TRAM from old mice treated with metformin (FDR

<0.05). Individual genes are highlighted. (See table S15 for a full list of genes and enriched GO

processes).

(F) PCA plot of transcriptomes of alveolar type II cells from animals in the four conditions.

(G) Heatmap shows differentially expressed genes in alveolar type II cells from metformin

treated animals (FDR<0.01 in ANOVA like test) (see also Table S16).

(H) Volcano plot shows differences in alveolar type II cells from young mice treated with

metformin (FDR <0.05). Individual genes are highlighted, and GO processes are shown in the

center. See Table S17 for a full list of genes and enriched GO processes.

(I) Volcano plot shows differences in alveolar type II cells from old mice treated with metformin

(FDR<0.05). Individual genes are highlighted (see Table S18 for a full list of genes and enriched

biological GO processes).

Differences between tissue-resident and monocyte-derived alveolar macrophages persist during

aging.

We took advantage of the small but stable chimerism in our shielded bone marrow chimeric mice

to compare transcriptomic changes with aging in MoAM and TRAM, which were similar (Figure

5A). Nevertheless, a number of genes were differentially expressed over time, and, indeed,

further diverged with aging (Cluster IV and V, Figure 5A). These MoAM were evenly

distributed in the lung tissue (Figure 5B), thus, the observed differences in the transcriptome of

MoAM and TRAM likely reflect their ontogeny. We observed significant overlap between these

differentially expressed gene and genes we previously reported to be differentially expressed

between TRAM and MoAM 10 months after bleomycin-induced lung injury (Misharin et al.,

2017) (Figure 5C). Similarly we observed all of the genes identified as differentially expressed

between MoAM and TRAM after liposomal clodronate using microarray technology (Gibbings

et al., 2015). There was little overlap between these genes and those differentially expressed

genes TRAM harvested from CD45.1 and CD45.2 mice (Figure S5). These results reveal

consistent transcriptional differences between TRAM and MoAM residing in the same

microenvironment irrespective of the stimulus that led to their recruitment. While several groups

have associated histone modifications in the differentiation of monocytes to macrophages and in

the development of innate immune memory, the importance of DNA methylation in this process

has not been explored (Gosselin et al., 2014; Lavin et al., 2014; Morales-Nebreda et al., 2019;

Netea et al., 2016; Quintin et al., 2012; Saeed et al., 2014; Yoshida et al., 2015). Accordingly, we

performed reduced representation bisulfite sequencing on TRAM and MoAM from aged

chimeric animals. We looked for evidence of differential DNA methylation in promoter regions

1000 bp upstream and downstream of the start site of differentially expressed genes in our RNA- bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Seq dataset and in putative enhancer regions specific to alveolar macrophages defined as

H3K4me1 peaks identified by Lavin et al. exclusive of promoter regions (Figure 5D) (Lavin et

al., 2014; Weinberg et al., 2019). We found no evidence of differences in these regions.

GO processes 3 months 6 months 18 months 24 months Genes Nudfa1 Mitochondrial respiratory Stat3 chain complex I assembly Hif1a Antigen processing and Sod2/Sod1 presentation Csf1 Complement activation CD200r1 Cholesterol metabolic CD68 process genes) (1162 I Msr1

Positive regulation of Sp1 transcription by RNA Axl polymerase II Cdk12 Chromatin organization Tgfbr1 Histone modification Mertk Positive regulation of cell Pten motility genes) (1528 II Rxra

Cell migration Apoe Regulation of cell Klf4 proliferation Notch4 Dnmt1 CD163 Mitotic cell process Csf1r Cell division (496) III Ccna2 Clec4a2

Lipid catabolic process Cd36 Ion transport Hmox1 Sirt2

IV (836 genes) (836 IV Ripk1 Marco

TRAM MoAM TRAM MoAM TRAM MoAM TRAM MoAM B IV (939 genes) (939 IV CD45.1 C

CD45.2 Cluster IV/V Misharin et al.

P<6.4 E-82

D Average methylation around Average methylation around AverageAverage methylation methylation around around cluster 4 TSS cluster 5 TSS non-promoternon-promoter enhancer enhancer peaks peaks

80 CD45.2 CD45.1 70

50 AMdonorNaive AMrecipientNaive 40

30 CpG methylation *beta * 100) * methylation*beta CpG 20

-4 kb -2 kb Peak +2 kb +4 kb center bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 5. Transcriptional differences between monocyte-derived alveolar macrophages and

tissue-resident alveolar macrophages persist over the lifespan.

(A) Alveolar macrophages from shielded chimeric mice were harvested at the indicated ages and

TRAM and MoAM were flow sorted based on the CD45.1 or CD45.2 label. Differentially

expressed genes (FDR <0.01 in ANOVA-like test) were identified and subjected to k-means

clustering. Selected genes and GO processes from each cluster are highlighted (see also Table

S19 for full list of genes and GO processes).

(B) Representative immunofluorescence image of a lung section from a shielded chimeric mouse

(6 months of age) stained for CD45.2 to mark TRAM and CD45.1 to mark MoAM. Combined

image overlaid on phase contrast image is shown. Scale bar is 10 µm.

TRAM in this model with an independent dataset (Misharin et al., 2017) collected 10 months

after bleomycin exposure.

(D) Reduced representation bisulfite sequencing was performed on TRAM and MoAM from 6

month-old mice. The frequency of methylated CpG motifs in promoter regions within 1000 bp

upstream and downstream of the transcriptional start site of differentially expressed genes

between TRAM and MoAM in shielded chimeric mice was compared with their frequency

across the genome. A similar analysis was performed using putative enhancer regions specific to

alveolar macrophages defined as consensus H3K4me1 peaks by (Lavin et al., 2014). No

significant differences in DNA methylation were detected.

De Gene w/ FDR<0.01 overlap A 79 All clusters CD45.1/CD45.2 33 4612

P=7.46 E-12

De Gene w/ FDR<0.01 overlap 37 B Cluster IV/V 75 CD45.1/CD45.2 1738

P=4.25 E-7

Figure S5. Differences in CD45.1/CD45.2 strains do not explain differential gene expression

in tissue-resident alveolar macrophages and monocyte-derived alveolar macrophages from

aged shielded chimeric mice.

(A) Venn diagram showing overlap between all differentially expressed genes in TRAM and

MoAM from shielded chimeras in aging (See Figure 5A) with those differentially expressed in

TRAM from untreated CD45.1 and CD45.2 mice.

(B) Venn diagram showing overlap between differentially expressed genes in Clusters IV and V

of Figure 5A (comparison of TRAM and MoAM in aging shielded chimeras) with those

differentially expressed in TRAM from untreated CD45.1 and CD45.2 mice.

Sequential injury models provide a physiologically relevant model to determine whether alveolar

macrophage ontogeny affects the response to repeated environmental challenge in aging. The

findings above suggest that in some individuals, episodic lung injury over the lifespan could

result in at least two transcriptionally distinct populations of alveolar macrophages within the

same microenvironment. We and others have reported that MoAM play a causal role in the

development of bleomycin-induced lung fibrosis and influenza A virus-induced pneumonia (Kim

et al., 2008; McCubbrey et al., 2018; Misharin et al., 2017). We therefore reasoned that the

induction of ontological chimerism after infection with the influenza A virus or treatment with

bleomycin (Figure 1G) would provide a physiologically relevant system to determine whether

differential responses of MoAM and TRAM to a second stimulus underlie the observation of

innate immunologic memory. Accordingly, we generated a cohort of shielded chimeric mice and

subjected them to sequential injury models (Figure 6A,E). We harvested animals and performed

flow cytometry sorting of TRAM and MoAM and analyzed them using RNA-Seq (Figure S6).

