Expression Profiling of Macrophages Reveals Multiple Populations with Distinct Biological Roles in an Immunocompetent Orthotopic Model of Lung Cancer This information is current as of September 28, 2021. Joanna M. Poczobutt, Subhajyoti De, Vinod K. Yadav, Teresa T. Nguyen, Howard Li, Trisha R. Sippel, Mary C. M. Weiser-Evans and Raphael A. Nemenoff J Immunol 2016; 196:2847-2859; Prepublished online 12 February 2016; Downloaded from doi: 10.4049/jimmunol.1502364 http://www.jimmunol.org/content/196/6/2847 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2016/02/11/jimmunol.150236 Material 4.DCSupplemental References This article cites 44 articles, 10 of which you can access for free at: http://www.jimmunol.org/content/196/6/2847.full#ref-list-1

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The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology

Expression Profiling of Macrophages Reveals Multiple Populations with Distinct Biological Roles in an Immunocompetent Orthotopic Model of Lung Cancer

Joanna M. Poczobutt,* Subhajyoti De,* Vinod K. Yadav,* Teresa T. Nguyen,* Howard Li,*,† Trisha R. Sippel,* Mary C. M. Weiser-Evans,*,‡ and Raphael A. Nemenoff*,‡

Macrophages represent an important component of the tumor microenvironment and play a complex role in cancer progression. These cells are characterized by a high degree of plasticity, and they alter their phenotype in response to local environmental cues. Whereas the M1/M2 classification of macrophages has been widely used, the complexity of macrophage phenotypes has not been well studied, par- ticularly in lung cancer. In this study we employed an orthotopic immunocompetent model of lung adenocarcinoma in which murine lung

cancer cells are directly implanted into the left lobe of syngeneic mice. Using multimarker flow cytometry, we defined and recovered several Downloaded from distinct populations of monocytes/macrophages from tumors at different stages of progression. We used RNA-seq transcriptional profiling to define distinct features of each population and determine how they change during tumor progression. We defined an alveolar resident macrophage population that does not change in number and expresses multiple genes related to lipid metabolism and lipid signaling. We also defined a population of tumor-associated macrophages that increase dramatically with tumor and selectively expresses a panel of chemokine genes. A third population, which resembles tumor-associated monocytes, expresses a large number of genes involved in matrix remodeling. By correlating transcriptional profiles with clinically prognostic genes, we show that specific monocyte/macrophage popu- http://www.jimmunol.org/ lations are enriched in genes that predict outcomes in lung adenocarcinoma, implicating these subpopulations as critical determinants of patient survival. Our data underscore the complexity of monocytes/macrophages in the tumor microenvironment, and they suggest that distinct populations play specific roles in tumor progression. The Journal of Immunology, 2016, 196: 2847–2859.

ung cancer remains the leading cause of cancer death in Among the diverse cell types of the TME, macrophages have been men and women, and most patients die of complications of implicated as important participants and are the most abundant non– L metastatic disease (1). Whereas tumor initiation is driven tumor cell type in most cancers (3). Studies in several types of cancer, by somatic mutations in oncogenic drivers and tumor suppressor including lung cancer, have demonstrated that macrophage depletion genes, progression and metastasis involve critical crosstalk be- either through pharmacological or genetic approaches results in by guest on September 28, 2021 tween cancer cells and the tumor microenvironment (TME) (2). slower tumor growth, implicating these cells as mediators of tumor progression (4, 5). In lung cancer, several studies have described *Department of Medicine, University of Colorado Denver, Aurora, CO 80045; correlations in macrophage number or phenotype with clinical out- †Veterans Affairs Medical Center, Denver, CO 80220; and ‡Department of Pharma- comes (6). Many of these studies have employed the model of M1 cology, University of Colorado Denver, Aurora, CO 80045 andM2macrophages.Thismodel,whichisbasedonactivationof ORCIDs: 0000-0002-7211-4176 (T.T.N.); 0000-0002-1230-2941 (H.L.). macrophages in vitro, designates a macrophage phenotype as being Received for publication November 5, 2015. Accepted for publication January 7, either proinflammatory M1, which is proposed to inhibit cancer 2016. progression, or alternatively activated M2, which is proangiogenic, This work was supported by National Institutes of Health/National Cancer Institute Grants R01 CA162226, R01 CA164780, and R01 CA108610 (to R.A.N.); the Colorado immunosuppressive, and promotes cancer progression (7, 8). Al- Lung Specialized Program of Research Excellence (National Cancer Institute Grant P50 though the M1/M2 classification of macrophages serves as a useful CA058187); Ruth L. Kirschstein National Research Service Award T32CA17468 (to T.R.S.); and by Department of Veterans Affairs Grant CDA 1IK2BX001282-01A1 (to H.L.). The starting point, it does not take into account the complexity and Flow Cytometry and the Genomics and Microarray Shared Resources receive support plasticity of these cells. Recent studies have demonstrated that dis- from the National Institutes of Health/National Cancer Institute (University of Colorado tinct populations of macrophages exist in tissues such as the lung, and Cancer Center Support Grant P30 CA046934). S.D. acknowledges support from the Boettcher Foundation and the United Against Lung Cancer Foundation. that these different populations not only express distinct markers but The RNA-seq data presented in this article have been submitted to the National also have different developmental origins (9). For example, the Center for Biotechnology Information Gene Expression Omnibus repository (http:// resident alveolar macrophages in the lung are derived from the www.ncbi.nlm.nih.gov/geo/) under accession number GSE76033. embryonic yolk sac, whereas the recruited monocytes and macro- Address correspondence and reprint requests to Prof. Raphael A. Nemenoff, Univer- sity of Colorado Denver, Department of Medicine, Division of Renal Diseases and phages are derived from the bone marrow. Hypertension C-281, 12700 East 19th Avenue, Aurora, CO 80045. E-mail address: These different populations are likely to play distinct and po- [email protected] tentially opposing roles in cancer progression. In fact, tumors are The online version of this article contains supplemental material. infiltrated by different dynamically changing populations of Abbreviations used in this article: COX, cyclooxygenase; ECM, extracellular matrix; FPKM, monocytes/macrophages (8), and identifying key features of each fragments per kilobase of exon per million fragments mapped; GSEA, gene set enrich- ment analysis; ImmGen, Immunological Genome Project; LLC, Lewis lung carcinoma; population may help to target them therapeutically. LTC4, leukotriene C4; MHC II, MHC class II; -N, from naive mice; PPAR, peroxisome To study the role of the TME in lung cancer, we have devel- proliferator–activated receptor; RNA-seq, RNA sequencing; TME, tumor microenvironment; oped an orthotopic immunocompetent mouse model, in which -2wk, from 2 wk tumor-bearing mice; -3wk, from 3 wk tumor-bearing mice. Lewis lung carcinoma (LLC) cells, which were derived from a Copyright Ó 2016 by The American Association of Immunologists, Inc. 0022-1767/16/$30.00 spontaneous lung adenocarcinoma in C57BL/6 mice, are directly www.jimmunol.org/cgi/doi/10.4049/jimmunol.1502364 2848 EXPRESSION PROFILING OF MACROPHAGES IN LUNG CANCER implanted into the lungs of fully immunocompetent syngeneic RNA extraction and RNA-seq C57BL/6 mice. Within 2–3 wk these cells form a primary tumor Total RNAwas isolated from flow cytometry–sorted cells using QIAshredders that metastasizes to the other lung lobes, mediastinal lymph nodes, and an RNeasy Plus micro kit (Qiagen). RNA quality and quantity were liver, and brain. We have previously shown that tumor progression analyzed using a NanoDrop and Bioanalyzer. RNA-seq library preparation in this model is associated with increases in several monocyte/ and sequencing were conducted at the Genomics and Microarray Core at the macrophage populations (10). However, functional differences University of Colorado Denver–Anschutz Medical Campus. Libraries were constructed using a NuGen Ovation human FFPE RNA-seq multiplex system between these populations have not been identified, and the role of kit customized with mouse specific oligonucleotides for rRNA removal. specific monocyte/macrophage subtypes in tumor progression is Directional mRNA-seq was conducted using the Illumina HiSeq 2000 sys- unknown. In this study, we have used multimarker flow cytometry tem, using the single-read 100 cycles option. to identify and isolate resident populations as well as dynamically Bioinformatic analysis expanding populations of monocytes and macrophages. Using RNA-seq reads were obtained using Illumina HiSeq analysis pipeline. Reads RNA sequencing (RNA-seq) we have been able to profile differ- quality was checked using FastQC (http://www.bioinformatics.bbsrc.ac.uk/ ences in gene expression between these macrophage populations projects/fastqc). The median number of reads per condition was 24 million. and determine how these profiles change during tumor progres- Reads were then processed and aligned to the University of California Santa sion. Furthermore, using transcriptional profiles, we were able to Cruz Mus musculus reference genome (build mm10) using TopHat v2 (11). TopHat incorporates the Bowtie v2 algorithm to perform the alignment (12). correlate specific monocyte/macrophage populations to clinical TopHat initially removes a portion of reads based on quality information outcomes in lung adenocarcinoma. accompanying each read and then maps reads to the reference genome. The prebuilt M. musculus University of California Santa Cruz mm10 index was

downloaded from the TopHat homepage and used as the reference genome. Downloaded from Materials and Methods The default parameters for TopHat were used. The aligned read files were processed by Cufflinks v2.0.2 (13). Reads were assembled into transcripts Cells and their abundance was estimated. Cufflinks uses the normalized RNA-seq LLC cells stably expressing luciferase were obtained from Caliper Life fragment counts to measure the relative abundances of transcripts. The unit of Sciences and maintained in DMEM (Corning CellGro, no. 10-017-CV) measurement is fragments per kilobase of exon per million fragments mapped containing 10% FBS, penicillin/streptomycin, and G418 (500 ng/ml). (FPKM). Confidence intervals for FPKM estimates were calculated using a Bayesian inference method (13). EdgeR is a Bioconductor software package for examining differential expression of replicated count data (14). Briefly, it http://www.jimmunol.org/ Orthotopic mouse model used an overdispersed Poisson model to account for both biological and C57BL/6 mice were obtained from The Jackson Laboratory and bred and technical variability. Additionally, it used empirical Bayes methods to mod- maintained in the Center for Comparative Medicine at the University of erate the degree of overdispersion across transcripts, thereby improving the Colorado Denver. Experiments were performed in 10- to 16-wk-old males. reliability of inference. Default parameters for EdgeR were used. LLC cells stably expressing luciferase (1 3 105 in 25 ml per injection) Hierarchical clustering was performed using Cluster 3.0 (15). Gene ex- were suspended in PBS containing 15% growth factor reduced Matrigel pression levels were centered on the mean and normalized. Clustering was (BD Biosciences, catalog no. 354230) and injected into the parenchyma done using complete linkage and Euclidean distance as the metric. Heat maps of the left lung lobe through the rib cage using a 30-gauge needle, as were generated using Java TreeView (16). Pathway overrepresentation anal- previously described (10). During injection, the lung was directly visu- ysis was conducted using ConsensusPathDB (http://cpdb.molgen.mpg.de/ alized through a 4- to 5-mm incision in the skin. After the procedure, the MCPDB) (17, 18). Searches were conducted in KEGG and Reactome data- by guest on September 28, 2021 incision was closed using veterinary adhesive. All procedures were per- bases with cut-off values of p , 0.01 and a minimum of three overlapping formed under protocols approved by the Institutional Animal Care and genes. The set of initially selected 2458 differentially expressed genes was Use Committee at the University of Colorado Denver. used as background. The analyses were additionally confirmed by DAVID (http://david.abcc.ncifcrf.gov/) (19, 20) and Gene Ontology (21, 22). For comparison with profiles of immune cells published by the Im- Preparation of single-cell suspensions and flow cytometry munological Genome Project (ImmGen), the following populations from + Tumor-bearing mice were sacrificed either 2 or 3 wk after cancer cell naive mice were used: lung alveolar macrophages (MF_Lu), lung CD11b injection, along with uninjected control mice. The lung circulation was macrophages (MF_11b+_Lu), blood monocytes classical (Mo_6C+II+_Bl, perfused with PBS/heparin (20 U/ml, Sigma-Aldrich). Left lung lobes Mo_6C+II-_Bl), and blood monocytes, nonclassical (Mo_6C-II-_Bl, containing tumors were excised and weighed. For control mice, all the Mo_6C-II+_Bl, Mo_6C-IIint_Bl). The top 100 genes differentially ex- lung lobes were harvested for analysis. Tissues from three to five mice pressed in any pairwise comparison between these populations were down- were pooled, mechanically dissociated with scissors, and incubated for loaded from the Web site (http://www.Immgen.org) and combined into a list 30 min at 37˚C with 3.2 mg/ml collagenase type 2 (Worthington Bio- of 645 genes. The expression of these 645 genes among the populations in chemical, 43C14117B), 0.75 mg/ml elastase (Worthington Biochemical, this study was examined and subjected to hierarchical clustering. Clusters of 33S14652), 0.2 mg/ml soybean trypsin inhibitor (Worthington Bio- genes highly expressed selectively in one cell type were identified. Expres- chemical, S9B11099N), and 40 mg/ml DNAse I (Sigma-Aldrich). During sion of genes in these clusters was mapped back to the reference ImmGen incubation, samples were placed in a shaking water bath and dispersed populations using the ImmGen Web site tool My Gene Set. by pipetting every 10 min. Resulting single-cell suspensions were filtered For the analysis of clinically prognostic genes, the list of ∼23,000 genes through 70-mm cell strainers (BD Biosciences) and washed with staining and their associated survival meta-Z scores for lung adenocarcinoma buffer (PBS containing 1% FBS, 2 mM EDTA, 10 mM HEPES). Samples were downloaded from https://precog.stanford.edu/index.php. The survival were subjected to RBC lysis, washed with staining buffer, and filtered scores were used as the metric to rank the genes, and the gene set en- through 40-mm cell strainers (BD Biosciences). Prior to staining, FcgR richment analysis (GSEA; preranked tool) (23, 24) was used to examine was blocked with anti-CD16/CD32 Ab (BD Biosciences) for 10 min. enrichment of good or bad prognostic genes in the gene clusters highly Cells were stained for 30–45 min at 4˚C with the following Abs: CD11b- expressed in MacA or MacB cells as defined in Fig. 3. FITC (clone M1/70, BD Biosciences), Siglec-F–PE or Alexa Fluor 647 The RNA-seq data reported in this study have been deposited in the National (clone E50-2440, BD Biosciences), Ly6G-PE-Cy7 (clone 1A8, BD Biosci- Center for Biotechnology Information Gene Expression Omnibus repository ences), CD64-PE or Alexa Fluor 647 (clone X54-5/7.1, BD Biosciences), (http://www.ncbi.nlm.nih.gov/geo/), with the accession no. GSE76033. CD11c-allophycocyanin-Cy7 (clone HL3, BD Biosciences), and CD11c- Quantitative real-time PCR PerCP-Cy5.5 (clone N418, BioLegend). Flow cytometry analysis and cell sorting were conducted at the University of Colorado Cancer Center Flow Total RNA was isolated from flow cytometry–sorted cells using QIAsh- Cytometry Core Facility using a Gallios flow cytometer (Beckman Coulter) redders and an RNeasy micro kit (Qiagen), and converted into cDNA for analysis and XDP-100 or Astrios (Beckman Coulter) cell sorters for using an iScript cDNA synthesis kit (Bio-Rad). Quantitative real-time sorting. The sorting strategy involved exclusion of debris and cell doublets PCR was conducted as previously described (10). by light scatter and dead cells by DAPI (1 mg/ml). Absolute cell counts Eicosanoid measurements were obtained using Sphero AccuCount ultra rainbow fluorescent particles 3.8 (Spherotech) per the manufacturer’s instructions. Data were analyzed Mice were sacrificed 2.5 wk after cancer cell injection, along with unin- using Kaluza software (Beckman Coulter). jected control mice, and macrophage populations were recovered by flow The Journal of Immunology 2849 cytometry. Immediately after sorting, cells were stimulated with a calcium composed of cells with low expression of Ly6C and intermediate ionophore, and eicosanoids were extracted and measured by liquid expression of MHC II. chromatography/tandem mass spectrometry previously described (10). Results were normalized to total protein. Recovery of cells and transcriptional profiling To gain further insight into the identity and functional properties Results of myeloid cells in the lung TME, we performed transcriptome Identification of myeloid cell subsets in the lung during tumor profiling using RNA-seq. We used flow cytometry–based sorting to development recover cells from tumor-bearing lungs 2 and 3 wk after cancer LLC cells were injected directly into the left lung lobe of fully cell injection, as well as from uninjected control mice. RNA was immunocompetent syngeneic wild-type C57BL/6 mice. We har- extracted from three pools of mice that were independently in- vested the whole tumor-bearing left lung lobes 2 and 3 wk after jected and harvested, with three to five mice contributing to each injection, along with lungs from uninjected control mice. Analysis pool. We obtained sufficient cell numbers and RNA quantity to of weights revealed rapid tumor growth within the lung paren- sequence the following populations isolated from three inde- chyma (Fig. 1A). Tissues were processed into single-cell sus- pendent experiments: from naive mice, MacA cells (MacA-N), pensions and analyzed by flow cytometry using myeloid-specific MacB1 cells (MacB1-N), and MacB2 cells (MacB2-N); from cell-surface markers. 2 wk tumor-bearing mice, MacA cells (MacA-2wk), MacB2 cells In addition to the CD11b and CD11c markers, which are tra- (MacB2-2wk), and MacB3 cells (MacB3-3wk); and from 3 wk ditionally used for identifying macrophages in the lung (25, 26), tumor-bearing mice, MacB2 cells (MacB2-3wk) and MacB3 cells our strategy employed Siglec-F and CD64 markers (9). Siglec-F is (MacB3-3wk). We performed RNA-seq profiling on these samples, Downloaded from a marker specific for murine lung-resident alveolar macrophages compared their transcriptional profile to published databases, and that is not expressed by interstitial or inflammatory macrophages. analyzed differential gene expression between these populations. Although Siglec-F also stains eosinophils, their lack of CD11c expression allows us to exclude eosinophils from the monocyte/ Comparison to ImmGen profiles macrophage pool (9, 27–29). CD64 is a macrophage-specific To verify the identity of MacA and MacB populations, we com-