Principal component analysis of the entire dataset demonstrates good reproducibility in the data

and shows markedly different responses of MoAM and TRAM to influenza A virus-induced

pneumonia and bleomycin-induced pulmonary fibrosis (Figure 6B,F).

Ontogeny alters the response of alveolar macrophages to influenza A virus-induced pneumonia

and bleomycin-induced pulmonary fibrosis. In response to acute infection with influenza A virus

(4 days) TRAM and MoAM upregulated the expression of genes involved in the immune

response to virus and downregulated genes involved in macrophage homeostasis (Figure 6C,

Cluster I and II). Some of these genes were exclusively upregulated or downregulated in MoAM

relative to TRAM (Figure 6C, Cluster III, IV). In addition, the fold-change in the expression of bioRxiv preprint doi: https://doi.org/10.1101/717033; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

almost all of these genes was more pronounced in MoAM relative to TRAM as reflected by the

average Z-score for the expression of upregulated and downregulated genes in response to

influenza A virus infection (Figure 6D). Similar differences in the expression of profbrotic and

homeostatic macrophage genes were observed in TRAM compared with MoAM during

bleomycin-induced pulmonary fibrosis (Figure 6E-H). These results highlight an important role

for ontogeny in the phenotype of MoAM and TRAM in the acute phases of lung injury and

fibrosis.

PCA plot double hit flu−bleo exp

A Ontogeny B CD45.1 CD45.2 TRAM 10 MoAM1 + CD45.1 MoAM Siglec Flow CD45.1+ 2 Siglec Flow Time Before first 0 injury After first injury Before second CD45.1+ injury −10 Siglec Fhigh After second injury PC2: 14.8% variance

CD45.2+ Siglec Fhigh −20 Color Key Influenza A Kmeans 4 ANOVA FDR<0.01Bleomycin sig genes

−30

−20 0 20 40 T1 T2 PC1: 51% variance T3 T4 −3 −1 0 1 2 3 Value C Naive After 1st influenza A D TRAM (naive)

Hk2 TRAM (influenza A infection) Car4/Msr1/Csf1 Defense response Stat3 Response to virus MoAM (influenza A infection) Nampt Cellular response to Arg2 interferon beta 2 Antigen processing

816 Ifngr2 Il1b/Il1a and presentation Tlr3/Tlr4/Myd88 Traf6 1

Tgfb1 Cholesterol metabolic process

gene Regulation of myeloid leukocyte Pdk1 differentiation

378 Rara 0

Cdc45 Regulation of transcription Keap1 Dnmt1 Antigen processing and presentation 273 -1 Average Z-Score Average Cd36 Marco Mfge8 Phagocytosis, recognition 348 SiglecF Cell recognition -2 I II III IV

TRAM TRAM (IAV) MoAM (IAV) PCA plot double hit bleo−bleoCluster exp

Ontogeny E CD45.1 F CD45.2 TRAM

MoAM1 + CD45.1 MoAM SC006_T2_F_AMR_R1 SC007_T2_F_AMR_R2 SC008_T2_F_AMR_R3 SC009_T2_F_AMR_R4 SC010_T2_F_AMR_R5 SC006_T2_F_AMD_R1 SC007_T2_F_AMD_R2 SC008_T2_F_AMD_R3 SC009_T2_F_AMD_R4 SC010_T2_F_AMD_R5 2 10 SC024_T1_N_AMR_R1 SC025_T1_N_AMR_R2 SC026_T1_N_AMR_R3 SC027_T1_N_AMR_R4 SiglecSC028_T1_N_AMR_R5 SC001_T1_N_AMR_R1 FlowSC002_T1_N_AMR_R2 SC003_T1_N_AMR_R3 SC004_T1_N_AMR_R4 SC005_T1_N_AMR_R5 CD45.1+ Siglec Flow Time Before first injury After first injury 0 Before second CD45.1+ injury Siglec Fhigh After second injury PC2: 14.8% variance

CD45.2+ −10 Siglec Fhigh

Color Key Bleomycin Bleomycin Kmean 4 ANOVA FDR<0.01 sig genes

−20

−20 0 20 40 T1 T2 PC1: 51% variance T3 T4

−3 −1 0 1 2 3 Value G H TRAM (naive) Naive After 1st Bleomycin Fasn Lipid biosynthetic process Gas6 Cell differentiation TRAM (bleomycin) Mvk Glucose homeostasis 658 Foxo3 MoAM (bleomycin) Pdgfb Locomotion Hk2 Response to chemical 1.5 Loxl3 Cell Migration Mmp12 Regulation of cytokine Timp1 production 1.0 Cav1 Regulation of cell adhesion 2336 Spp1 Response to wounding 0.5

Rac1 Nucleic acid metabolic gene Cdka1 process 0.0 Pola1 RNA splicing

1017 Sfpq Cell cycle -0.5

Pparg Z-Score Average Cdh1 Lipid oxidation -1.0 Marco Cell differentiation

1909 Mtor -1.5 I II III IV Cluster TRAM TRAM (Bleo) MoAM (bleo) SC029_T2_B_AMR_R1 SC030_T2_B_AMR_R2 SC031_T2_B_AMR_R3 SC032_T2_B_AMR_R4 SC033_T2_B_AMR_R5 SC029_T2_B_AMD_R1 SC030_T2_B_AMD_R2 SC031_T2_B_AMD_R3 SC032_T2_B_AMD_R4 SC033_T2_B_AMD_R5 SC024_T1_N_AMR_R1 SC025_T1_N_AMR_R2 SC026_T1_N_AMR_R3 SC027_T1_N_AMR_R4 SC028_T1_N_AMR_R5 SC001_T1_N_AMR_R1 SC002_T1_N_AMR_R2 SC003_T1_N_AMR_R3 SC004_T1_N_AMR_R4 SC005_T1_N_AMR_R5

Figure 6. Alveolar macrophage ontogeny determines responses to influenza A virus-

induced pneumonia and bleomycin-induced pulmonary fibrosis.

(A) Experimental design for repeated injury experiments using influenza A virus infection as a

first stimulus (see also Figure S6). (B) PCA of alveolar macrophage transcriptomes. Colors and symbols refer to panel A.

TRAM 4 days after influenza A-induced pneumonia, and recruited MoAM at the same time

(FDR < 0.01 in ANOVA-like test). Representative genes and GO processes from four clusters are shown on the right. See also Table S20.

(D) Average Z-score for genes in each cluster from Panel C. Colors refer to cell populations as indicated. Z-score reflects the average gene expression within a given cluster in TRAM from naïve animals (n=10), TRAM four days after influenza A virus infection (n = 5) and MoAM recruited four days after influenza A virus infection (n=5). Mean value is shown, error bars indicate 95% confidence interval.

(E) Experimental design for repeated injury experiments using intratracheal bleomycin as a first injury. Similar to A, but mice were treated with two sequential doses of intratracheal bleomycin separated by 60 days.

(F) PCA of alveolar macrophage transcriptomes. Colors and symbols refer to panel E.

(G) Heatmap shows k-means clustering of differentially expressed genes between naïve TRAM,

TRAM 21 days after bleomycin-induced lung injury, and recruited Mo-AM at the same time

(FDR < 0.01 in ANOVA like test). Representative genes and GO processes from four clusters are shown on the right. See also Table S21.

(H) Average Z-score for each cluster from panel G. Colors refer to cell populations as indicated.

Z-score reflects the average gene expression in a given cluster from naïve TRAM (n=10), TRAM

21 days after bleomycin (n = 5) and MoAM recruited 21 days after bleomycin (n = 5).

A Ontogeny CD45.1 CD45.2 TRAM

MoAM1 + CD45.1 MoAM Siglec Flow CD45.1+ 2 Siglec Flow Time Before first injury After first injury Before second CD45.1+ injury Siglec Fhigh After second injury

CD45.2+ Siglec Fhigh

Influenza A or Bleomycin Bleomycin

T1 B T2 T3 T4

Population Time (Days) Markers Symbol(s) Tissue Resident alveolar macrophages 0, 21, 60, 81 CD45.2+SiglecFhigh Monocyte-derived alveolar macrophages recruited to the 4 (influenza), 21 CD45.1+SiglecFlow lung after the first injury (bleomycin) Monocyte derived alveolar macrophages recruited to the 64 (influenza), 81 CD45.1+SiglecFlow lung after the second injury (bleomycin) Mo-AM persisting after first injury 60 (both stimuli) CD45.1+SiglecFhigh Mo-AM persisting after first injury responding to second 64 (influenza), 81 CD45.1+SiglecFhigh injury (bleomycin)

Figure S6. Strategy to examine differential responses in MoAM and TRAM.

(A) Experimental design. Mice were administered either intratracheal bleomycin or infected with influenza A virus on day 0 followed by treatment with bleomycin on day 60. Alveolar macrophage populations were collected as indicated. Refers to Figure 6.

(B) Flow cytometry gating was performed as previously described (see Misharin et al., 2017).

Table describes flow markers used to sort cell populations for RNA-Seq in Figure 6.

The local microenvironment shapes monocyte-derived alveolar macrophages during the resolution of lung injury. We next examined the differences between MoAM and TRAM 60 days after the administration of bleomycin or influenza A infection, when lung injury is resolving.

Twenty-one days after bleomycin induced lung injury, MoAM (CD45.1+) were primarily localized to areas of injury (Figure 7A). Mo-AM recruited during lung injury persisted during the resolution of influenza A or bleomycin-induced injury (60 days after administration, Figure 7B).

At this time point there were significantly fewer differentially expressed genes between MoAM and TRAM irrespective of the initial injury (Figure 7C for bleomycin, Figure 7E for influenza A infection). TRAM harvested 60 days after injury had incompletely resolved their changes in gene expression induced by either influenza A infection or bleomycin administration when compared with TRAM from naïve mice. Many of the genes that differed between TRAM and MoAM during resolution still represented stimulus-specific genes and processes, a common set of differentially expressed genes was observed between MoAM and TRAM, which were similar to those we identified in normal aging. Interestingly, GO processes and genes that distinguished

MoAM and TRAM at these later time points were similar irrespective of the injury (Figure 7C-

F).

Ontogeny CD45.1 A B CD45.2 TRAM MoAM1 + CD45.1 MoAM Siglec Flow CD45.1+ 2 Siglec Flow CD45.1 Time Before first injury CD45.2 After first injury Before second CD45.1+ injury Siglec Fhigh After second injury

CD45.2+ Siglec Fhigh

Influenza A or Bleomycin Bleomycin

T1 T2 T3 T4

C 60 Days after bleomycin D TRAM (naive) Ptger4 Mitotic cell cycle process MoAM (60 days after bleomycin) Top2b Chromosome organization TRAM (60 days after bleomycin) Cdk1 Cell differentiation 2 Nuf2

I (1375) Camk4 1

Mr1 Response to external stimulus 0 Arg1 Regulation of response to stress ApoE Lipid metabolic process H2-M3 -1 Average Z-Score Average Color Key C1rb II (1539) T1 T3 AMR AMD Flu Hif1a -2 Kmean 2 ANOVA FDR<0.01 sig genes I II Cluster TRAM MoAM1 TRAM 1861 genes −3 −1 0 1 2 3 Value E 60 Days after influenza A Elbow plot F TRAM (naive) MoAM (60 days after influenza A virus infection) Tlr2 Cytokine Secretion involved in immune Custer1 834 genes NLRP3 response TRAM (60 days after influenza A virus infection) Top2b Multicellular organismal development 2

I (834) Cdk1 Positive regulation of cell differentiation Nuf2 1

Mr1gene Regulation of response to stimulus Arg1 Response to stress 0 ApoE Lipid metabolic process Custer2 1027 genes H2-M2 -1 Cav1 Z-Score Average II (1027)

Hif1a squares of sum within-clusters Total -2 I II 10000 15000 20000 25000 30000 Cluster TRAM MoAM1 TRAM 2468 10 12 14 Number of clusters K Figure 7. Monocyte-derived alveolarKmeans2$cluster macrophages and tissue-resident alveolar SC011_T3_F_AMD_R1 SC012_T3_F_AMD_R2 SC013_T3_F_AMD_R3 SC014_T3_F_AMD_R4 SC011_T3_F_AMR_R1 SC012_T3_F_AMR_R2 SC013_T3_F_AMR_R3 SC014_T3_F_AMR_R4 SC024_T1_N_AMR_R1 SC025_T1_N_AMR_R2 SC026_T1_N_AMR_R3 SC027_T1_N_AMR_R4 SC028_T1_N_AMR_R5 SC001_T1_N_AMR_R1 SC002_T1_N_AMR_R2 SC003_T1_N_AMR_R3 SC004_T1_N_AMR_R4 SC005_T1_N_AMR_R5

TR-AM Naive Mo-AM Flu TR-AM Flu before 2nd hit before 2nd hit macrophages become increasingly similar during the resolution of lung injury.

(A) Lung sections from shielded chimeric mice 21 days after the administration of bleomycin

were analyzed using immunofluorescent microscopy with antibodies against CD45.2 to label

TRAM (yellow) or CD45.1 to label MoAM (red). As demonstrated in the representative section,

CD45.1 MoAM were disproportionately represented in areas of injury/fibrosis compared to areas

that were relatively free of fibrosis. (B) Schematic of the experimental design. The red frame indicates the populations compared in panels C-F.

(C) Heatmap shows k-means clustering of differentially expressed genes (FDR < 0.01) in TRAM and MoAM retained after bleomycin-induced pulmonary fibrosis. Naïve TRAM are included as a comparison. Representative genes and GO processes are shown for each cluster (see also table

S22).

(D) Average Z-score for each cluster according to cell type and condition (naive TRAM, n=10,

MoAM1, n=5, TRAM after bleomycin, n=5).

(E) Heatmap shows k-means clustering of differentially expressed genes (FDR < 0.01) in TRAM and MoAM retained after influenza A-induced pneumonia. Naïve TRAM are included as a comparison. Representative genes and GO processes are shown for each cluster (see also table

S23).

(F) Average Z-Score for each cluster according to cell type and condition (naive TRAM, n=10,

MoAM1, n=4, TRAM after bleomycin, n=4).