marker, and in contrast to F4/80, its expression is low or nega- pared their transcriptional profiles to immune cells from naive http://www.jimmunol.org/ tive on monocytes, dendritic cells, and granulocytes (9). mice analyzed by the ImmGen (32). As a reference we focused on Using a modification of our previously published gating strategy nine populations of myeloid cells identified by the ImmGen: (10) shown in Fig. 1B, we identified multiple subsets of myeloid lung macrophages (alveolar macrophages and CD11b+ lung cells. In both naive and tumor-bearing lungs, we identified MacA macrophages), lung dendritic cells (CD11b+ and CD11b2), and cells, which bear markers of alveolar macrophages (Siglec-F+/ blood monocytes (Ly6C+/MHC II2, Ly6C+/MHC II+, Ly6C2/ CD11c+). Furthermore, we excluded eosinophils (Siglec-F+/ MHC II2, Ly6C2/MHC II+, Ly6C2/MHC IIint). We selected 645 CD11c2, Eos gate) and neutrophils (Ly6G+/CD11b+,Neugate). genes that were differentially expressed between these nine ref- The remaining CD11b+ cells (MacB gate) were separated into erence populations and analyzed their expression in MacA and three distinct populations based on CD11c and CD64 expression: MacB cells. Using hierarchical clustering, we delineated five gene by guest on September 28, 2021 MacB1 cells (CD11b+/CD64lo/CD11c+), MacB2 cells (CD11b+/ clusters that were highly expressed in a specific MacA or MacB CD64int/CD11c2), and MacB3 cells (CD11b+/CD64hi/CD11c+). population (Fig. 2). To examine whether these clusters mapped As shown in the flow cytometry plots, the MacB compart- back to specific ImmGen cell types, we plotted their expression in ment in naive lungs was dominated by MacB1 and MacB2 the nine ImmGen populations using the My Gene Set tool (http:// cells, with MacB3 present at very low numbers. Experiments www.ImmGen.org). This analysis indicated that genes specific quantifying absolute cell numbers of these populations in the to MacA-N and MacA-2wk (cluster I) are highly expressed in lung revealed that the total numbers of MacA and MacB1 cells ImmGen alveolar lung macrophages. For MacB1 cells, we defined in the left lobe did not change with tumor growth, whereas the two separate clusters (IV and V). Genes in cluster V map to MacB2 and MacB3 populations rapidly expanded with in- CD11b+ lung dendritic cells, whereas genes in cluster IV map to creasing tumor size, with the most rapid expansion seen in Ly6C2/MHC II2 and Ly6C2/MHC IIint monocytes. This indicates MacB3 cells (Fig. 1C). that MacB1-N is a mixed population composed of lung CD11b+ We further characterized MacA and MacB cells using Ly6C dendritic cells and Ly6C2 monocytes. MacB2 cells (MacB2-N, and MHC class II (MHC II), which are markers frequently used MacB2-2wk, and MacB2-3wk) express genes in cluster III, and to phenotype murine myeloid populations by flow cytometry these genes correlate with Ly6C+/MHC II+ and Ly6C+/MHC II2 (Fig.1D). MacA cells, in the presence or absence of tumor, were blood monocytes. Lastly, MacB3-2wk and MacB3-3wk express negative for Ly6C and stained weakly for MHC II, as expected high levels of genes in cluster II, and this cluster maps to CD11b+ for alveolar macrophages. MacB1 cells expressed low levels of lung macrophages. Thus, the genes that are highly expressed in Ly6C in the presence or absence of tumor. In naive lung, this MacA or MacB populations allowed us to map them to specific gate contained both MHC II2 and MHC II+ populations, indi- immune cells based on published transcriptional profiles in naive cating that the MacB1 gate was composed of at least two distinct mice. Furthermore, this analysis is in concordance with our cell types, with the MHC II+ cells likely representing CD11b+ immunophenotyping and Ly6C and MHC II expression by flow dendritic cells (9, 30). In the presence of tumor, the MHC II cytometry. expression on the cells in the MacB1 gate was heterogeneous, ranging from negative to high. MacB2 cells, both with and Global analysis of differentially expressed genes without tumor, expressed high levels of Ly6C but had little to no The analysis of RNA-seq data allowed us to identify differentially expression of MHC II, which is typical for monocytes (31). expressed genes between the cell populations and between time MacB3 cells, which in naive mice were present in very low points for respective cell populations. In brief, in a pairwise analysis numbers, were negative for Ly6C and expressed high levels of a differentially expressed gene was expected to have expression MHC II, which is consistent with interstitial/infiltrating lung level FPKM of .5 in at least one condition, and have .2.5-fold macrophages (31). In the presence of tumor, the MacB3 gate was change in log2 expression with a false discovery rate–adjusted 2850 EXPRESSION PROFILING OF MACROPHAGES IN LUNG CANCER Downloaded from http://www.jimmunol.org/ by guest on September 28, 2021

FIGURE 1. Identification of myeloid cell populations in lung during tumor growth. LLC cells were injected directly into the lungs of syngeneic C57BL/6 mice, and tumor-bearing left lobes were harvested 2 or 3 wk after injection, along with lungs from uninjected mice. For each time point, the injection and harvest were repeated three times independently, with three to five mice pooled each time. (A) Tumor growth: weights of uninjected and tumor-bearing left lung lobes; uninjected, n = 12 mice (weight measurements not taken for all mice); 2 wk tumor, n =12mice;3wktumor,n =9mice.(B) Gating strategy to identify MacA, MacB1, MacB2, and MacB3 cells. Representative plots are for no tumor (control), 2 wk tumor-bearing, and 3 wk tumor-bearing mice. (C) Total absolute numbers of MacA and MacB populations in tumor-bearing left lung lobes quantified in separate experiments. (D) Characterization of Ly6C and MHC II expression in MacA and MacB populations. p value ,0.05 between the two conditions. In all pairwise compari- using a dendrogram and a heat map (Fig. 3). The top dendrogram sons between cell types and/or time points, we identified 2458 genes indicates that the individual replicates of the cell populations that were differentially expressed (Supplemental Table I). The large cluster together. Interestingly, the replicates of MacB2-N and number of differentially expressed genes suggests that although all the MacB2-2wk were closely clustered. This may reflect the fact that analyzed cell populations belong to the mononuclear phagocyte in 2 wk samples, the harvested tissue was composed of smaller lineage, significant biological differences exist between them. tumors with a larger contribution from the uninvolved lung. To identify relationships between genes and among MacA and Otherwise, genes could be grouped based on their expression into MacB populations, we performed unbiased hierarchical clustering five broad clusters: upregulation (or downregulation) of gene of all 2458 differentially expressed genes and visualized the data expression in MacA-N and MacA-2wk (cluster A); MacB1-N The Journal of Immunology 2851 Downloaded from http://www.jimmunol.org/ by guest on September 28, 2021

FIGURE 2. Comparison of transcriptional profiles of MacA and MacB populations to immune cells from naive mice published by the ImmGen. MacA and MacB populations were recovered from three independently injected and harvested pools of tumor-bearing and control mice by flow cytometry–based sorting. Sufficient cell numbers were obtained for MacA-N, MacB1-N, MacB2-N, MacA-2wk, MacB2-2wk, MacB3-3wk, MacB2-3wk, and MacB3-3wk. RNA was extracted from these populations, and transcriptional profiles were assessed by RNA-seq. We constructed a list of 645 genes that were differ- entially expressed among the following cell types profiled by the ImmGen: lung alveolar macrophages, lung CD11b+ macrophages, blood monocytes/ classical (Ly6C+MHC II+, Ly6C+/MHC II2), blood monocytes/nonclassical (Ly6C2/MHC II+, Ly6C2/MHC II2, Ly6C2/MHC IIint), and lung dendritic cells (CD11b+ and CD11b2). The expression of these 645 genes among the MacA and MacB cells was examined by hierarchical clustering and is shown as a heat map. Clusters of genes highly expressed selectively in one cell type were outlined based on the nodes in the dendrogram (d): (I) MacA, cluster expressed in MacA-N and -2wk; (II) MacB3, cluster expressed in MacB3-2wk and -3wk; (III) MacB2, cluster expressed in MacB2-N, -2wk, and -3wk; (IV) and (V) two separate clusters expressed in MacB1-N. The genes in the clusters were mapped back to the reference ImmGen populations using the ImmGen Web site tool My Gene Set (right panels). The ImmGen populations with highest expression are circled in blue. DC_103-11b+24+_Lu, CD11b+ lung dendritic cell; DC_103+11b-_Lu, CD11b2 lung dendritic cell; MF_Lu, lung alveolar macrophage; MF_103-11b+24-_Lu, lung CD11b+ macrophage; Mo_6C+II-_Bl, blood monocyte/classical Ly6C+/MHC II2; Mo_6C+II+_Bl, blood monocyte/classical Ly6C+/MHC II+; Mo_6C-/II-_Bl, blood monocyte/ nonclassical Ly6C2/MHC II2; Mo_6C-/II+_Bl, blood monocyte/nonclassical Ly6C2/MHC II+; Mo_6C-/IIint_Bl, blood monocyte/nonclassical Ly6C2/MHC IIint. 2852 EXPRESSION PROFILING OF MACROPHAGES IN LUNG CANCER

to the lung and may play a critical role in early stages of lung cancer or in the setting of slowly developing tumors. In cluster A, pathway overrepresentation analysis highly ranked multiple gene sets related to lipid metabolism (Fig. 4A). To follow up on the pathway overrepresentation results, we selected consensus genes from the top-ranking five lipid metabolism pathways, and we examined their expression across the populations in the study. The heat map in Fig. 4B shows 28 lipid metabolism genes that are differentially expressed. Eighteen of these genes formed a cluster that was highly expressed in MacA cells but was expressed at low levels in the other populations. Thus, both the overrepresentation analysis and the analysis of expression of lipid pathway genes across MacA and MacB populations revealed that lipid meta- bolism is a specific gene expression signature of MacA cells. Another pathway that ranked highly in cluster A was peroxi- some proliferator receptor (PPAR) signaling (KEGG). A heat map visualizing all differentially expressed genes belonging to this pathway shows that out of 16 PPAR signaling genes, 12 were

highly expressed in MacA cells (Fig. 4C). Downloaded from We have previously shown that MacA cells in tumor-bearing lungs produce leukotrienes, which are a subfamily of lipid in- flammatory eicosanoids derived from arachidonic acid (10). Thus, in Fig. 4D we examined the expression of known genes in the eicosanoid pathway (KEGG arachidonic acid metabolism) (33,

34), of which 16 were differentially expressed across the pop- http://www.jimmunol.org/ ulations in this study. The key leukotriene metabolism genes 5- lipoxygenase (Alox5) and leukotriene C4 (LTC4) synthase (Ltc4s) were highly expressed in MacA cells from both tumor- and non– tumor-bearing lungs. In contrast, the key enzyme in PG produc- tion pathway, cyclooxygenase (COX)-2 (Ptgs2), was specifically downregulated in MacA cells compared with other populations. COX-1 (Ptgs1) was expressed at low levels in MacA cells from lungs without tumor; however, its expression was upregulated in MacA-2wk. For these key enzymes, we confirmed the mRNA-seq by guest on September 28, 2021 data in a separate group of mice using quantitative RT-PCR (Fig. 4E). To confirm the gene expression data functionally, we recovered MacA and Mac B cells from control and tumor-bearing mice by flow cytometry, stimulated these cells in vitro with a FIGURE 3. Global analysis of genes differentially expressed between calcium ionophore, and measured eicosanoid production by liquid MacA and MacB populations. Differentially expressed genes were iden- chromatography/tandem mass spectrometry (Fig. 4F). The pro- tified in all pairwise comparisons between the recovered populations from duction of LTC4 (a product of 5-lipoxygenase and LTC4 synthase) our RNA-seq analysis (2458 genes). Hierarchical clustering was performed was specific to MacA cells, both in tumor-bearing and non–tumor- on the set of 2458 genes and clusters with high expression in specific populations were identified: cluster A, genes highly and selectively bearing lungs. Furthermore, the production of PGE2 (a product of expressed in MacA-N and MacA-2wk; cluster B2, highly expressed in Ptgs1/COX-1 or Ptgs2/COX-2) was increased in MacA cells from MacB2-N and MacB2-2wk; cluster B1, highly expressed in MacB1-N; tumor-bearing mice versus control, which is in agreement with cluster B2-3wk, highly expressed in MacB2-3wk; cluster B3, highly increased Ptgs1 expression by RNA-seq. Surprisingly, MacB cells expressed in MacB3-2wk and MacB3-3wk. produced low levels of PGE2, but this may be due to the non- physiological stimulation. Among the receptors, MacA expressed (cluster B1); MacB2-N and MacB2-2wk (cluster B2); MacB2-3wk high levels of Ptger2, the receptor for PGE2, and Fpr2, the re- (cluster B2-3wk); and MacB3-2wk and MacB3-3wk (cluster B3). ceptor for lipoxins. In contrast, the receptor for LTB4 (Ltb4r1, We conducted pathway overrepresentation analysis of the BLT1) was specifically downregulated in MacA cells compared gene clusters using Web-based bioinformatic tools at Con- with other populations, as was the receptor for cysteinyl leuko- sensusPathDB (http://consensuspathdb.org/) (17, 18), searching trienes (Cysltr1). in the KEGG and Reactome databases. The analyses were ad- Such a combination of enzymes and receptors suggests that ditionally confirmed by DAVID (http://david.abcc.ncifcrf.gov/) among the studied populations, MacA cells would be the major (19, 20) and Gene Ontology (21, 22) analyses independently. producers of leukotrienes, but would not respond to these products. Below, we discuss characteristic pathway signatures of individual However, they would be responsive to lipoxins, which are the anti- clusters in greater detail. inflammatory products of the lipoxygenase pathway, and to PGE2. The data also suggest that MacA cells in naive lung do not pro- MacA cells duce PGs; however, they gain that capability in the setting of We first focused on genes highly expressed by MacA-N and MacA- tumor through the upregulation of COX-1. These data are con- 2wk (see Supplemental Table I). Although MacA cells do not sistent with increased levels of PGs seen in tumor-bearing mice, increase in number during tumor growth in our model, they rep- which are abrogated when cytosolic phospholipase A2, the rate resent pre-existing resident alveolar macrophages that are unique limiting enzyme in eicosanoid production, is deleted (10, 35). The Journal of Immunology 2853 Downloaded from http://www.jimmunol.org/ by guest on September 28, 2021

FIGURE 4. Analysis of MacA cells. (A) Top 10 KEGG/Rectome database pathways overrepresented in gene cluster A (genes highly expressed in MacA-N and MacA-2wk cells). Blue dots indicate pathways related to lipid metabolism. (B) Levels of differentially expressed genes related to lipid metabolism. Gene list was based on consensus between the top-ranking lipid metabolism pathways. (C) Levels of differentially expressed genes related to PPAR signaling. Gene list was based on the KEGG set PPAR signaling pathway. (D) Levels of differentially expressed genes in the eicosanoid pathway. The key leukotriene pathway enzymes (Alox5, Ltc4s) and PG pathway enzymes (Ptgs1, Ptgs2) are indicated. (E)LevelsofmRNAofAlox5,Ltc4s,Ptgs1,andPtgs2.The cells were recovered form a separate group of mice, in which the MacB subpopulations were pooled, and mRNA was extracted and quantified by quantitative RT-PCR. Levels were normalized to reference genes

(geometric average of b-actin, Gapdh, 18s, and UbqC). (F) Production of LTC4 and PGE2 by MacA and MacB cells recovered by flow cytometry and stimulated in vitro with calcium ionophore. (G) Genes upregulated in MacA-2wk compared with MacA-N. The arrow indicates the two populations being compared. Shown are only genes that met the differential expression criteria in this comparison, and whose average expression in MacA was higher than in the MacB populations. 2854 EXPRESSION PROFILING OF MACROPHAGES IN LUNG CANCER

Comparing MacA-2wk to MacA-N, we identified 89 genes that macrophages in vitro (35). Interestingly, IL-6 appears to be se- were upregulated in tumor-bearing compared with control lungs, lectively expressed by MacA in the setting of tumor. and 1 gene that was downregulated (Supplemental Table II). However, even though the differential expression was statistically MacB3 cells significant, the absolute expression levels of the upregulated genes We next focused on MacB3 cells, which increase rapidly with remained significantly lower in MacA-2wk than in MacB pop- tumor growth and constitute the major component of the lung ulations (Supplemental Fig. 1). It is possible that the increased TME, particularly at the 3 wk time point. As indicated by pathway levels of these genes reflect overall dysregulation of gene ex- overrepresentation analysis, cluster B3 (genes highly expressed pression in MacA cells from tumor-bearing lungs, rather than both in MacB3-2wk and MacB3-3wk) was enriched in pathways biologically significant induction. Out of the 89 upregulated related to chemokine and cytokine signaling (Fig. 5A, Supple- genes, only 5 (Ncam1, Slc40a1, Il6, Tsku, Ptgs1) had above av- mental Table I). To confirm this result independently of KEGG erage absolute expression levels in MacA-2wk cells compared and Reactome databases, we chose a published panel of chemo- with other populations (Fig. 4G). As discussed above, the in- kine genes (37) and examined these in MacA and MacB pop- creased expression of Ptgs1 (COX-1), which was confirmed by ulations. As shown in the heat map in Fig. 5B, of 21 differentially quantitative RT-PCR and by increased PGE2 production in vitro expressed chemokine genes, 16 clustered as highly expressed in (Fig. 4E, 4F), indicates that MacA cells from tumor-bearing lungs MacB3s. Of those 16, 6 were also highly expressed in MacB2- may gain the potential to produce PGs. IL-6 has been implicated 3wk (Fig. 5B). Thus, the analysis of gene cluster B3 suggests that in many types of cancer, and increased levels are associated with MacB3 cells play a critical role in communication between the poorer overall survival in lung cancer (36). We have previously diverse cells of the TME through the secretion of multiple che- Downloaded from demonstrated increased levels of IL-6 in our model and shown mokines. The other highly ranking pathways (Meiotic recombi- that cancer cells can induce expression in bone marrow–derived nation, Systemic lupus erythematosus, Alcoholism, Chromosome http://www.jimmunol.org/ by guest on September 28, 2021

FIGURE 5. Analysis of MacB3 cells. (A) Top 10 KEGG/Reactome pathways overrepresented in gene cluster B3 (genes highly expressed in MacB3-2wk and MacB3-3wk cells). Blue dots indicate pathways related to chemokine signaling. (B) Levels of differentially expressed chemokine genes in MacA and MacB populations. (C) Analysis of genes upregulated in MacB3 cells with tumor progression, comparing MacB3-3wk to MacB3-2wk. Top 10 KEGG/ Reactome pathways overrepresented among the 35 upregulated genes are shown. Blue dots indicate ECM-related pathways. (D) Specific genes that were upregulated in MacB3-3wk compared with MacB3-2wk and that belonged to the ECM-related pathways. The yellow boxes indicate the two populations being compared. The Journal of Immunology 2855 maintenance) all contained 14 histone genes that overlapped with or M2 markers (Supplemental Fig. 2). This analysis demonstrates cluster B3, without any other genes pointing to a specific function. the complexity of macrophage phenotypes in vivo and poor cor- In naive lung, the MacB3 cells are present in very low numbers, relation with the M1/M2 designation, which was based on in vitro and thus we were unable to recover sufficient numbers of cells for activation of bone marrow–derived macrophages (42). RNA-seq analysis. However, these cells increased rapidly with Correlation with genes prognostic in human lung tumor growth, between 2 and 3 wk. We identified 35 genes that adenocarcinoma were upregulated and 4 that were downregulated in MacB3 cells at 3 wk compared with 2 wk (Supplemental Table II). Overrepre- Finally, a recent study has used a bioinformatics approach to es- sentation analysis indicated enrichment of pathways related to tablish a system to examine associations between gene expression extracellular matrix (ECM) (Fig. 5C). Interestingly, whereas up- in human tumors and patient survival across multiple types of regulation of the ECM-related genes in MacB3 at 3 wk was sta- cancer (PRECOG; https://precog.stanford.edu/) (43). We used the tistically significant, these genes had the highest expression in data from the PRECOG database to test whether MacA or MacB MacB2 cells at 3 wk (Fig. 5D). This suggests that ECM produc- populations are enriched in genes correlated with good or bad tion is upregulated in the TME at 3 wk, with MacB2 cells being clinical outcomes in lung adenocarcinoma. We ranked ∼23,000 the major producers, but MacB3 cells also contributing. human genes according to their survival scores, and we used GSEA (preranked tool) (23, 24) to examine whether the clusters MacB2 and MacB1 cells associated with MacA or MacB populations (defined in Fig. 3) are MacB2 cells also increase substantially during tumor growth. In our enriched in genes correlated with good or bad prognosis. The initial clustering of 2458 differentially expressed genes (Fig. 3), GSEA plots and normalized enrichment scores (Fig. 7) indicate Downloaded from genes that were highly expressed in MacB2 at 3 wk formed a cluster that MacB2-3wk and MacB3 (-2wk and -3wk) are enriched in separate from genes highly expressed in MacB2 from uninjected genes correlated with bad clinical outcomes. In contrast, MacB1-N, lung or from 2 wk tumor. In this cluster (B2-3wk), pathways that MacA, and the early stage MacB2 (-N and -2wk) were enriched in were highly ranked by overexpression analysis were related to genes predicting good clinical outcomes, with MacB1-N having the ECM, particularly collagen (Fig. 6A, Supplemental Table I). highest enrichment score. These data suggest that prognostic genes

To follow up on this result, we examined a published collection of identified in the analysis of whole tumors are highly expressed in http://www.jimmunol.org/ ECM proteins (38, 39) of which 61 were differentially expressed in specific macrophage populations, suggesting that enrichment of these this study (Fig. 6B). Interestingly, this analysis revealed two distinct populations in a tumor may predict clinical outcome. gene clusters, one highly expressed in MacB2-3wk (34 genes) and one highly expressed in MacB1-N cells (21 genes). The MacB2- Discussion 3wk cluster contained several genes previously associated with Macrophages have been implicated as critical components of the tumor progression, such as collagens, fibronectin, tenascin C, and TME, controlling tumor initiation, progression, and metastasis. In versican (40, 41). As discussed above, these genes were also up- this study we have taken an unbiased approach to begin to define the regulated in MacB3 cells at 3 wk (Fig. 5D). complexity of macrophage phenotypes in the setting of lung cancer We examined changes in gene expression in MacB2 as a progression. We have employed an immunocompetent orthotopic by guest on September 28, 2021 function of tumor progression. In comparison with MacB2-N, 87 model of cancer progression in which interactions between mac- genes were upregulated and 3 were downregulated in MacB2-2wk rophages, cancer cells, and adaptive immune cells are present. Our cells. However, most of these genes were not selectively expressed study has defined at least four separate monocyte/macrophage in MacB2 cells. Comparison of MacB2-N to MacB2-3wk found populations and used transcriptional profiling to begin to define that 472 genes were upregulated and 97 were downregulated in their function. Our results reveal that each population has a distinct MacB2-3wk cells (Supplemental Table II). Overrepresentation gene expression signature, suggesting that it plays a specific role in analysis indicated that the set of upregulated genes was enriched cancer progression. in pathways related to ECM and pathways related to cell cycle Our data underscore that the TME is shaped both by increases regulation (Fig. 6C, 6D). Because expression of ECM proteins in specific cell populations, as well as by gene expression changes was a distinctive feature of MacB2-3wk, this result is consistent in each of these populations (Fig. 8). The population designated with upregulation of these genes during tumor progression. The MacA represents resident alveolar macrophages. This is confirmed downregulated genes contained genes related to biological oxi- by correlating these cells with the ImmGen signature. This pop- dation, and enriched pathways included Phase I—Functionali- ulation does not change significantly in number during tumor zation of compounds, Drug metabolism—cytochrome P450, and progression. However, there are modest but potentially important Biological oxidations. changes in gene expression associated with the presence of the Analysis of genes highly expressed in MacB1 (cluster B1) or in tumor. The gene expression signature implies that this MacA MacB2 from uninjected lung and from 2 wk tumor (cluster B2) population may be the major mediator of leukotriene production, did not provide robust and repetitive results, such as those observed and the induction of COX-1 suggests that these cells also con- for MacA, MacB2-3wk, and MacB3 cells. tribute to the increase in PG production observed in lung cancer progression (10). Although we confirmed the gene expression data Relationship to M1/M2 macrophages by measuring LTC4 and PGE2 production in MacA cells, one Because studies focusing on clinical outcomes frequently use the limitation of this experiment is that the cells were analyzed out- M1/M2 designation of macrophages, we examined the relation- side of their physiological environment. Another gene selectively ship of our populations with commonly used M1 and M2 markers. expressed in the MacA from tumor-bearing lungs is IL-6, which This analysis has shown that ArgI (M2 marker) was expressed at plays a complex role in cancer progression. highest levels in MacB3 (at both 2 and 3 wk), whereas Nos2 (M1 Cells that we have designated as MacB, especially MacB2 and marker)wasexpressedinbothMacB2(2and3wk)aswellas MacB3, represent populations that increase during tumor pro- MacB3 (2 and 3 wk), with the highest levels in MacB2-3wk. gression in our model. Our analysis suggests that these cells are Analysis of a broader set of M1/M2 markers (42) indicated involved in specific functions regulating tumor progression. In the that none of our populations preferentially expresses either M1 absence of tumor, these cells are present in very low numbers in 2856 EXPRESSION PROFILING OF MACROPHAGES IN LUNG CANCER Downloaded from http://www.jimmunol.org/ by guest on September 28, 2021