The response to a second injury is independent of alveolar macrophage ontogeny. We next compared the response of TRAM and newly resident MoAM (recruited after the first injury) in response to a second dose of bleomycin (Figure 8). The response of TRAM and newly resident

MoAM to bleomycin was largely similar irrespective of their ontogeny and irrespective of whether they were recruited in response to bleomycin (Figure 8A,B) or influenza A virus infection (Figure 8C,D). Despite the similarity in their responses, the differences in gene expression between TRAM and MoAM persisted during the injury. These changes were similar to the differences between TRAM and MoAM during aging and the differences between MoAM and TRAM recruited during the first injury (Figure 8E).

Ontogeny A CD45.1 B CD45.2 TRAM Marco 80 Up in TRAM: 199 Up in MoAM : 154 Chil1 MoAM1 1 + CD45.1 MoAM Prox1 Serpin8 Siglec Flow CD45.1+ 2 Ppfia4 Ctse Siglec Flow Time Apoc1 Bcl2

Before first Slc1a3 60 Gas8 injury Serpinb8 After first Cd209b Ppp1r12b Slamf7 injury Before second Cxcr2 CCr2 CD45.1+ injury SiglecF 40 Fabp7 high Siglec F After second LogFDRval Steap3 − Wwtr1 injury Sema3e Wdfy1 Chil1 + Tcaf2 Ptprv CD45.2 Tcaf1 Siglec Fhigh 20

Bleomycin Bleomycin 0

−10 −5 0 5 10

T1 T2 T3 T4 logFC

Ontogeny C CD45.1 D CD45.2 TRAM 80 MoAM1 Marco Up in TRAM: 287 Up in MoAM1: 190 Chil1 + CD45.1 MoAM Prox1 Serpin8 Siglec Flow CD45.1+ 2 Siglec Flow Time Apoc1 Bcl2 Chil1 Before first 60 injury Slc1a3 Ctse Gas8 After first Cd209b Slamf7 injury Before second Cxcr2 ApoE

+ injury 40 CD45.1 Serpinb8 high SiglecF Pdgfa

Siglec F After second LogFDRval Tcaf1 − Wwtr1 injury Sema3e Wdfy1 Mmp12 Marco + Bmpr1a CD45.2 Gm11033 Siglec Fhigh 20

Influenza A Bleomycin 0

Gene Overlap Mo-AM retain vs TR-AM FDR<0.01 −10 −5 0 5 10

T1 T2 T3 Flu-Bleo T4 logFC Bleo-Bleo E Influenza A-Bleomycin Bleomycin-Bleomycin 138 93133

Figure 8. The microenvironment determines the response of alveolar macrophages to bleomycin-induced pulmonary fibrosis irrespective of ontogeny.

(A) Schematic of the experimental design for panel B.

(B) Volcano plot shows differentially expressed genes (FDR<0.05) between TRAM and MoAM

(recruited in response to historic bleomycin exposure as the first injury) after the second injury with bleomycin. Representative genes are shown adjacent to the plot. See Table S24 for a full list of genes. (C) Schematic of the experimental design for panel D.

(D) Volcano plot shows differentially expressed genes (FDR<0.05) between TRAM and MoAM

(recruited in response to historic influenza A virus-induced pneumonia as the first injury) after the second injury with bleomycin. Representative genes are shown adjacent to the plot. See

Table S25 for a full list of genes.

(E) Venn diagram shows overlap between genes differentially expressed between TRAM and

MoAM recruited in response to historic bleomycin or influenza A infection after a second injury with bleomycin. P value= 4.43e-173.

Repeated injury to the microenvironment shapes the response of monocyte-derived alveolar macrophages during bleomycin-induced fibrosis. Repeated administration of bleomycin followed by recovery has been shown to result in more severe and persistent injury when compared with a single dose of bleomycin (Degryse et al., 2010). Consistent with these observations, we found that MoAM recruited to the lungs of animals historically exposed to bleomycin differed from those recruited to the lungs of naïve animals (Figure 9A, B). In contrast, the response of MoAM to bleomycin as a second injury was almost identical in mice historically exposed to influenza A infection and naïve mice (Figure 9C,D). A comparison of differentially expressed genes in mice historically exposed to bleomycin or influenza A virus identified genes/GO processes associated with fibrosis (Figure 9C-F). In contrast, TRAM demonstrated evidence of immune tolerance irrespective of the previous exposure (Figure 10) or ontogeny

(Figure S7) as their proinflammatory response to the second injury was diminished in comparison to a naïve mouse. These results highlight the importance of both the microenvironment and ontogeny in determining the response of both MoAM and TRAM to a repeated immunologic challenge implicating the microenvironment in the development of innate immune memory. A B Ontogeny CD45.1 + CD45.1 CD45.2 CD45.1+ TRAM low Siglec F Siglec Flow Negative Siglecf 30 Ppfia4 Positive MoAM1 Up in bleomycin: 181 Up in historic influenza A: 230 Influenza A regulation of Nlrp3 2 Gm15590 regulation of MoAM2 Historic Influenza A biological Per1 mt-Atp6 nucleotide Bleomycin 25 Time Historic Bleomycin Before first process Crem Ppfia4 metabolic injury Regulation of process After first cellular 20 injury Before second CD45.1+ process injury Siglec Fhigh 15

After second LogFDRval injury − Gm15590 10 CD45.2+ Siglecf Ets2 Siglec Fhigh Fkbp5 Cyp2f2 5 Influenza A or Bleomycin Bleomycin 0

−10 −5 0 5 10 T1 T2 T3 T4 logFC C D Ontogeny CD45.1 + CD45.1 CD45.2 CD45.1+ TRAM low Siglec F low Siglec F Cell cycle Fkbp5 80 Up in bleomycin: 1069 Up in historic bleomycin: 1570 Ier2 Cell adhesion MoAM1 Influenza A Siglecf Tnf Regulation of MoAM2 Historic Influenza A Ier2 Bleomycin Top2a Egr1 cell migration Time Historic Bleomycin Cdk1 Tnf Nfkbia Extracellular Before first 60 injury Tra2b Egr1 Postn structure After first Nfkbia Cxcl1 organization injury Postn Before second + Mmp15 Angiogenesis

CD45.1 40 injury Siglec Fhigh Ccl2 Tissue LogFDRval After second − development injury Neil3 CD45.2+ Gpr141 Snn Siglec Fhigh 20 Siglecf Influenza A or Bleomycin Bleomycin 0

−10 −5 0 5 10

T1 T2 T3 T4 logFC

E Ontogeny CD45.1 F + CD45.1 CD45.2 CD45.1+ TRAM low Siglec F Siglec Flow MoAM1 Response Siglecf Postn Cell adhesion 80 Up in historic flu: 348 Up in historic bleomycin: 1173 Influenza A MoAM2 Historic Influenza A to Per3 Kit Cell migration Gm14023 Bleomycin interferon Mif Pcdh1 Extracellular Time Historic Bleomycin Serping1 Before first beta Itgb6 matrix injury 60 Il1a Loxl2 organization After first Ier2 Col1a1 injury Col6a2 Mmp3 Extracellular Before second CD45.1+ structure injury high Siglec F 40 After second organization LogFDRval

injury − CD45.2+ Siglec Fhigh Ppfia4 20

Influenza A or Gm15590 Bleomycin Bleomycin 0

−10 −5 0 5 10 T1 T2 T3 T4 logFC

Figure 9. The environment determines the response of newly recruited monocyte-derived alveolar macrophages to repeated injury.