FIGURE 6. Analysis of MacB2-3wk. (A) Top 10 KEGG/Reactome pathways overrepresented in gene cluster B2-3wk (genes highly expressed in MacB2- 3wk cells). (B) Levels of differentially expressed ECM genes. (C) Analysis of genes upregulated in MacB2 cells with tumor progression. Comparison of MacB2-3wk to MacB2-N is shown. Top 10 KEGG/Reactome pathways overrepresented among the 472 upregulated genes are shown. Blue dot indicates ECM-related pathways. Green dot indicates pathways related to cell cycle. (D) Specific genes that were upregulated in MacB2-3wk compared with MacB2- N and that belonged to the ECM-related pathways. The arrow indicates the two populations being compared. The Journal of Immunology 2857

FIGURE 8. Overview of the changes in cell number and gene expres-

sion of key monocyte and macrophage populations identified in the lung Downloaded from TME.

the lung, suggesting that they are recruited from the circulation in response to factors produced by cancer cells. MacB3 cells, which

bear markers of infiltrating macrophages, expressed high levels http://www.jimmunol.org/ of multiple cytokines at both time points examined. This would suggest that these cells may orchestrate the recruitment of other stromal cells during tumor progression. This may induce a feed- forward loop, in which early recruitment of MacB3 cells leads to additional waves of recruitment of other stromal cells. Interest- ingly, at the later time point, we observed increases in genes encoding ECM and enzymes that remodel the ECM. Similar ECM- related pathways were also detected in MacB2 cells, which repre- sent tumor-associated monocytes, and which show similar in- by guest on September 28, 2021 creases during tumor progression. Thus, these distinct populations of macrophages can alter the TME both by recruiting addi- tional cells and by remodeling the physical properties of the TME. Correlating our data with analysis of human tumors, it appears that genes associated with better clinical outcomes are expressed at earlier time points, whereas genes associated with poor outcomes are expressed at later time points. Although studies have suggested that these changes are a result of a phenotypic switch in macro- phage populations interacting with cancer cells, our data indicate a more complex situation. Distinct populations of macrophages expand during cancer progression. Thus, a change in a specific marker will reflect alterations in existing populations due to phenotypic modulation, as well as increases in populations that express these markers constitutively. For example, studies have demonstrated increased production of TGF-b1 by macrophages during cancer progression. From our analysis, the level of TGF-b1 production is not altered in any of our populations (not shown), but marked increases in the numbers of MacB2 and MacB3 cells during progression will result in increased numbers of TGF-b1– producing cells. However, increases in several ECM proteins will be a result of increased numbers of ECM-producing macrophages, FIGURE 7. Correlation of MacA and MacB transcriptional profiles as well as upregulation of production in these cells during tumor to prognostic genes in human lung adenocarcinoma. (A) GSEA plots: progression. ∼23,000 human genes were ranked according to their survival scores in The populations we have defined do not easily conform to the lung adenocarcinoma as listed in the PRECOG database, and the GSEA M1/M2 paradigm. Although some M1/M2 markers are enriched preranked analysis tool was used to examine enrichment in good or bad in certain populations, using a more extensive list of markers prognosis genes in the gene clusters highly expressed in MacA or MacB shows that these genes are expressed across multiple populations. cells as defined in Fig. 3. (B) GSEA enrichment scores: FDR q-val, false discovery rate; NOM p-val, nominal p value; NES, normalized enrichment Additionally, a model in which macrophages recruited to the score. site of the tumor as M1/antitumorigenic macrophages undergo phenotypic changes to an M2/protumorigenic phenotype is not 2858 EXPRESSION PROFILING OF MACROPHAGES IN LUNG CANCER supported by our data. Based on the PRECOG analysis, most of References the populations we have defined retain an expression signature 1. Jemal, A., R. Siegel, E. Ward, Y. Hao, J. Xu, T. Murray, and M. J. Thun. 2008. associated with either good or bad clinical outcomes throughout Cancer statistics, 2008. CA Cancer J. Clin. 58: 71–96. 2. Kenny, P. A., G. Y. Lee, and M. J. Bissell. 2007. Targeting the tumor micro- tumor progression. An exception is the MacB2 population, which environment. Front. Biosci. 12: 3468–3474. appears to be associated with good outcomes at early time points, 3. Noy, R., and J. W. Pollard. 2014. Tumor-associated macrophages: from mech- but undergoes changes in gene expression leading to an associ- anisms to therapy. Immunity 41: 49–61. 4. Zaynagetdinov, R., T. P. Sherrill, V. V. Polosukhin, W. Han, J. A. Ausborn, ation with a bad outcome at later time points. A. G. McLoed, F. B. McMahon, L. A. Gleaves, A. L. Degryse, To our knowledge, this is one of the first studies to molecularly G. T. Stathopoulos, et al. 2011. A critical role for macrophages in promotion of define the diversity of macrophages in lung cancer in an immu- urethane-induced lung carcinogenesis. J. Immunol. 187: 5703–5711. 5. Sharma, S. K., N. K. Chintala, S. K. Vadrevu, J. Patel, M. Karbowniczek, and nocompetent setting. The major conclusion of our analysis is that M. M. Markiewski. 2015. Pulmonary alveolar macrophages contribute to the distinct macrophage populations express specific gene signatures, premetastatic niche by suppressing antitumor T cell responses in the lungs. J. Immunol. 194: 5529–5538. which predicts that they play specific and distinct roles in cancer 6. Remark, R., C. Becker, J. E. Gomez, D. Damotte, M. C. Dieu-Nosjean, progression. However, the actual function of these cells will need C. Saute`s-Fridman, W. H. Fridman, C. A. Powell, N. K. Altorki, M. Merad, and to be validated in experiments that selectively deplete a specific S. Gnjatic. 2015. The non-small cell lung cancer immune contexture. A major determinant of tumor characteristics and patient outcome. Am. J. Respir. Crit. macrophage subpopulation in the TME. Our studies indicate that Care Med. 191: 377–390. recruited populations of macrophages generally aid tumor pro- 7. Allavena, P., A. Sica, G. Solinas, C. Porta, and A. Mantovani. 2008. The in- gression, suggesting that selectively blocking their recruitment flammatory micro-environment in tumor progression: the role of tumor- associated macrophages. Crit. Rev. Oncol. Hematol. 66: 1–9. may represent a therapeutic strategy to inhibit lung cancer pro- 8. Mantovani, A., S. Sozzani, M. Locati, P. Allavena, and A. Sica. 2002. Macro- gression. A limitation of this study is the use of only one lung phage polarization: tumor-associated macrophages as a paradigm for polarized Downloaded from cancer model. However, we would argue that our orthotopic in- M2 mononuclear phagocytes. Trends Immunol. 23: 549–555. 9. Immunological Genome Consortium. 2012. Gene-expression profiles and tran- jection model is an appropriate model to answer questions con- scriptional regulatory pathways that underlie the identity and diversity of mouse cerning progression of advanced lung cancer and the role of tissue macrophages. Nat. Immunol. 13: 1118–1128. specific cells in the lung TME. Other models would less accu- 10. Poczobutt, J. M., M. Gijon, J. Amin, D. Hanson, H. Li, D. Walker, M. Weiser- Evans, X. Lu, R. C. Murphy, and R. A. Nemenoff. 2013. Eicosanoid profiling in rately reflect the TME of the lung (e.g., s.c. flank injections) or an orthotopic model of lung cancer progression by mass spectrometry demon- would not result in a well-defined primary tumor (e.g., tail vein strates selective production of leukotrienes by inflammatory cells of the mi- http://www.jimmunol.org/ injections). Another limitation of these studies is the use of a croenvironment. PLoS One 8: e79633. 11. Trapnell, C., L. Pachter, and S. L. Salzberg. 2009. TopHat: discovering splice single lung cancer cell line. Targeted therapies in lung adenocar- junctions with RNA-Seq. Bioinformatics 25: 1105–1111. cinoma are based on identification of specific oncogenic drivers 12. Langmead, B., C. Trapnell, M. Pop, and S. L. Salzberg. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. that drive proliferation of cancer cells in a cell-autonomous Genome Biol. 10: R25. fashion. However, there is little known as to how specific driv- 13. Trapnell, C., D. G. Hendrickson, M. Sauvageau, L. Goff, J. L. Rinn, and ers affect the TME. It will be of interest to determine whether the L. Pachter. 2013. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31: 46–53. changes in number and gene expression profiles of the macro- 14. Robinson, M. D., D. J. McCarthy, and G. K. Smyth. 2010. edgeR: a Bio- phage populations we have defined are dependent on the onco- conductor package for differential expression analysis of digital gene expression genic drivers of the cancer cells. For our orthotopic model, this data. Bioinformatics 26: 139–140. by guest on September 28, 2021 15. de Hoon, M. J., S. Imoto, J. Nolan, and S. Miyano. 2004. Open source clustering will require a panel of murine lung cancer cells that express the software. Bioinformatics 20: 1453–1454. drivers that have been identified in human lung cancer. Thus, our 16. Saldanha, A. J. 2004. Java Treeview—extensible visualization of microarray report provides a framework for studies of macrophage pop- data. Bioinformatics 20: 3246–3248. 17. Kamburov, A., K. Pentchev, H. Galicka, C. Wierling, H. Lehrach, and R. Herwig. ulations in other models of lung cancer, including spontaneous 2011. ConsensusPathDB: toward a more complete picture of cell biology. models in which specific oncogenic drivers are activated. Nucleic Acids Res. 39: D712–D717. Finally, it is important to extend these studies to human lung 18. Kamburov, A., C. Wierling, H. Lehrach, and R. Herwig. 2009. ConsensusPathDB— a database for integrating human functional interaction networks. Nucleic Acids Res. tumors. In this regard, it is encouraging that we were able to 37: D623–D628. correlate transcriptional profiles of specific macrophage pop- 19. Huang, W., B. T. Sherman, and R. A. Lempicki. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. ulations with either bad or good prognostic genes derived from 4: 44–57. bioinformatic studies in human lung adenocarcinoma. Thus, our 20. Huang, W., B. T. Sherman, and R. A. Lempicki. 2009. Bioinformatics enrich- studies form the basis to develop a panel of markers to charac- ment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37: 1–13. terize macrophage populations in human tumors. Earlier studies 21. The Gene Ontology Consortium. 2000. Gene Ontology: tool for the unification have shown that the location of macrophages in lung tumors may of biology. Nat. Genet. 25: 25–29. determine their role in cancer progression. Increased numbers of 22. Boyle, E. I., S. Weng, J. Gollub, H. Jin, D. Botstein, J. M. Cherry, and G. Sherlock. 2004. GO:TermFinder—open source software for accessing Gene macrophages within the tumor itself was associated with a fa- Ontology information and finding significantly enriched Gene Ontology terms vorable prognosis, whereas increased numbers of macrophages in associated with a list of genes. Bioinformatics 20: 3710–3715. the stroma surrounding the tumor was associated with an unfa- 23. Mootha, V. K., C. M. Lindgren, K. F. Eriksson, A. Subramanian, S. Sihag, J. Lehar, P. Puigserver, E. Carlsson, M. Ridderstra˚le, E. Laurila, et al. 2003. vorable prognosis (44). The development of a panel of markers PGC-1a-responsive genes involved in oxidative phosphorylation are coordi- specific for MacB3 and MacB2 cells will be required to define nately downregulated in human diabetes. Nat. Genet. 34: 267–273. their localization, and may be of value in predicting clinical 24. Subramanian, A., P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, and outcomes in human disease. J. P. Mesirov. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102: 15545–15550. Acknowledgments 25. Duan, M., W. C. Li, R. Vlahos, M. J. Maxwell, G. P. Anderson, and M. L. Hibbs. 2012. Distinct macrophage subpopulations characterize acute infection and We thank the members of the University of Colorado Cancer Center Flow chronic inflammatory lung disease. J. Immunol. 189: 946–955. Cytometry Core for assistance with flow cytometry and the Genomics and 26. Janssen, W. J., L. Barthel, A. Muldrow, R. E. Oberley-Deegan, M. T. Kearns, Microarray Core for assistance with RNA-seq. C. Jakubzick, and P. M. Henson. 2011. Fas determines differential fates of resident and recruited macrophages during resolution of acute lung injury. Am. J. Respir. Crit. Care Med. 184: 547–560. 27. Stevens, W. W., T. S. Kim, L. M. Pujanauski, X. Hao, and T. J. Braciale. 2007. Disclosures Detection and quantitation of eosinophils in the murine respiratory tract by flow The authors have no financial conflicts of interest. cytometry. J. Immunol. Methods 327: 63–74. The Journal of Immunology 2859

28. Sung,S.S.,S.M.Fu,C.E.Rose,Jr.,F.Gaskin,S.T.Ju,andS.R.Beaty.2006. 36. Liao, C., Z. Yu, W. Guo, Q. Liu, Y. Wu, Y. Li, and L. Bai. 2014. Prognostic value A major lung CD103 (aE)-b7 integrin-positive epithelial dendritic cell pop- of circulating inflammatory factors in non-small cell lung cancer: a systematic ulation expressing Langerin and tight junction proteins. J. Immunol. 176: review and meta-analysis. Cancer Biomark. 14: 469–481. 2161–2172. 37. Balkwill, F. R. 2012. The chemokine system and cancer. J. Pathol. 226: 148–157. 29.Zhang,J.Q.,B.Biedermann,L.Nitschke,andP.R.Crocker.2004.The 38. Hynes, N. E., and H. A. Lane. 2005. ERBB receptors and cancer: the complexity murine inhibitory receptor mSiglec-E is expressed broadly on cells of the of targeted inhibitors. Nat. Rev. Cancer 5: 341–354. innate immune system whereas mSiglec-F is restricted to eosinophils. Eur. J. 39. Ricard-Blum, S. 2011. The collagen family. Cold Spring Harb. Perspect. Biol. 3: Immunol. 34: 1175–1184. a004978. 30. Desch, A. N., G. J. Randolph, K. Murphy, E. L. Gautier, R. M. Kedl, 40. Theocharis, S., J. Klijanienko, C. Giaginis, J. Rodriguez, T. Jouffroy, A. Girod, M. H. Lahoud, I. Caminschi, K. Shortman, P. M. Henson, and C. V. Jakubzick. + D. Point, G. Tsourouflis, and X. Satre-Garau. 2011. Peroxisome proliferator- 2011. CD103 pulmonary dendritic cells preferentially acquire and present ap- activated receptor-g in mobile tongue squamous cell carcinoma: associations optotic cell-associated antigen. J. Exp. Med. 208: 1789–1797. with clinicopathological parameters and patients survival. J. Cancer Res. Clin. 31.Becher,B.,A.Schlitzer,J.Chen,F.Mair,H.R.Sumatoh,K.W.Teng, Oncol. 137: 251–259. D. Low, C. Ruedl, P. Riccardi-Castagnoli, M. Poidinger, et al. 2014. High- 41. Lu, P., V. M. Weaver, and Z. Werb. 2012. The extracellular matrix: a dynamic dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15: niche in cancer progression. J. Cell Biol. 196: 395–406. 1181–1189. 42. Murray, P. J., J. E. Allen, S. K. Biswas, E. A. Fisher, D. W. Gilroy, S. Goerdt, 32. Immunological Genome Project Consortium. 2008. The Immunological Genome S. Gordon, J. A. Hamilton, L. B. Ivashkiv, T. Lawrence, et al. 2014. Macrophage Project: networks of gene expression in immune cells. Nat. Immunol. 9: 1091– 1094. activation and polarization: nomenclature and experimental guidelines. Immunity 33. Wang, D., and R. N. Dubois. 2010. Eicosanoids and cancer. Nat. Rev. Cancer 10: 41: 14–20. 181–193. 43. Gentles, A. J., A. M. Newman, C. L. Liu, S. V. Bratman, W. Feng, D. Kim, 34. Dennis, E. A., and P. C. Norris. 2015. Eicosanoid storm in infection and in- V. S. Nair, Y. Xu, A. Khuong, C. D. Hoang, et al. 2015. The prognostic landscape flammation. Nat. Rev. Immunol. 15: 511–523. of genes and infiltrating immune cells across human cancers. Nat. Med. 21: 938– 35. Weiser-Evans, M. C., X. Q. Wang, J. Amin, V. Van Putten, R. Choudhary, 945. R. A. Winn, R. Scheinman, P. Simpson, M. W. Geraci, and R. A. Nemenoff. 44. Welsh, T. J., R. H. Green, D. Richardson, D. A. Waller, K. J. O’Byrne, and Downloaded from 2009. Depletion of cytosolic phospholipase A2 in bone marrow-derived mac- P. Bradding. 2005. Macrophage and mast-cell invasion of tumor cell islets rophages protects against lung cancer progression and metastasis. Cancer Res. confers a marked survival advantage in non-small-cell lung cancer. J. Clin. 69: 1733–1738. Oncol. 23: 8959–8967. http://www.jimmunol.org/ by guest on September 28, 2021 Supplemental Figure 1

MacA MacB1 MacB2 MacB3 N2wkN N2wk 3wk 2wk 3wk

Supplemental Figure 1. The 89 genes meeting the statistical criteria for upregulation MacA-2wk compared to MacA-N. For the majority of these genes, the absolute expression levels remained significantly lower in MacA-2wk than in MacB populations. Supplemental Figure 2