(A) Schematic for experimental design for panel B. The comparison focuses on MoAM recruited to naïve lungs with those recruited to lung in historically exposed to influenza A virus.

(B) Volcano plot comparing MoAM recruited to naïve lungs with those recruited to lungs historically treated with influenza A virus (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S26 for a full list of genes.

(C) Schematic for experimental design for panel D. The comparison focuses on MoAM recruited to naïve lungs with those recruited to lungs historically exposed to bleomycin. (D) Volcano plot comparing MoAM recruited to naïve lungs with those recruited to lungs historically exposed to bleomycin (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S26 for a full list of genes.

(E) Schematic for experimental design for panel F. Comparison between MoAM recruited in response to bleomycin in mice historically treated with bleomycin and those historically treated with influenza A infection.

(F) Volcano plot comparing MoAM recruited in response to bleomycin in mice treated historically with influenza A virus or bleomycin (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S26 for a full list of genes.

A Ontogeny B TRAM CD45.1 CD45.2

MoAM + Signal transduction Apoe 80 Malat1 1 CD45.1 CD45.1+ Up in bleomycin: 1136 Up in historic influenza A: 992 Siglec Flow Siglec Flow Inflammatory Itgam Plekha6 MoAM2 Thbs1 Time response Folr2 Gpc1 Adam8 Card11 Before first Regulation of cell Ccl8 Macf1 injury Tmem176a motility Gas6 60 Abca9 After first Ccl8 injury Mmp12 Il7r Fgl2 Before second CD45.1+ injury Runx3 Siglec Fhigh After second Csf1 injury C1qc 40 + CD45.2 Influenza A Historic Influenza A LogFDRval Siglec Fhigh − Bleomycin Historic Bleomycin Galnt9 Malat1 Influenza A or Bleomycin Bleomycin 20 Gm42418 Gm10513 Cd209f

T1 T2 T3 T4

0 Ighg2b

−10 −5 0 5 10

logFC

Ontogeny

C D 80 TRAM CD45.1 Response to Fcrls Up in bleomycin: 1268 Up in historic bleomycin: 966 Lif Lipid metabolic CD45.2 MoAM + external biotic Folr2 Tnf process 1 CD45.1 CD45.1+ Aoah Siglec Flow low Cxcl1 MoAM2 Siglec F stimulus Itgam Regulation of cell Tmem176a Gdf15 Time Regulation of cell Apoe Folr2 proliferation Before first migration Ccl8 60 Ms4a7 injury Runx3 After first Response to injury stress Il7r Before second CD45.1+ Ctse

injury 40 Siglec Fhigh After second Gdf15 LogFDRval

injury − CD45.2+ Influenza A Historic Influenza A Siglec Fhigh Tnf Bleomycin Historic Bleomycin 20 Ereg Influenza A or Bleomycin Bleomycin Cxcl1 Cd209f 0

T1 T2 T3 T4 −10 −5 0 5 10

logFC

E Ontogeny F

80 Tnf Inflammatory TRAM CD45.1 Cell cycle process Macf1 Up in historic flu: 566 Up in historic bleomycin: 504 CD45.2 Il6 MoAM + Ubr4 response 1 CD45.1 CD45.1+ Chromatin low MoAM Siglec F Siglec Flow Malat1 Ier2 Regulation of cell 2 organization Egr1 Time Marco death Before first 60 Clec4e Regulation of cell injury Socs3 migration After first injury Jun Regulation of cell Before second Tnf CD45.1+ Il1b adhesion

injury 40 Siglec Fhigh Ier2 Il6 After second Cxcl2 LogFDRval injury − Nfkbiz CD45.2+ Influenza A Historic Influenza A Macf1 Egr1 Siglec Fhigh Bleomycin Historic Bleomycin 20 Kcnq1ot1 Il28a Influenza A or Bleomycin Bleomycin 0

T1 T2 T3 T4 −10 −5 0 5 10 logFC

Figure 10. Tissue-resident alveolar macrophages demonstrate evidence of immune tolerance irrespective of the previous exposure.

(A) Schematic for experimental design for panel B. The comparison focuses on TRAM from naïve lungs with those from the lungs historically treated with influenza A virus.

(B) Volcano plot comparing TRAM from naïve lungs with TRAM from lungs historically treated with influenza A virus (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S27 for a full list of genes.

(C) Schematic for experimental design for panel D. The comparison focuses on TRAM from naïve lungs with those from the lungs historically treated with bleomycin. (D) Volcano plot comparing TRAM from naïve lungs with those from the lungs historically treated with bleomycin (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S27 for a full list of genes.

(E) Schematic for experimental design for panel F. Comparison between TRAM exposed to bleomycin or influenza A as first injury after exposure to bleomycin as a second injury.

(F) Volcano plot comparing TRAM exposed to bleomycin or influenza A as first injury after exposure to bleomycin as a second injury (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S27 for a full list of genes.

Ontogeny Signal Apoe Up in naive: 1045 Up in historic influenza A: 740 Malat1 A B 100 TRAM CD45.1 transduction Itgam Plekha6 CD45.2 MoAM + Inflammatory Folr2 Card11 1 CD45.1 CD45.1+ Stab1 low response Ccl8 Macf1 MoAM Siglec F Siglec Flow

80 Olfml3 2 Regulation of Gas6 Time cell motility Mmp12 Rnf150 Before first Runx3 Plbd1 injury Ccl8 Csf1 60 Adam8 After first C1qc Itgam injury CD45.1+ Before second Gabbr2 Siglec Fhigh Wdfy1 injury Historic Influenza A LogFDRval − 40 After second Historic Bleomycin injury

+ CD45.2 Influenza A Kcnq1ot1 Chil1 Siglec Fhigh 20 Bleomycin Influenza A or Bleomycin Bleomycin 0

−10 −5 0 5 10

T1 T2 T3 T4 logFC

C Ontogeny D Response to Fcrls Lif Lipid metabolic

TRAM CD45.1 100 Up in naive: 842 Up in historic bleomycin:513 CD45.2 external biotic Folr2 Cxcl1 process MoAM + 1 CD45.1 CD45.1+ stimulus Itgam Gdf15 Regulation of cell Siglec Flow low MoAM2 Siglec F Regulation of cell Apoe proliferation

migration Ccl8 80 Ppfia4 Tmem176b Time Before first Response to Runx3 injury stress Il7r Tmem176a

After first 60 injury CD45.1+ Ccl8 Folr2 Before second Siglec Fhigh injury Historic Influenza A LogFDRval − After second 40 Historic Bleomycin injury Wdfy1

+ CD45.2 Influenza A Gdf15 Siglec Fhigh Nfkbia Ctse Bleomycin 20 Influenza A or Bleomycin Bleomycin 0

−10 −5 0 5 10 T1 T2 T3 T4 logFC

Ontogeny E TRAM CD45.1 F CD45.2 Cell cycle Macf1 Up in historic influenza A: 769 Up in historic bleomycin:656 Tnf Inflammatory MoAM + 100 1 CD45.1 CD45.1+ Siglec Flow low process Ubr4 Il6 response MoAM2 Siglec F Chromatin Malat1 Nfkbia Ier2 Regulation of cell Time organization Marco Egr1 death Before first 80 Clec4e Regulation of cell injury Tnf Socs3 migration After first Jun Regulation of cell injury

CD45.1+ 60 Il1b adhesion Before second Egr1 Siglec Fhigh injury Cxcl2 Historic Influenza A Ier2 After second Historic Bleomycin injury LogFDRval Cxcl1 − 40 + CD45.2 Influenza A Siglec Fhigh Bleomycin Kcnq1ot1 Influenza A or Macf1 Bleomycin 20 Bleomycin Ppfia4 0

T1 T2 T3 T4 −10 −5 0 5 10 logFC

Figure S8. Tissue-resident alveolar macrophages demonstrated evidence of immune tolerance irrespective of ontogeny.