M1 markers

MacA MacB1 MacB2 MacB3 N2wkN N2wk 3wk 2wk 3wk

M2 markers

MacA MacB1 MacB2 MacB3 N2wkN N2wk 3wk 2wk 3wk

Supplemental Figure 2. Expression of consensus M1/M2 markers in MacA and MacB cells. Top panel – M1 markers; Bottom panel – M2 markers. Genes in gray were not expressed above the threshold (5 FPKM) in any of the populations. Genes differentially expressed in any pairwise comparison Cluster A Cluster B1 Cluster B2 Cluster B2-3wk Cluster B3 0610040J01Rik 1190005I06Rik 1700023F06Rik 5031414D18Rik 1810011O10Rik 1700017B05Rik 1110007C09Rik 2610034B18Rik 9430020K01Rik 8430408G22Rik 2700081O15Rik 2510009E07Rik 1110051M20Rik 4930506M07Rik A530099J19Rik A430078G23Rik A930004D18Rik 5430435G22Rik 1110058L19Rik 4931406C07Rik Abca8a Abca9 Acsbg1 AA467197 1190005I06Rik A230050P20Rik Ablim1 Abi3 Adamts14 Abcc3 1300017J02Rik Abcc5 Ablim3 Acss2 Adamtsl3 Adap2 1500012F01Rik Abcd2 Ace Aox3 Adm Aif1 1600014C10Rik Abcg1 Acer2 Ap1s3 Aebp1 Ang 1700006E09Rik Abhd12 Ackr2 Arhgap15 Ak1 Anpep 1700007K13Rik Abhd17c Adam23 Arhgef10 Amotl1 Aoah 1700017B05Rik Abhd3 Adamts1 Arhgef18 Angptl2 Arg1 1700023F06Rik Abhd5 Adcy4 Arrb1 Ank Arhgap10 1700025G04Rik Acaa1b Adh1 Atp2a3 Ank3 Arhgap19 1700026L06Rik Acot1 Adrbk2 Azin2 Ano1 Asb2 1700029I15Rik Acot2 Afap1l1 BC021614 Apc2 B430306N03Rik 1700056E22Rik Acox1 Aff3 BC035044 Apcdd1 B4galt6 1810011H11Rik Adam22 Ager Bcl11a Arhgap22 Baiap2 1810011O10Rik Adam33 Akap12 Bcl6 Arhgef25 Basp1 2200002D01Rik Adamts2 Aldh1a1 Birc3 Arhgef40 Batf2 2310009B15Rik Adarb1 Aldh1a2 Bmyc Asns Bcl2l1 2510009E07Rik Adcy3 Aplnr Bpifa1 Aspn Blnk 2610034B18Rik Adipor2 Apol7c Bpifb1 B3gnt9 Bnip3 2610524H06Rik Afap1 Aqp1 Btla B9d1 Bst2 2700081O15Rik Agmo Aqp5 Cbr2 Bace1 C1qa 2810417H13Rik Agpat9 Arhgap29 Ccdc88c Bag2 C1qb 2900026A02Rik Aifm2 Arhgef15 Cd300lb Bcar1 C1qc 4930481A15Rik Ak8 Arhgef26 Cd3d Bcat1 C3ar1 4930506M07Rik Alas1 Arl4d Cd55 Bmp1 Ccl12 4931406C07Rik Aldoc Art3 Cdc42ep2 Bnc1 Ccl2 5031414D18Rik Alox5 Astl Cdh23 Btbd11 Ccl24 5330417C22Rik Aloxe3 Axin2 Ceacam1 C1qtnf6 Ccl7 5430435G22Rik Angel1 B4galt4 Chad Cald1 Ccl8 8430408G22Rik Angptl3 Bach2 Cldn3 Cbx6 Ccr1 8430419L09Rik Angptl4 BC049352 Clec2g Ccbe1 Ccr5 9430020K01Rik Anxa4 Bcam Clec2i Ccdc136 Cd38 A230050P20Rik Arl11 Bcl2 Clec3b Ccl11 Cd72 A430078G23Rik Atp10a Bcl6b Csgalnact2 Cd248 Cd81 A530032D15Rik Atp6v0d2 Bgn Csrp2 Cda Cd93 A530064D06Rik Atxn1 Bmp4 Cst3 Cdc42ep5 Cebpzos A530099J19Rik Axl Bmp6 Cyp2ab1 Cdr2l Clec4d A730061H03Rik B3gnt7 C130074G19Rik Cyp2f2 Cep170b Cmklr1 A930004D18Rik BC026585 C1qtnf2 Cytip Chpf Col14a1 AA467197 Bcar3 C7 Ddit4 Clip3 Cpne2 Aatk Bckdhb Cadm3 Dennd3 Clstn3 Cst7 AB124611 Bend6 Calcrl Dusp10 Clu Cstb Abca1 Best1 Camk2n1 Dusp2 Cnn3 Ctla2b Abca6 Blvra Car14 E130208F15Rik Col18a1 Ctsc Abca8a Bmpr1a Casc4 Ebi3 Col1a1 Ctsl Abca9 Bmx Casz1 Fam49a Col3a1 Cxcl14 Abcb1b Bzw2 Cbfa2t3 Fam71a Col5a1 Cxcl16 Abcb6 C2cd2l Cblb Fcho1 Col6a1 Cxcl2 Abcc3 C530008M17Rik Ccdc170 Flt3l Col6a2 Cxcl9 Abcc5 Car4 Ccdc92 Foxp1 Col6a3 Dnase1l3 Abcd2 Car5b Ccl17 Frat1 Col8a1 Dok2 Abcg1 Card11 Ccl22 Frat2 Cpe Dppa3 Abcg3 Ccdc80 Ccm2l Fyb Crtap Ecm1 Abhd1 Ccl6 Ccr7 G0s2 Csgalnact1 Emp1 Abhd12 Ccnd2 Cd163 Gbp9 Ctxn1 Etv5 Abhd14a Ccpg1 Cd209a Gm1966 Cul7 Fabp3 Abhd17c Cd101 Cd209c Gpr18 Cxxc5 Fads1 Abhd3 Cd164 Cd209d Haao Cyr61 Fam20c Abhd5 Cd1d1 Cd226 Hes1 Dbn1 Fam213b Abi3 Cd2 Cd300e Hpgd Dcbld2 Fam46c Ablim1 Cd200r1 Cd300ld Hsd11b1 Dcn Fcgr1 Ablim3 Cd200r4 Cd34 Il1b Ddr2 Fcna Abtb2 Cd22 Cd3e Il27 Dkk2 Fcrlb Acaa1b Cd274 Cd7 Ip6k3 Dlg5 Fcrls Ace Cd300lf Cd79a Itga4 Dlx1 Fgd6 Acer2 Cd302 Cd83 Itgal Dlx2 Fgf13 Acer3 Cd9 Cdc14a Itgb3 Dmwd Fmnl2 Ackr2 Cdc25b Cdh5 Itpr1 Dnm1 Fnip2 Ackr3 Cdc42ep3 Cdkn1c Kctd1 Dpt Folr2 Acot1 Cdca7l Cdo1 Klf2 Dpy19l3 Gabbr2 Acot2 Cdh1 Cecr6 Klf9 Dpysl3 Galnt2 Acox1 Celf4 Cep112 Krt80 Dusp18 Gas6 Acp5 Ces2c Cep85l Krtcap3 Dusp9 Gatm Acrbp Chd5 Ces1d Ldlrad3 Egfr Gdf3 Acsbg1 Chil3 Chil1 Lifr Enah Glrx Acss1 Chp1 Chn2 Limd2 Etv1 Gpr34 Acss2 Cib2 Cldn18 Lipe Etv4 Gpr84 Actn1 Cideb Cldn5 Lmo1 Eya1 Grhpr Acyp1 Cidec Clec10a Lpcat4 F3 Grn Ada Cldn1 Clec14a Lrmp Fam149a Hexb Adam19 Clec1b Clec1a Lrrk2 Fam92a Hist1h1a Adam22 Clec7a Clic3 Lst1 Farp1 Hist1h2ab Adam23 Clmn Clic5 Ly6i Fbn1 Hist1h2ae Adam33 Cmbl Col27a1 Lyz1 Fgfr1 Hist1h2ag Adam8 Colec12 Cox4i2 Map3k12 Fhl2 Hist1h2ah Adamts1 Comt Cpm Map3k6 Figf Hist1h2ak Adamts14 Coro2b Crip2 Map3k8 Fkbp10 Hist1h2bh Adamts2 Coro6 Crispld2 Mbp Fkbp11 Hist1h2bk Adamtsl3 Cox6b2 Cryab Mef2c Fkbp9 Hist1h2bm Adap2 Cped1 Ctgf Megf9 Flnb Hist1h3b Adarb1 Cpne5 Ctla2a Mettl7a1 Flnc Hist1h3c Adck3 Cpne8 Ctnnd2 Mtus1 Flrt2 Hist1h4f Adcy3 Cracr2b Cxcl12 Muc1 Fosl1 Hist2h3b Adcy4 Ctsf Cxcl15 Myo18a Foxg1 Hist3h2ba Add3 Cx3cl1 Cxx1c Nav1 Fscn1 Hmox1 Adh1 Cxcr1 Cyfip2 Nbeal2 Fstl1 Hpgds Adipor2 Cxcr2 Cyp2b10 Nedd4l Fstl3 Htr2b Adm Cyb561 Cyp2s1 Nfkbie Gas1 Htra3 Adora2a Cyb561a3 Cyp4b1 Nhsl2 Gas2 Iigp1 Adora2b D330045A20Rik Cys1 Nr4a1 Gja1 Il21r Adora3 D630039A03Rik Cytl1 Nrarp Gli3 Il7r Adrb1 Dapk1 Cyyr1 Nsmaf Gm773 Insl6 Adrb2 Dgat2 Dapl1 Nsun4 Gnb4 Irg1 Adrbk2 Dhrs3 Dcstamp Ntng2 Gpc1 Itga9 Adssl1 Dhrs7b Dennd4a Pdcd4 Gpr124 Kcnj10 Aebp1 Dip2c Dgkh Pik3ip1 Gpsm2 Kcnk13 Afap1 Dmd Dlc1 Pilrb1 Gpx8 Kcnn4 Afap1l1 Dmxl2 Dll4 Plagl1 Grem1 Kctd7 Aff3 Dnajb13 Dnah12 Plbd1 Gxylt2 Lat2 Afmid Dusp13 Dnaja4 Pmaip1 Hic1 Lgals3bp Ager Dysf Dpep1 Pou2f2 Hmga1 Lgmn Agmo Ear1 Dpp4 Ppp1r15a Hmga2 Lhfpl2 Agpat9 Ear2 Dusp16 Psd Hmgn3 Maf Ahnak2 Ear6 Edn1 Ptprj Hoxa7 Mif Ahr Ech1 Efemp1 Rap1gap2 Hoxd8 Mmp12 Aif1 Echdc1 Efnb1 Rasgrf2 Hoxd9 Mmp13 Aif1l Ehhadh Efnb2 Rasgrp4 Hspb8 Mmp14 Aifm2 Enpp1 Egfl7 Rcsd1 Hspg2 Ms4a14 Ak1 Epcam Egflam Rere Htra1 Ms4a6d Ak4 F7 Egr3 Rgs18 Igf2bp2 Ms4a7 Ak8 Fabp1 Ehf Ripk2 Igf2r Msr1 Akap12 Fabp4 Eltd1 Rnase6 Igfbp4 Ndufaf6 Akap2 Fam115a Emcn S1pr4 Igfbp6 Nme4 Akap5 Fam118b Emp2 Samsn1 Il18rap Npy Akr1b8 Fam122b Emr4 Sap25 Il1r1 Nt5dc2 Akr1e1 Fam189a2 Eno3 Satb1 Il1rl1 Nup43 Akt3 Fam213a Enpep Scgb1a1 Il33 Nxpe5 Alas1 Fam69a Epas1 Scgb3a1 Islr Oit3 Aldh1a1 Fam78b Epha4 Scgb3a2 Itpr3 Olfml3 Aldh1a2 Fam89a Ephb4 Sept6 Kcnq5 Ophn1 Aldh1b1 Fcgrt Ephx1 Siglecg Kdelr3 Orc1 Aldoc Fcor Erg Sik1 Kera Paox Alox15 Ffar2 Esam Skint3 Kirrel Pdgfa Alox5 Ffar4 Esrp2 Slc2a6 Klf5 Pdlim4 Aloxe3 Flt1 Etl4 Slc44a2 Krt18 Pdpn Amica1 Fmn1 Ets1 Slc46a3 Krt8 Pf4 Amot Fpr1 Etv3 Slc6a12 Lama5 Pgam1 Amotl1 Fpr2 F2rl2 Sntb1 Lamb1 Plau Ang Fut7 Faim3 Spic Lamc1 Pmepa1 Angel1 Fzd8 Fam174b Sstr4 Leprel2 Ppbp Angptl2 Gal Fas St8sia4 Lgi2 Prkar1b Angptl3 Galm Fcrl1 Stk10 Lpar1 Procr Angptl4 Galnt3 Fcrla Susd3 Lrig1 Rab11fip5 Ank Gca Fermt2 Tbc1d8 Lrrc15 Rab3il1 Ank3 Gdf15 Fgd5 Tex22 Lrrc17 Rai14 Ankrd13b Gm4980 Fhl1 Tmc8 Ltbp1 Rapsn Ankrd33b Gmds Flt3 Tmem88 Ltbp3 Rbpj Ankrd37 Gmpr Fmo1 Traf3ip3 Lum Rgs1 Anln Gna15 Fmo2 Trem3 Maged1 Rgs10 Ano1 Golm1 Fndc1 Upb1 Magi1 Sash1 Anpep Gpd1 Foxf1 Utrn Map1b Scamp5 Anxa4 Gpr137b Fxyd1 Vav3 Mapk8ip1 Sdc4 Aoah Gpr155 Fyn Vps37b Mast4 Serf1 Aox3 Gpr160 Galnt18 Wfdc2 Medag Slc13a3 Ap1s3 Gpr55 Gata2 Wnt11 Mfsd2a Slc29a3 Ap5s1 Grap2 Gata3 Xdh Micall2 Slc2a1 Apbb2 Grb7 Gem Zcchc11 Mmp3 Slc37a2 Apc2 Gstt3 Gfra2 Zfp296 Mpzl1 Slc46a1 Apcdd1 Gyg Gimap1 Zfyve9 Mxra8 Slc6a8 Apitd1 Hadhb Gimap4 Myc Slc7a8 Apln Hcar2 Gimap6 Nav3 Spdl1 Aplnr Hebp1 Gja4 Nckap1 Src Apobec1 Hook1 Gja5 Nedd4 Stab1 Apoc1 Hr Gjb2 Ngf Steap3 Apoc2 Hsd17b7 Glp1r Nhsl1 Syngr1 Apoe Hsd3b7 Gm10277 Nid1 Syt11 Apol7c Hspa12a Gpc3 Nkain1 Tgfbi Aprt Htr2c Gpihbp1 Nkx2-2 Tlr1 Aqp1 Hvcn1 Gpr116 Nphp1 Tlr12 Aqp5 Ifitm10 Gpr182 Nptx1 Tm4sf19 Arap1 Il12rb2 Gprc5a Nptxr Tmem119 Arap2 Il18 Gpx3 Nr2f1 Tmem171 Areg Il1rl2 Grap Nrn1 Tmem37 Arg1 Inadl Grasp Ntn1 Tnfrsf14 Arg2 Iqsec1 Grrp1 Nxn Tnfrsf9 Arhgap10 Itgax Gsta3 Oaf Tpi1 Arhgap15 Kazald1 Gucy1a3 Obsl1 Trem2 Arhgap19 Kazn Gzma Osr1 Treml2 Arhgap22 Kcne3 H2-DMb2 Osr2 Tsen15 Arhgap24 Kcnh4 H2-Oa Pard3 Tubb2a Arhgap26 Kcnip4 H2-Ob Parva Xylt2 Arhgap29 Kcnk5 H2-Q5 Pcbp4 Zdhhc14 Arhgef10 Kcnn3 H2-Q6 Pcdh7 Zmynd15 Arhgef10l Kctd12b Hc Pcgf2 Arhgef15 Klhdc4 Hecw2 Pcolce Arhgef18 Krt19 Heg1 Pear1 Arhgef25 Krt79 Hepacam2 Peg10 Arhgef26 Kynu Hey1 Plac1 Arhgef3 Laptm4b Hfe Plod2 Arhgef37 Large Hmcn1 Ppic Arhgef40 Lepr Hs3st1 Prkcdbp Arid5a Lilra5 Icam2 Prkg2 Arl11 Lima1 Igfbp2 Prl2c2 Arl4d Lipa Igfbp5 Prl2c3 Arl5c Lipf Igfbp7 Prrg4 Arntl2 Lmo4 Ikzf3 Prrx1 Arrb1 Lphn2 Il4i1 Ptges Arsg Lphn3 Ildr1 Pthlh Art3 Lpin1 Inmt Ptn Asap2 Lpl Irf4 Ptpla Asb2 Lrp4 Itga8 Ptpn14 Asf1b Lsr Jam2 Ptprn Asns Ltc4s Kdr Ptprs Aspn Ly75 Kif21b Pxdn Ass1 Lyplal1 Kit Rab4a Astl Lyrm4 Klrd1 Rbfox2 Atg9b Magee1 Klri1 Rbp4 Atp10a Mak Lamp3 Rbpms2 Atp1a3 Mamdc2 Ldb2 Rcn1 Atp2a3 Mamld1 Limch1 Rcor2 Atp2b4 Mapre3 Lims2 Rdh10 Atp6v0d2 Marco Loxl1 Rgma Atp8b2 Marveld2 Lphn1 Rgs16 Atxn1 Matn2 Lrrc32 Rgs17 Auts2 Mavs Lsmem1 Rhbdf1 AW112010 Mcam Ltb Rhox2d Axin2 Mccc2 Ltbp4 Rhox2g Axl