(A) Schematic for experimental design for panel B. The comparison focuses on TRAM during the first injury (bleomycin) and MoAM recruited after historic exposure to influenza A virus and re-challenged with bleomycin.

(B) Volcano plot comparing TRAM during the first injury (bleomycin) and MoAM recruited after historic exposure to influenza A virus and re-challenged with bleomycin (FDR <0.05).

Selected differentially expressed genes and GO processes are shown. See Table S28 for a full list of genes. (C) Schematic for experimental design for panel D. The comparison focuses on TRAM during the first injury (bleomycin) and MoAM recruited after historic exposure to bleomycin and re- challenged with bleomycin.

(D) Volcano plot comparing TRAM during the first injury (bleomycin) and MoAM recruited after historic exposure to bleomycin and re-challenged with bleomycin (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S28 for a full list of genes.

(E) Schematic for experimental design for panel F. The comparison focuses on MoAM recruited after the historic exposure to bleomycin or influenza A virus and re-challenged with bleomycin.

(F) Volcano plot comparing MoAM recruited after the historic exposure to bleomycin or influenza A virus and re-challenged with bleomycin (FDR <0.05). Selected differentially expressed genes and GO processes are shown. See Table S28 for a full list of genes.

Discussion

Long-lived macrophage populations in the lung, brain, and liver are derived from myeloid progenitors originating from the fetal yolk sac or fetal liver and maintain their populations via self-renewal for months or years (Gomez Perdiguero et al., 2015). Depletion of tissue-resident macrophages leads to the recruitment of bone marrow-derived monocytes to the tissue where they rapidly differentiate into macrophages. Guided by signals from the local microenvironment these monocyte-derived macrophages reshape their epigenome and obtain gene expression profiles and surface markers resembling tissue-resident macrophages (Guilliams and Scott, 2017;

Watanabe et al., 2019). This raises the possibility that some of the dramatic changes in the immune response to environmental challenges in aged animals are secondary to cell autonomous changes in long-lived tissue resident macrophages or retained epigenetic marks in monocyte- derived compared to tissue-resident macrophages. We used a combination of genetic lineage tracing studies and transcriptomic profiling of flow cytometry sorted alveolar macrophage populations to show that TRAM and MoAM responses to environmental challenges are instead shaped by the local microenvironment of the aged or injured lung.

Pneumonia is the most common cause of death from an infectious disease and pneumonia mortality disproportionately affects the elderly (Jain et al., 2015; Ortiz et al., 2013). Furthermore, in the year after hospital discharge, elderly survivors of pneumonia are at increased risk of developing age-related disorders including lung injury (Mittl et al., 1994), skeletal muscle dysfunction leading to immobility (Herridge et al., 2003), myocardial infarction (Corrales-

Medina Vicente et al., 2012), chronic kidney disease (Murugan et al., 2010), dementia (Tate et al., 2014) and cognitive impairment (Girard et al., 2018). Therefore, interventions targeting immune dysfunction during aging are predicted to impact healthspan. We found that heterochronic adoptive transfer partially or completely reversed age-related transcriptional changes in alveolar macrophages. These findings have implications for therapy–senolytic or other novel therapies might indirectly target the immune dysfunction during aging through effects on the lung parenchyma (Baker et al., 2011; Jeon et al., 2017). Surprisingly, heterochronic parabiosis in which circulating factors and cells were shared between young and old animals, had remarkably little effect on the age-related changes in the transcriptome of either alveolar macrophages or alveolar type II cells, suggesting the alveolar microenvironment is impervious to this intervention during homeostasis.

Previous environmental challenge can prime alveolar macrophages and modulate their response to subsequent challenge, a process referred as “trained immunity” or “innate immune memory”(Netea et al., 2016). Our data suggest that innate immune memory in alveolar macrophages is conferred by the local microenvironment irrespective of ontogeny, as we detected few changes in the transcriptome or DNA methylome between TRAM and MoAM either at baseline or in response to single or repeated environmental challenges. Moreover, we found that MoAM recruited after a second exposure to bleomycin showed higher expression of pro-fibrotic genes when compared with MoAM recruited after the first injury. In sharp contrast, the response to bleomycin (second injury) was similar in MoAM and TRAM in naïve mice and mice historically infected with influenza A virus (first injury). Interestingly, TRAM exhibited immune tolerance irrespective of historic injury and ontogeny. These data are supported by physiologic data showing repeated doses of bleomycin lead to more severe and persistent fibrosis

(Degryse et al., 2010). Collectively, these findings are consistent with a model in which innate immunologic memory in alveolar macrophages is driven by the lung microenvironment via non- cell autonomous mechanisms (Yao et al., 2018).

During injury TRAM and MoAM upregulated inflammatory and fibrotic genes, respectively, in response to influenza A infection or bleomycin, however, the expression of these genes was uniformly higher in MoAM. As monocyte to macrophage differentiation involves the downregulation of inflammatory genes, some of these changes are likely cell autonomous and related to macrophage differentiation (Misharin et al., 2017). Alternatively, these differences may be driven by signals from the injured microenvironment, as areas of injury were disproportionately populated by MoAM. Over time, however, TRAM and MoAM became increasingly similar. Inducible lineage tracing systems will be required to determine whether this loss of transcriptional differences between MoAM and TRAM during the resolution of injury reflects cell autonomous or microenvironment driven differentiation of MoAM (or both), or if distinct waves of monocyte-derived macrophages are recruited over the course of injury and repair (Watanabe et al., 2019).

Alveolar macrophages avidly take up inhaled environmental particles small enough to access the alveolar space, which are then transported to the mouth via mucociliary clearance and excreted in the feces (Semmler et al., 2004). Interestingly, we found that myeloid cells are not recruited in response to short term relatively high level exposure to inhaled urban particulates. These data are consistent with observations in patients after lung transplantation. Even though transplant rejection and enhanced susceptibility to infection in these patients likely creates an environment more inflammatory than normal, alveolar macrophages in the transplanted lung are almost exclusively derived from the donor, even years after transplant (Nayak et al., 2016).

Interestingly, we have shown that both influenza A infection and the intratracheal administration of bleomycin cause a depletion of TRAM (Misharin et al., 2017) . Based on these findings, and the observed replacement of TRAM by MoAM after radiation or the intratracheal administration of liposomal clodronate, it is tempting to speculate that the retention of MoAM in the lung requires depletion of the TRAM pool and opening of the alveolar niche (Guilliams and Scott,

2017). If so, this would explain our observation that adoptive transfer of TRAM was impossible without prior depletion with liposomal clodronate.

Our study has some limitations and important negative findings. First, while the reproducible changes in the alveolar macrophage transcriptome with aging are attributable to the local microenvironment of the alveolus, the mechanisms by which those interactions change with aging are not known, and are likely multiple. Our data and growing single-cell RNA-Seq atlases should allow the identification of putative receptor ligand pairs associated with macrophage differentiation in aging alveolar macrophages or alveolar type II cells that might be targeted to

“rejuvenate” old macrophages. Second, ontogeny may play a role in shaping the proteome independent of the transcriptome and since we measured only a small number of surface proteins via flow cytometry we would be unlikely to detect these changes. In addition, we observed reproducible transcriptomic differences between MoAM and TRAM that did not affect the response to bleomycin or to influenza A infection. We tested whether this regulation involves changes in DNA methylation using reduced representation bisulfate sequencing. These findings were negative as we did not find any association between the transcriptional changes and differences in the DNA methylome, nor could we detect differences in DNA methylation within putative enhancer regions reported in alveolar macrophages. It is therefore likely that the transcriptional differences between TRAM and MoAM are maintained via different epigenetic mechanisms, such as histone modifications, as has been previously described (Gosselin et al.,

2014; Gosselin et al., 2017; Lavin et al., 2014).

In conclusion, our study highlights the overwhelming importance of the lung microenvironment in shaping the transcriptional response of alveolar macrophages during aging and in response to repetitive injury. During acute injury when alveolar macrophage differentiation is incomplete and the microenvironment is abnormal, cellular ontogeny is key to the function of alveolar macrophages, but these differences are transient. Studies with parabiosis suggest the age-related changes in the alveolar microenvironment occur independently of circulating factors or immune cells. These findings dramatically expand the possible mechanisms by which immune function might change during aging and suggest that therapies that directly target the aging microenvironment, for example metformin, senolytics or others, might improve macrophage function via non-cell autonomous mechanisms.

METHODS

CONTACT FOR REAGENT AND RESOURCE SHARING

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact: Scott Budinger ([email protected]).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Mice

All animal experiments and procedures were performed according to protocols approved by the

Institutional Animal Care and Use Committee at Northwestern University. C57BL/6J (Jax

000664) and CD45.1 (Jax 002014) mice were bred in our facility and our colonies are refreshed yearly with mice purchased from the Jackson Laboratory. When indicated, young and aged

C57BL/6 mice from NIA NIH colony were used. Number of animals per group was determined based on our previous publications. Investigators were not blinded to the group allocation. Mice were housed at the Center for Comparative Medicine at Northwestern University, in microisolator cages, with standard 12 hr light/darkness cycle, ambient temperature 23C and were provided standard rodent diet (Envigo/Teklad LM-485) and water ad libitum.

Murine Model of PM Exposure

Inhalational exposure to PM2.5 CAPs was performed as previously described (Chiarella et al.,

2014). Briefly, mice were housed 8 hr per day for 3 consecutive days in a chamber connected to a Versatile Aerosol Concentration and Exposure System (VACES). We exposed control mice to filtered air in an identical chamber connected to the VACES in which a Teflon filter was placed on the inlet valve to remove all particles. We estimated ambient PM2.5 concentrations as the mean of reported values from the 4 EPA monitoring locations closest to our location. The mean concentration in the PM exposure chamber was 118.3 ± 5.21 mg/m3.

Adoptive transfer of alveolar macrophages

Mouse primary alveolar macrophages were isolated by bronchoalveolar lavage performed on euthanized mice with 3 ml of PBS with 1 mM EDTA. Only male mice were used as a source of alveolar macrophages. The lavage was centrifuged at 300 g for 10 min and resuspended in RPMI supplemented with 10% FBS and plated in a density of 100,000 cells/cm2. Alveolar macrophage purity was analyzed by flow cytometry and was confirmed to be >95%. Recipient mice were pretreated with 50 ul of clodronate-loaded liposomes (Liposoma) 72 hours before the adoptive transfer of donor alveolar macrophages to partially deplete tissue-resident alveolar macrophages and make the niche permissive for engraftment. Donor alveolar macrophages (1.5x105 cells in 50

µl of PBS) were transferred via intratracheal instillation into isoflurane anesthetized mice.

CD45.1/.2 mice were used to discriminate between the recipient and donor cells.

Bone marrow chimeras and bone marrow chimeras with thoracic shielding.

Bone marrow chimeras were established by transferring 5 × 106 bone marrow cells isolated from

C57BL/6 mice (this strain expresses CD45.2 alloantigen) into 8-wk-old lethally irradiated (single dose of 1,000 cGy γ-radiation using a Cs-137–based Gammacell-40 irradiator; Nordion) recipient mice (expressing CD45.1 alloantigen). Mice were maintained on autoclaved water supplemented with antibiotics (trimethoprim/sulfamethoxazole; Hi-Tech Pharmacal) for 4 wk after bone marrow transfer and then switched to normal housing regimen. CD45.2 to CD45.1 bone marrow chimeras were used for experiments 8 wk after bone marrow transfer. 8 wk after bone marrow transfer, >95% of all leukocytes and 100% of monocytes and neutrophils in peripheral blood were of donor origin. Bone marrow chimeras with thoracic shielding were used to assess the origin of pulmonary macrophages (TRAMs vs. Mo-AMs) and were generated in a manner similar to that previously described (Janssen et al., 2011; Janssen et al., 2010), with additional modifications (Misharin et al., 2014; Misharin et al., 2017). Briefly, to protect TR-

AMs from radiation, we applied a uniform lead shield that covered the lungs during irradiation.

To eliminate the residual recipient bone marrow in the shielded region mice were treated with myeloablative agent busulfan (30 mg/kg body weight; Sigma-Aldrich)(Chevaleyre et al., 2013)

6 h after the irradiation, followed 12 h later by bone marrow infusion. Chimerism was assessed 2 mo after the procedure via FACS analysis of the peripheral blood collected from facial vein.

Bone marrow chimeras with thoracic shielding were maintained on antibiotics for 4 week as described above and then switched back to the normal housing regimen.

Parabiosis

Parabiosis was performed as described previously (Kamran et al., 2013). Briefly, parabiont mice were co-housed for 2 weeks prior to surgery. After anesthesia and preparation of the surgery site, the skin flap from elbow to knee was created on left and right sides of parabionts, accordingly, ankles and knees of parabionts were joined using non-absorbable suture and skin flaps were joined using continuous uninterrupted suture. Mice were treated with Buprenorphine SR during the first two weeks of recovery and provided with recovery diet gel (ClearH2O). After recovery mice were switched back to the normal housing regimen.

Tissue preparation and flow cytometry

Tissue preparation for flow cytometry analysis and cell sorting was performed as previously described (Misharin et al., 2013; Misharin et al., 2017). Blood was collected into EDTA- containing tubes via facial vein bleed (from live animals) or cardiac puncture (from euthanized animals). Whole blood was stained with fluorochrome-conjugated antibodies, and erythrocytes were then lysed using BD FACS lysing solution. For single-cell suspension obtained from tissues erythrocytes were lysed using BD Pharm Lyse, and cells were counted using Nexcelom K2

Cellometer C automated cell counter (Nexcelom) with AO/PI reagent. Cells were stained with eFluor 506 (eBioscience) viability dyes, incubated with FcBlock (BD), and stained with fluorochrome-conjugated antibodies (see STAR Methods). Data and cell sorting were performed at Northwestern University RLHCCC Flow Cytometry core facility on SORP FACSAria III, BD

LSR II, BD Fortessa and BD Symphony instruments (Becton Dickinson). Sorting was performed using a 100-µm nozzle and 40 psi pressure. Compensation, analysis and visualization of the flow cytometry data were performed using FlowJo software (Tree Star). “Fluorescence minus one” controls were used when necessary to set up gates.