Mcemp1 Ly6c1 Rhox2h Azin2 Mcoln3 Lysmd2 Rpl13 B3gnt3 Mertk Lyve1 Rtkn B3gnt5 Mfap3l Mal2 Rundc3a B3gnt7 Mfsd7c Map3k14 S100a8 B3gnt8 Mgat4b Mapt S100a9 B3gnt9 Mgll Mcc Samd4 B430306N03Rik Mgst2 Mdm1 Satb2 B4galt2 Mgst3 Meis2 Scara3 B4galt4 Mical3 Mfap4 Sccpdh B4galt6 Mkx Mgl2 Sdc2 B9d1 Mr1 Mgp Sema3a Bace1 Mrc1 Mid2 Sema4c Bach2 Mreg Mmp15 Sema5a Bag2 Mrps33 Mmp23 Sept5 Bahcc1 Ms4a8a Mmp25 Serpinf1 Baiap2 Mtap7d3 Mmrn2 Serpinh1 Bambi Mtfp1 Msrb3 Sertad4 Basp1 Mtmr7 Myct1 Sh3d19 Batf2 Mum1 Myh14 Shisa4 BC021614 Myo6 Mylk Six4 BC026585 Myo7a Myo1b Slc14a1 BC028528 Nabp1 Myo1d Slc16a1 BC035044 Napepld Myo1g Slc20a2 BC049352 Nceh1 Myzap Slc30a4 BC055324 Ncoa4 Nbl1 Slc38a4 Bcam Nedd9 Ncald Slc9b1 Bcar1 Net1 Nccrp1 Smo Bcar3 Neurl1b Nckap5 Smtn Bcas1 Nol4l Ndnf Smyd2 Bcat1 Nr3c2 Ndufa4l2 Snai1 Bckdhb Nrg4 Nebl Snai2 Bcl11a Nucb2 Negr1 Sox4 Bcl2 Nudt12 Nfib Sox9 Bcl2a1a Nxt2 Nfkbiz Spred3 Bcl2a1b Ocln Ngfr Spry2 Bcl2a1d Olr1 Nkd1 Spry4 Bcl2l1 Ovol2 Nkg7 Ssc5d Bcl3 P2ry13 Notch3 Stc1 Bcl6 P2ry14 Npnt Steap2 Bcl6b P2ry2 Npr1 Synpo Bcr Pald1 Nrgn Tcf7l1 Bdh1 Parp16 Ntn4 Tead1 Bend6 Pbx1 Ntrk2 Tead2 Best1 Pced1b Ogn Tenm4 Bgn Pcyox1 Osgin2 Tfap2a Bhlhe41 Pdk4 P2ry10 Thbs2 Bin1 Pecr Palmd Tnc Birc3 Penk Pcdh1 Tnfrsf10b Birc5 Per2 Pcolce2 Tnk2 Blnk Perp Pcp4l1 Tpm1 Blvra Pex11a Pde3a Tpm2 Bmp1 Pgap1 Pde5a Traf4 Bmp4 Pgpep1 Pdgfrb Trip6 Bmp5 Phgdh Pdzd2 Tubb3 Bmp6 Phospho1 Pecam1 Twist1 Bmpr1a Pik3ap1 Pglyrp1 Twist2 Bmx Pla2g15 Phlpp1 Uaca Bmyc Plcb2 Pitpnm2 Unc5b Bnc1 Pld3 Pkp3 Usp43 Bnc2 Plekhg1 Plcb4 Vasn Bnip3 Plet1 Pllp Wisp1 Bpifa1 Plscr1 Plvap Wnt7b Bpifb1 Plscr4 Podxl Zfp462 Bst1 Pnpla7 Podxl2 Zfp580 Bst2 Pnpla8 Postn Zfp827 Btbd11 Poli Pot1b Zic2 Btla Pon3 Ppap2a Bub1 Pparg Ppp1r14a Bub1b Ppm1j Ppp1r16b Bzrap1 Ppp1r12b Prelp Bzw2 Ppp1r9a Prex2 C130074G19Rik Prkar2b Prickle1 C1qa Pros1 Prrg1 C1qb Prrt1 Prx C1qc Ptger2 Ptp4a3 C1qtnf1 Ptgfrn Ptprb C1qtnf2 Ptpn3 Ptprcap C1qtnf6 Pxmp4 Ptprg C1rl Qdpr Ptprm C1s1 Qprt Pvrl1 C2cd2l Rab19 Ramp2 C330027C09Rik Rab44 Rapgef3 C3ar1 Ralgds Rasgrp3 C4b Rem1 Rasip1 C530008M17Rik Renbp Rbms3 C5ar1 Reps2 Rbpms C5ar2 Rhof Rel C7 Rims3 Retnla C77080 Rnase2b Rgag4 Cacna1d Rnf144b Rhoj Cacnb3 Rnf180 Ripply3 Cad Rora Rnf144a Cadm1 Rps4l Robo2 Cadm3 Rpusd3 Robo4 Calcrl Rras2 Rtn1 Cald1 Rufy4 Rtp3 Calhm2 Rxra S100a16 Camk1 S100a1 S1pr1 Camk2n1 Sardh S1pr5 Camk2n2 Sec14l2 Scn3b Capg Sema3e Scn7a Car13 Sema6d Scnn1a Car14 Serpinb1a Scube2 Car4 Serpinb6a Sdpr Car5b Serpine1 Sec14l3 Car6 Sgk1 Sema3c Card11 Sh2d1b1 Sema3f Card6 Sh2d4b Sema3g Casc4 Sh3bgrl2 Sema6a Casc5 Siglecf Sema7a Cass4 Slc17a9 Sept1 Casz1 Slc18a1 Sept4 Cbfa2t3 Slc1a3 Serping1 Cblb Slc22a17 Sesn3 Cbr2 Slc22a18 Setbp1 Cbx5 Slc29a1 Sftpa1 Cbx6 Slc39a12 Sftpb Ccbe1 Slc39a2 Sftpc Ccdc102a Slc52a3 Sftpd Ccdc136 Slc5a6 Sgip1 Ccdc170 Slc6a4 Sh2d3c Ccdc80 Slc7a2 Shroom3 Ccdc88c Slc9a2 Slc12a2 Ccdc92 Slc9a4 Slc15a2 Ccl11 Slc9a7 Slc16a9 Ccl12 Slco2b1 Slc24a1 Ccl17 Slpi Slc25a23 Ccl2 Snn Slc27a3 Ccl22 Snrpg Slc2a3 Ccl24 Snx7 Slc34a2 Ccl27a Socs2 Slc43a3 Ccl4 Sort1 Slc9a3r2 Ccl5 Spag11b Slco2a1 Ccl6 Spire1 Smad6 Ccl7 Spns1 Smagp Ccl8 St7 Sod3 Ccl9 Sulf2 Sox13 Ccm2l Sult2b1 Sox17 Ccna2 Syn1 Sox18 Ccnb2 Syne2 Sox7 Ccnd1 Synj2 Spata13 Ccnd2 Syp Spn Ccne1 Tbc1d2 Spns2 Ccne2 Tc2n Spock2 Ccnf Tcf7l2 St3gal5 Ccpg1 Tfec St5 Ccr1 Tle6 St6galnac2 Ccr2 Tlr5 St8sia6 Ccr5 Tma7 Stap1 Ccr7 Tmem138 Stmn2 Cd101 Tmem150a Strip2 Cd109 Tmem151a Sult1a1 Cd163 Tmem154 Tagln Cd164 Tmem216 Tbx2 Cd177 Tmem238 Tbx3 Cd1d1 Tmem41a Tcea3 Cd2 Tmem64 Tcf21 Cd200 Tnfaip2 Tcf7 Cd200r1 Tnfrsf26 Tek Cd200r4 Tnfsf13 Tgfb1i1 Cd209a Tob1 Tgm3 Cd209c Trf Thbd Cd209d Trim13 Thsd1 Cd22 Trim29 Tiam2 Cd226 Trim46 Tie1 Cd244 Tspan32 Timd4 Cd248 Ttyh2 Timp3 Cd27 Tuba1b Tinagl1 Cd274 Ucp3 Tk1 Cd276 Unc119 Tmem100 Cd300a Ung Tmem204 Cd300e Usp49 Tmem26 Cd300lb Vav2 Tmem47 Cd300ld Vkorc1l1 Tmem98 Cd300lf Vstm2a Tmtc2 Cd300lg Wfdc10 Tnfsf9 Cd300lh Wfdc21 Tnip3 Cd302 Wwtr1 Tox2 Cd320 Xlr Traf1 Cd34 Xrcc5 Treml4 Cd36 Xylb Trib2 Cd38 Zfand2a Tspan12 Cd3d Zfp125 Tspan18 Cd3e Zfp770 Tspan2 Cd3g Zfp935 Tspan33 Cd4 Zfp948 Tspan7 Cd40 Vipr2 Cd52 Vldlr Cd55 Vtn Cd63 Zbtb16 Cd7 Zbtb46 Cd72 Zc3h12c Cd74 Zeb1 Cd79a Zfp366 Cd79b Cd81 Cd82 Cd83 Cd86 Cd9 Cd93 Cd97 Cda Cdc14a Cdc20 Cdc25b Cdc42ep2 Cdc42ep3 Cdc42ep5 Cdc7 Cdca3 Cdca7 Cdca7l Cdca8 Cdh1 Cdh23 Cdh5 Cdhr4 Cdk1 Cdk18 Cdkn1c Cdkn2b Cdo1 Cdr2l Cds1 Ceacam1 Cebpzos Cecr6 Celf4 Cenpf Cenpv Cep112 Cep170b Cep55 Cep85l Cers4 Ces1d Ces2c Cfap61 Cfb Cfh Ch25h Chad Chaf1a Chd5 Chek1 Chil1 Chil3 Chn2 Chp1 Chpf Chrne Chst11 Chst15 Ciart Cib2 Cideb Cidec Ciita Cit Cited4 Ckap2 Ckap4 Ckb Cks1b Cldn1 Cldn18 Cldn3 Cldn5 Clec10a Clec12a Clec14a Clec1a Clec1b Clec2g Clec2i Clec3b Clec4a1 Clec4b1 Clec4d Clec4n Clec5a Clec7a Clec9a Clic3 Clic5 Clip3 Clmn Clmp Clspn Clstn3 Clu Cmbl Cmc1 Cmc2 Cmklr1 Cmpk2 Cmtm7 Cndp2 Cnn2 Cnn3 Cobll1 Col14a1 Col15a1 Col16a1 Col18a1 Col1a1 Col27a1 Col3a1 Col5a1 Col6a1 Col6a2 Col6a3 Col8a1 Colec12 Comt Coq7 Coro2b Coro6 Cox4i2 Cox6b2 Cpe Cped1 Cpm Cpne2 Cpne5 Cpne8 Cpq Cracr2b Creb5 Crip1 Crip2 Crispld2 Crtap Cryab Csf1 Csf1r Csf3r Csgalnact1 Csgalnact2 Csrp2 Cst3 Cst7 Cstb Ctgf Ctla2a Ctla2b Ctnnd2 Ctsc Ctsd Ctse Ctsf Ctsk Ctsl Cttnbp2nl Ctxn1 Cul7 Cx3cl1 Cx3cr1 Cxcl1 Cxcl10 Cxcl12 Cxcl13 Cxcl14 Cxcl15 Cxcl16 Cxcl17 Cxcl2 Cxcl3 Cxcl9 Cxcr1 Cxcr2 Cxx1a Cxx1c Cxxc5 Cyb561 Cyb561a3 Cyfip2 Cyp27a1 Cyp2a5 Cyp2ab1 Cyp2b10 Cyp2f2 Cyp2s1 Cyp39a1 Cyp4b1 Cyr61 Cys1 Cysltr1 Cytip Cytl1 Cyyr1 D330045A20Rik D3Ertd751e D630039A03Rik D830025C05Rik D8Ertd82e Dab2 Dagla Dapk1 Dapl1 Dbn1 Dbp Dcbld2 Dcn Dcstamp Dctd Ddit4 Ddr1 Ddr2 Dennd2a Dennd3 Dennd4a Dgat2 Dgcr6 Dgkg Dgkh Dhdh Dhrs3 Dhrs7b Diap3 Dip2c Dkk2 Dlc1 Dlg4 Dlg5 Dlgap5 Dll4 Dlx1 Dlx2 Dmd Dmkn Dmpk Dmwd Dmxl2 Dnah12 Dnah2 Dnaja4 Dnajb13 Dnase1l3 Dnm1 Dnmt3l Dnph1 Dok2 Dpep1 Dpp4 Dpp7 Dppa3 Dpt Dpy19l3 Dpysl3 Dst Dtx4 Dusp10 Dusp13 Dusp14 Dusp16 Dusp18 Dusp2 Dusp4 Dusp8 Dusp9 Dut Dyrk3 Dysf E030030I06Rik E130208F15Rik E2f7 Ear1 Ear2 Ear6 Ebi3 Ece1 Ech1 Echdc1 Ecm1 Ect2 Edn1 Efemp1 Efemp2 Efnb1 Efnb2 Egfl7 Egflam Egfr Egln3 Egr2 Egr3 Ehbp1 Ehf Ehhadh Eif4e3 Eltd1 Emb Emcn Emilin2 Eml6 Emp1 Emp2 Emr4 Enah Eno3 Enpep Enpp1 Epas1 Epb4.1l1 Epb4.1l3 Epcam Epha2 Epha4 Ephb4 Ephx1 Epm2a Epn2 Epsti1 Ereg Erg Ero1l Errfi1 Esam Esr1 Esrp2 Ethe1 Etl4 Ets1 Etv1 Etv3 Etv4 Etv5 Eya1 F11r F13a1 F2r F2rl2 F3 F5 F7 Fabp1 Fabp3 Fabp4 Fabp5 Fads1 Fah Faim3 Fam109b Fam115a Fam117a Fam118b Fam122b Fam124a Fam149a Fam174b Fam189a2 Fam189b Fam20a Fam20c Fam213a Fam213b Fam26f Fam43a Fam46c Fam49a Fam64a Fam65b Fam69a Fam71a Fam78b Fam83a Fam89a Fam92a Farp1 Fas Fastkd2 Fat1 Fau Fblim1 Fbln1 Fbn1 Fbxo5 Fcgr1 Fcgr4 Fcgrt Fcho1 Fcna Fcnb Fcor Fcrl1 Fcrla Fcrlb Fcrls Fermt2 Ffar2 Ffar4 Fgd2 Fgd3 Fgd5 Fgd6 Fgf1 Fgf13 Fgf7 Fgfr1 Fgfr4 Fgl2 Fhad1 Fhdc1 Fhl1 Fhl2 Fhl3 Fibin Figf Fignl1 Fkbp10 Fkbp11 Fkbp9 Flnb Flnc Flrt2 Flt1 Flt3 Flt3l Fmn1 Fmnl2 Fmo1 Fmo2 Fn1 Fnbp1l Fndc1 Fnip2 Folr2 Fosl1 Foxf1 Foxf2 Foxg1 Foxm1 Foxp1 Fpr1 Fpr2 Frat1 Frat2 Fscn1 Fstl1 Fstl3 Fth1 Fut7 Fxyd1 Fxyd2 Fyb Fyn Fzd4 Fzd8 G0s2 Gabbr1 Gabbr2 Gadd45a Gal Galk1 Galm Galnt18 Galnt2 Galnt3 Galnt6 Galnt9 Gapt Gas1 Gas2 Gas6 Gas7 Gata2 Gata3 Gatm Gatsl2 Gbgt1 Gbp2 Gbp3 Gbp5 Gbp8 Gbp9 Gca Gcdh Gchfr Gclm Gdf15 Gdf3 Gem Gemin4 Gfi1b Gfra2 Ggn Ghr Gimap1 Gimap4 Gimap6 Gins2 Gja1 Gja4 Gja5 Gjb2 Gjc1 Gli2 Gli3 Glis2 Glis3 Glmn Glp1r Glrp1 Glrx Gm10134 Gm10244 Gm10277 Gm10300 Gm10517 Gm10621 Gm10655 Gm10801 Gm12185 Gm14221 Gm14446 Gm1673 Gm1966 Gm20498 Gm4955 Gm4980 Gm5431 Gm7008 Gm7694 Gm773 Gm9733 Gmds Gmfg Gmpr Gna15 Gnao1 Gnb4 Gng10 Golm1 Got1 Gpc1 Gpc3 Gpcpd1 Gpd1 Gpihbp1 Gpnmb Gpr116 Gpr124 Gpr132 Gpr137b Gpr141 Gpr153 Gpr155 Gpr157 Gpr160 Gpr171 Gpr18 Gpr182 Gpr183 Gpr34 Gpr35 Gpr55 Gpr56 Gpr65 Gpr68 Gpr84 Gprc5a Gprc5c Gpsm2 Gpt2 Gpx3 Gpx8 Grap Grap2 Grasp Grb7 Grem1 Grhpr Grn Grrp1 Grwd1 Gsg1 Gsr Gsta3 Gstk1 Gstm5 Gstt3 Gucy1a3 Gxylt2 Gyg Gypc Gys1 Gzma H1fx H2-Aa H2-Ab1 H2-DMb2 H2-Eb1 H2-Ke6 H2-M2 H2-Oa H2-Ob H2-Q5 H2-Q6 H2-Q7 H2-T24 H60b Haao Hadhb Hba-a1 Hba-a2 Hbb-b1 Hbb-b2 Hbegf Hc Hcar2 Hdac7 Hdc Hebp1 Hecw2 Heg1 Hells Helz2 Hepacam2 Hes1 Hexb Hey1 Hfe Hic1 Hip1r Hist1h1a Hist1h2ab Hist1h2ae Hist1h2af Hist1h2ag Hist1h2ah Hist1h2ak Hist1h2bh Hist1h2bk Hist1h2bl Hist1h2bm Hist1h2bn Hist1h3b Hist1h3c Hist1h3g Hist1h4f Hist1h4n Hist2h2bb Hist2h3b Hist3h2ba Hivep2 Hivep3 Hk1 Hk3 Hlf Hmcn1 Hmga1 Hmga1-rs1 Hmga2 Hmgn1 Hmgn3 Hmox1 Hn1l Hook1 Hopx Hoxa3 Hoxa7 Hoxb4 Hoxd8 Hoxd9 Hp Hpgd Hpgds Hpse Hr Hs3st1 Hsd11b1 Hsd17b7 Hsd3b7 Hspa12a Hspa1a Hspa1b Hspb11 Hspb8 Hspg2 Htr2b Htr2c Htr7 Htra1 Htra3 Hvcn1 Hyal1 Ica1 Icam2 Ier3 Ier5l Ifi203 Ifi204 Ifi205 Ifi27l2a Ifi47 Ifit1 Ifit2 Ifit3 Ifitm1 Ifitm10 Ifitm2 Ifitm3 Ifitm5 Ifitm6 Ift122 Ift81 Igf1 Igf1r Igf2bp2 Igf2r Igfbp2 Igfbp3 Igfbp4 Igfbp5 Igfbp6 Igfbp7 Igsf9 Igtp Iigp1 