Transcriptome profiling via RNA-Seq

Flow cytometry sorting was used to isolate mouse alveolar macrophages and AT2 cells at indicated time points for each experiment. Cells were sorted with MACS buffer, pelleted and lysed in RLT Plus buffer supplemented with 2-mercaptoethanol (Qiagen). The RNeasy Plus Mini

Kit (Qiagen) was used to isolate RNA and remove genomic DNA. RNA quality was assessed with the 4200 TapeStation System (Agilent). Samples with an RNA integrity number (RIN) lower than 7 were discarded. RNA-seq libraries were prepared from 100ng of total RNA using the NEB Next RNA Ultra Kit (Qiagen) with poly(A) enrichment. Libraries were quantified and assessed using the Qubit Fluorimeter (Invitrogen) and 4200 TapeStation. Libraries were sequenced on NextSeq 500 instrument (Illumina) at 75 bp length, single end reads. The average reading depth across all experiments exceeded 6x106 per sample and over 94% of reads had a Q- score greater than 30. For RNA-seq analysis, reads were demultiplexed using bcl2fastq (version

2.17.1.14). Read quality was assessed with FastQC. Samples that did not pass half of the 12 assessed QC statistics were eliminated. Low quality basecalls were trimmed using trimmomatic

(version 0.33). Reads were then aligned to the Mus musculus reference genome (with mm10 assembly) using the TopHat2 aligner (version 2.1.0). Counts tables were generated using HTSeq

(version 0.6.1). Raw counts were processed in R (version 3.4.4) using edgeR (version 3.20.9) to generate normalized counts. Negative binomial likelihood with default setting followed by generalized linear models fitting was used to estimate differentially expressed genes. FDR q- values were used to correct for multiple comparisons and a value of 0.05 was used as a threshold to indicate statistical significance. K-means clustering was performed using the built-in R stats package (version 3.4.4). Gene Ontology analysis was performed using GOrilla (Eden et al. 2009) on two unranked gene lists. The RNA-seq datasets are available at GEO: GSE134397.

Computationally intensive work was performed on Northwestern University’s Quest High-

Performance Computing Cluster (Northwestern IT and Research computing).

Modified reduced representation bisulfite sequencing.

Measurement and analysis of DNA methylation was performed as previously described

(McGrath-Morrow et al., 2018; Singer, 2019; Walter et al., 2018; Wang et al., 2018; Weinberg et al., 2019). Briefly, genomic DNA was isolated from sorted macrophage populations (Qiagen

AllPrep Micro kit). Endonuclease digestion, size selection, bisulfite conversion, and library preparation were prepared as previously described from approximately 250 ng of input genomic

DNA. Bisulfite conversion efficiency was 99.6% as estimated by the measured percentage of unmethylated CpGs in λ-bacteriophage DNA (New England BioLabs N3013S) spiked in to each sample. Equimolar ratios of four libraries per run were sequenced on NextSeq 500 sequencer

(Illumina), at 75 bp length, single end reads using NextSeq 500/550 V2 High Output reagent kit.

Demultiplexing, trimming, alignment to Mus musculus reference genome (with mm10 assembly), and methylation calling were performed as previously described. Quantification was performed using the DSS R/Bioconductor package (Feng et al., 2014) and the SeqMonk platform

(Andrews, 2018). The RRBS dataset is available at GEO: GSE134238. Computationally intensive work was performed on Northwestern University’s Quest High-Performance

Computing Cluster (Northwestern IT and Research computing).

Statistics

Data were reported as mean±SEM and subjected to 1-way ANOVA. For pairwise significance, we applied two tailed Student’s t-test with Benjamini-Hochberg correction for multiple comparisons (R: stats: version 3.4.4; Prism 7: graphpad). Statistical methods for RNA-seq were described above. Sample power and size were estimated based on our previous work and published papers. The observed data met or exceeded required sample size criteria. The statistical parameters and criteria for significance can be found in figure legends.

Data and software availability

The RNA-seq datasets, containing raw and processed data, are available at GEO: GSE134238

(RRBS dataset), GSE134397 (RNA-seq dataset). R code that was used for the RNA-seq and RRBS analysis is available on GitHub.

ACKNOWLEDGMENTS

A.V.M.: NHLBI HL135124, NIH NIAMS AR061593, ATS/Scleroderma Foundation Research

Grant, DOD grant PR141319, and BD Bioscience Immunology

Research Grant. P.A.R.: HL076139. A.B.: NIH HL125940 and the Thoracic Surgery Foundation,

Society of University Surgeons, John H. Gibbon Jr. Research

Scholarship from American Association of Thoracic Surgery. B.D.S.:

HL128867, Parker B. Francis Research Opportunity Award. J.I.S.: NIH

AG049665, HL048129, HL071643, HL085534. G.M.M.: NIH ES015024 and

ES025644 and ES026718. K.R.: NIH HL079190 and HL124664. G.R.S.B.:

NIH ES013995, HL071643, AG049665, VA BX000201, DOD PR141319. ES015024 H.P.:

NIH AR064546, AG049665 HL134375 and the Mabel Greene Myers Chair.

N.S.C.: NIH grants AG049665, HL071643, CA197532. Core Facilities (Flow Cytometry and

Genomics NCI Cancer Center Support Grant P30 CA060553), the

Feinberg School of Medicine, the Center for Genetic Medicine, and Feinberg’s

Department of Biochemistry and Molecular Genetics, the Office of the Provost, the Office for Research, and Northwestern Information Technology, and maintained and developed by Feinberg IT and Research Computing Group.

S.W.: MSD Life Science Foundation, Public Interest Incorporated Foundation, Japan. David W.

Cugell and Christina Enroth-Cugell Fellowship Program

H.P.: NIH grants AR064546, HL134375, AG049665, and UH2AR067687, the United States-

Israel Binational Science Foundation (2013247), the Rheumatology Research Foundation (Agmt

05/06/14), Mabel Greene Myers Professor of Medicine and generous donations to the

Rheumatology Precision Medicine Fund. C.J.G: NIH grant HL143800. Z.R. Driskill graduate program in life science.

A.B NIH HL125940, HL145478, HL147290, HL147575

K.M.R. NIH HO071643, HL128194,GM096971

W.E.B. NIH AG049665

R.M. NIH AG049665

The authors declare no competing financial interests.

AUTHOR CONTRIBUTIONS

Conceptualization, G.R.S.B., A.V.M.; Methodology, G.R.S.B., A.V.M., Z.R., and A.C.M.P.;

Investigation, A.C.M.P., Z.R., N.J., S.W., M.C., Z.L., L.S., J.M.D, R.P., S.S., C.E.R., K.A.H and

A.Y.M.; Resources, M.A., H.A.V., A.B., S.M.B., M.J., and A.G.; Data Curation, Z.R.,T.S.,

P.A.R, K.R.A, K.N. and B.D.S; Writing – Review & Editing, G.R.S.B., A.V.M., B.D.S, E.R.W,

C.J.G, K.M.R., N.S.C, J.I.S, W.E.B., G.M.M, A.B., H.P., R.I.M.,Z.R.; Visualization,

Z.R.,G.R.S.B., and A.V.M.; Supervision, G.R.S.B., A.V.M.; Project Administration and

Funding, G.R.S.B. and A.V.M..

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