Ikbke Ikzf3 Il10 Il10ra Il11ra1 Il12rb2 Il15 Il18 Il18bp Il18rap Il1a Il1b Il1r1 Il1r2 Il1rl1 Il1rl2 Il1rn Il21r Il27 Il33 Il4i1 Il6 Il7r Ildr1 Inadl Inca1 Inhba Inmt Inpp4b Insl6 Ip6k3 Ipo4 Iqgap3 Iqsec1 Irf1 Irf4 Irf5 Irf7 Irg1 Irgm1 Isg15 Isg20 Islr Itga3 Itga4 Itga6 Itga8 Itga9 Itgal Itgam Itgax Itgb3 Itgb6 Itgb7 Itpr1 Itpr3 Jade2 Jam2 Jarid2 Jup Kank2 Kazald1 Kazn Kbtbd11 Kcne3 Kcnh4 Kcnip4 Kcnj10 Kcnk13 Kcnk5 Kcnn3 Kcnn4 Kcnq5 Kctd1 Kctd12b Kctd14 Kctd7 Kdelr3 Kdm6b Kdr Kera Kif18b Kif20a Kif21b Kif2c Kifc1 Kirrel Kit Klf2 Klf5 Klf9 Klhdc4 Klra17 Klrb1b Klrb1f Klrd1 Klri1 Klrk1 Kmo Knstrn Krt18 Krt19 Krt79 Krt8 Krt80 Krtcap3 Kynu L1cam Lacc1 Lama5 Lamb1 Lamc1 Lamp3 Laptm4b Large Lat2 Layn Lbh Lbp Lbr Lcn2 Ldb2 Ldhb Ldlrad3 Lepr Leprel2 Lgals1 Lgals3bp Lgi2 Lgmn Lhfp Lhfpl2 Lifr Lig1 Lilra5 Lilra6 Lima1 Limch1 Limd2 Lims2 Lipa Lipe Lipf Lmna Lmnb2 Lmo1 Lmo4 Lonrf3 Loxl1 Loxl3 Lpar1 Lpar6 Lpcat1 Lpcat2 Lpcat4 Lphn1 Lphn2 Lphn3 Lpin1 Lpl Lppr3 Lrg1 Lrig1 Lrmp Lrp12 Lrp4 Lrp8 Lrr1 Lrrc15 Lrrc16a Lrrc17 Lrrc25 Lrrc32 Lrrk2 Lsmem1 Lsr Lst1 Ltb Ltb4r1 Ltbp1 Ltbp3 Ltbp4 Ltc4s Lum Ly6a Ly6c1 Ly6c2 Ly6i Ly75 Ly86 Lyl1 Lyplal1 Lyrm4 Lysmd2 Lyve1 Lyz1 Mad2l1 Maf Mafb Mag Maged1 Magee1 Magi1 Mak Mal2 Mamdc2 Mamld1 Man1a Manea Map1b Map2k6 Map3k12 Map3k14 Map3k6 Map3k8 Mapk8ip1 Mapre3 Mapt Marcks Marcksl1 Marco Marveld2 Mast4 Matk Matn2 Mavs Mboat1 Mbp Mcam Mcc Mccc2 Mcemp1 Mcm10 Mcm2 Mcm3 Mcm5 Mcm6 Mcm7 Mcoln2 Mcoln3 Mcts2 Mcu Mdm1 Medag Mef2c Mefv Megf9 Meig1 Meis2 Meis3 Mertk Met Mettl10 Mettl7a1 Mfap3l Mfap4 Mfsd12 Mfsd2a Mfsd6 Mfsd7c Mgat4a Mgat4b Mgl2 Mgll Mgmt Mgp Mgst1 Mgst2 Mgst3 Mical3 Micall2 Mid2 Mif Mis18bp1 Mitf Mki67 Mkx Mlkl Mmp11 Mmp12 Mmp13 Mmp14 Mmp15 Mmp19 Mmp23 Mmp25 Mmp3 Mmp8 Mmp9 Mmrn2 Mnda Mndal Mob3b Mov10 Mpzl1 Mpzl3 Mr1 Mras Mrc1 Mrc2 Mreg Mrps22 Mrps33 Mrps6 Ms4a14 Ms4a4b Ms4a4c Ms4a6b Ms4a6c Ms4a6d Ms4a7 Ms4a8a Msantd3 Msi2 Msr1 Msrb2 Msrb3 Mt1 Mt2 Mt3 Mtap7d3 Mtcl1 Mtfp1 Mthfd1l Mtmr7 Mtus1 Muc1 Mum1 Mvb12b Mx1 Mx2 Mxra7 Mxra8 Mybl2 Myc Myct1 Myh14 Myl9 Mylk Myo18a Myo1b Myo1d Myo1e Myo1g Myo5b Myo6 Myo7a Myzap Nabp1 Nagk Naglu Napepld Napsa Nat8l Nav1 Nav2 Nav3 Nbeal2 Nbl1 Ncald Ncam1 Ncapd2 Ncaph Nccrp1 Nceh1 Nckap1 Nckap5 Ncoa4 Ndc1 Ndnf Ndrg1 Ndufa4l2 Ndufaf6 Nebl Nedd4 Nedd4l Nedd9 Negr1 Nek2 Nek6 Nes Net1 Neurl1b Neurl3 Nfatc2 Nfe2 Nfib Nfkbie Nfkbiz Ngf Ngfr Ngfrap1 Nhsl1 Nhsl2 Nid1 Ninj1 Nkain1 Nkd1 Nkg7 Nkx2-2 Nlgn2 Nlrc5 Nme4 Nod2 Nol4l Nos1ap Nos2 Nostrin Notch3 Npdc1 Nphp1 Npl Npnt Npr1 Nptx1 Nptxr Npy Nr2f1 Nr3c2 Nr4a1 Nr4a3 Nrarp Nrg1 Nrg4 Nrgn Nrn1 Nrp2 Nsmaf Nsun4 Nt5dc2 Nt5e Ntn1 Ntn4 Ntng2 Ntpcr Ntrk2 Nucb2 Nudt12 Nuf2 Nup210 Nup43 Nusap1 Nxn Nxpe4 Nxpe5 Nxt2 Oaf Oas1a Oas1g Oas2 Oas3 Oasl1 Oasl2 Oaz2 Obsl1 Ocln Ocstamp Ogn Oit3 Olfml3 Olr1 Ophn1 Oplah Orc1 Oscar Oscp1 Osgin1 Osgin2 Osmr Osr1 Osr2 Ovol2 P2ry10 P2ry12 P2ry13 P2ry14 P2ry2 P4ha2 Pabpc1l Pacsin3 Padi2 Padi4 Pag1 Pald1 Palld Palm Palmd Paox Papss2 Paqr3 Pard3 Parp16 Parva Parvg Pbk Pbx1 Pcbp4 Pcdh1 Pcdh7 Pced1b Pcgf2 Pcolce Pcolce2 Pcp4l1 Pcyox1 Pdcd1lg2 Pdcd4 Pde3a Pde5a Pde7b Pdgfa Pdgfb Pdgfc Pdgfra Pdgfrb Pdk4 Pdlim1 Pdlim4 Pdpn Pdzd2 Peak1 Pear1 Pecam1 Pecr Peg10 Penk Per2 Perp Pex11a Pf4 Pgam1 Pgap1 Pgk1 Pglyrp1 Pgpep1 Phactr1 Phf11a Phf11b Phf11d Phgdh Phlda3 Phlpp1 Phospho1 Pi16 Pianp Pif1 Pigz Pik3ap1 Pik3ip1 Pilrb1 Pitpnm2 Pkdcc Pkmyt1 Pkp3 Pla2g15 Pla2g7 Plac1 Plac8 Plagl1 Plau Plbd1 Plcb1 Plcb2 Plcb4 Pld2 Pld3 Plekhb1 Plekhg1 Plekhg2 Plekhn1 Plekho1 Plet1 Plk1 Plk4 Pllp Plod1 Plod2 Plscr1 Plscr4 Plvap Plxdc2 Plxna1 Pmaip1 Pmepa1 Pnpla7 Pnpla8 Podxl Podxl2 Pole Poli Pon3 Postn Pot1b Pou2f2 Ppa1 Ppap2a Pparg Ppargc1b Ppbp Ppfia4 Ppfibp1 Ppic Ppm1j Ppp1r12b Ppp1r13l Ppp1r14a Ppp1r15a Ppp1r16b Ppp1r3b Ppp1r9a Pram1 Prc1 Prdx1 Prelid2 Prelp Prex2 Prg4 Prickle1 Prickle2 Prim1 Prim2 Prkar1b Prkar2b Prkcb Prkcdbp Prkce Prkg2 Prl2c2 Prl2c3 Prnp Procr Pros1 Prr7 Prrg1 Prrg4 Prrt1 Prrx1 Prtn3 Prune2 Prx Psat1 Psd Psd3 Psen2 Psrc1 Pstpip1 Ptafr Pter Ptger2 Ptges Ptgfrn Ptgr1 Ptgs1 Ptgs2 Pthlh Ptk7 Ptms Ptn Ptp4a3 Ptpla Ptpn14 Ptpn18 Ptpn22 Ptpn3 Ptprb Ptprcap Ptprg Ptprj Ptprm Ptprn Ptpro Ptprs Ptpru Ptrf Ptrh1 Pvr Pvrl1 Pvrl3 Pxdc1 Pxdn Pxmp4 Pydc3 Pydc4 Pyhin1 Qdpr Qpct Qprt Rab11fip5 Rab19 Rab34 Rab36 Rab3il1 Rab44 Rab4a Rac3 Rad51 Raet1d Rai14 Ralgds Ramp1 Ramp2 Ramp3 Rap1gap2 Rapgef3 Rapgef4 Rapsn Rarres2 Rasa3 Rasa4 Rasgrf2 Rasgrp1 Rasgrp2 Rasgrp3 Rasgrp4 Rasip1 Rassf2 Rassf8 Rbfox2 Rbks Rbm38 Rbms3 Rbp4 Rbpj Rbpms Rbpms2 Rcn1 Rcor2 Rcsd1 Rdh10 Rdm1 Rel Rem1 Renbp Reps2 Rere Retnla Rfc3 Rfx2 Rgag4 Rgl1 Rgma Rgs1 Rgs10 Rgs16 Rgs17 Rgs18 Rhbdf1 Rhobtb1 Rhoc Rhof Rhoj Rhou Rhov Rhox2d Rhox2g Rhox2h Rilpl1 Rims3 Ripk2 Ripply3 Rmi2 Rnase2b Rnase6 Rnd1 Rnf128 Rnf144a Rnf144b Rnf150 Rnf180 Rnf213 Robo2 Robo4 Rogdi Ropn1l Rora Rpl13 Rpl36 Rpp40 Rps12l1 Rps4l Rps6ka2 Rpusd3 Rrad Rras2 Rrm1 Rrm2 Rsad2 Rspo1 Rtkn Rtn1 Rtp3 Rtp4 Rufy4 Rundc3a Runx3 Rxra Ryk S100a1 S100a16 S100a4 S100a6 S100a8 S100a9 S1pr1 S1pr4 S1pr5 Saa3 Sac3d1 Samd4 Samsn1 Sap25 Sardh Sash1 Satb1 Satb2 Scamp5 Scara3 Sccpdh Scgb1a1 Scgb3a1 Scgb3a2 Scimp Scn3b Scn7a Scnn1a Scube2 Sdc1 Sdc2 Sdc4 Sdpr Sdr39u1 Sec14l2 Sec14l3 Sec61g Sell Selm Selp Sema3a Sema3c Sema3e Sema3f Sema3g Sema4b Sema4c Sema4d Sema5a Sema6a Sema6d Sema7a Sept1 Sept4 Sept5 Sept6 Serf1 Serpina3g Serpinb1a Serpinb2 Serpinb6a Serpinb8 Serpine1 Serpinf1 Serping1 Serpinh1 Sertad4 Sesn1 Sesn3 Sestd1 Setbp1 Sfmbt2 Sftpa1 Sftpb Sftpc Sftpd Sgip1 Sgk1 Sgms2 Sgol1 Sgsh Sgsm3 Sh2d1b1 Sh2d3c Sh2d4b Sh3bgrl2 Sh3bp4 Sh3d19 Sh3pxd2b Shisa4 Shmt1 Shmt2 Shroom3 Siglec1 Siglecf Siglecg Sik1 Six1 Six4 Skint3 Slamf6 Slamf7 Slamf8 Slamf9 Slc11a1 Slc12a2 Slc12a7 Slc13a3 Slc14a1 Slc15a2 Slc16a1 Slc16a13 Slc16a3 Slc16a6 Slc16a9 Slc17a9 Slc18a1 Slc1a3 Slc20a2 Slc22a17 Slc22a18 Slc22a23 Slc23a2 Slc24a1 Slc25a23 Slc27a1 Slc27a3 Slc29a1 Slc29a3 Slc2a1 Slc2a3 Slc2a6 Slc30a4 Slc34a2 Slc37a2 Slc38a4 Slc38a6 Slc39a12 Slc39a14 Slc39a2 Slc40a1 Slc41a2 Slc43a3 Slc44a2 Slc46a1 Slc46a3 Slc4a11 Slc52a3 Slc5a3 Slc5a6 Slc6a12 Slc6a4 Slc6a8 Slc7a11 Slc7a2 Slc7a8 Slc9a2 Slc9a3r2 Slc9a4 Slc9a7 Slc9b1 Slc9b2 Slco2a1 Slco2b1 Slco3a1 Slfn1 Slfn4 Slfn5 Slfn8 Slfn9 Slirp Slpi Smad3 Smad6 Smagp Smc2 Smim5 Smo Smox Smpdl3b Smtn Smyd2 Snai1 Snai2 Snn Snrpg Sntb1 Snx24 Snx7 Socs1 Socs2 Socs3 Sod3 Sorl1 Sort1 Sox13 Sox17 Sox18 Sox4 Sox7 Sox9 Sp100 Spag11b Spata13 Spats2 Spc24 Spc25 Spdl1 Speg Sphk1 Spic Spink2 Spint1 Spint2 Spire1 Spn Spns1 Spns2 Spock2 Spon1 Spp1 Spred1 Spred3 Spry2 Spry4 Spsb1 Src Srgap3 Srgn Srm Srxn1 Ssbp2 Ssc5d Sstr4 St18 St3gal5 St5 St6galnac2 St7 St8sia4 St8sia6 Stab1 Stac2 Stap1 Stat4 Stc1 Steap2 Steap3 Stk10 Stk39 Stmn1 Stmn2 Ston1 Ston2 Stra6l Strip2 Stx11 Stxbp6 Sulf2 Sult1a1 Sult2b1 Susd1 Susd3 Syce2 Syn1 Syne1 Syne2 Syngr1 Synj2 Synpo Synpo2 Syp Syt11 Tacc2 Tacc3 Tagap Tagln Tanc2 Tap1 Tarm1 Tbc1d2 Tbc1d4 Tbc1d8 Tbx2 Tbx3 Tc2n Tcea3 Tcf19 Tcf21 Tcf4 Tcf7 Tcf7l1 Tcf7l2 Tctex1d2 Tead1 Tead2 Tead4 Tek Tenm4 Tes Tex22 Tfap2a Tfec Tgfb1i1 Tgfb3 Tgfbi Tgfbr3 Tgm1 Tgm3 Tgtp1 Tgtp2 Thbd Thbs1 Thbs2 Thop1 Thsd1 Tiam1 Tiam2 Tie1 Tifa Timd4 Timp1 Timp3 Tinagl1 Tipin Tk1 Tle6 Tlr1 Tlr12 Tlr4 Tlr5 Tlr9 Tm4sf19 Tma7 Tmc6 Tmc8 Tmem100 Tmem107 Tmem119 Tmem138 Tmem150a Tmem151a Tmem154 Tmem171 Tmem176a Tmem176b Tmem204 Tmem216 Tmem238 Tmem243 Tmem26 Tmem37 Tmem41a Tmem47 Tmem64 Tmem71 Tmem86a Tmem88 Tmem98 Tmtc2 Tnc Tnfaip2 Tnfaip6 Tnfrsf10b Tnfrsf14 Tnfrsf18 Tnfrsf22 Tnfrsf23 Tnfrsf26 Tnfrsf9 Tnfsf13 Tnfsf14 Tnfsf9 Tnip3 Tnk2 Tns1 Tns3 Tob1 Tomm6 Top2a Tox2 Tpcn1 Tpi1 Tpk1 Tpm1 Tpm2 Tppp Tppp3 Tpx2 Traf1 Traf3ip2 Traf3ip3 Traf4 Trdmt1 Trem2 Trem3 Treml2 Treml4 Trf Trib2 Trib3 Trim13 Trim2 Trim29 Trim30b Trim30c Trim46 Trim47 Trim7 Trip6 Trp53i11 Trpm2 Tsen15 Tsku Tspan12 Tspan13 Tspan18 Tspan2 Tspan3 Tspan32 Tspan33 Tspan4 Tspan7 Ttc23 Ttc39b Ttyh2 Tuba1a Tuba1b Tubb2a Tubb3 Twist1 Twist2 Tyms Uaca Ube2c Ucp3 Ugt1a8 Uhrf1 Unc119 Unc13d Unc5a Unc5b Ung Upb1 Upk3b Upp1 Usp18 Usp43 Usp49 Ust Utrn Vangl1 Vasn Vat1 Vav2 Vav3 Vcam1 Vcan Vcl Vdr Vegfa Vegfb Vegfc Vill Vipr2 Vkorc1l1 Vldlr Vnn3 Vps37b Vstm2a Vtn Wdr34 Wdr76 Wfdc10 Wfdc17 Wfdc2 Wfdc21 Wisp1 Wnt11 Wnt5a Wnt7b Wwtr1 Xaf1 Xdh Xlr Xrcc5 Xylb Xylt2 Yap1 Zbp1 Zbtb16 Zbtb46 Zc3h12c Zcchc11 Zcchc24 Zdhhc14 Zdhhc2 Zeb1 Zfand2a Zfhx3 Zfp125 Zfp296 Zfp366 Zfp36l1 Zfp428 Zfp462 Zfp467 Zfp516 Zfp580 Zfp770 Zfp821 Zfp827 Zfp935 Zfp948 Zfyve9 Zhx2 Zic2 Zmynd15 Zranb3 Genes upregulated in MacA-2wk vs MacA-N Symbol logFC logCPM PValue FDR Lum 10.9603 3.801 0 0 Dkk2 10.812 3.6185 0 0 Hmga2 9.3344 4.4417 0 0 Twist1 9.2808 2.0976 0 0 Bcat1 9.2712 2.0926 0 0 Ptn 9.0747 1.8433 0 0 Etv4 8.4289 1.2141 0 0 Rhox2h 7.9382 0.7562 0 0.0004 Saa3 7.0605 -0.1753 0.0001 0.0137 Arg1 6.9062 3.2543 0 0 Serpinf1 6.6155 3.5105 0 0 Il1rl1 6.303 3.1955 0 0 C1qc 6.2819 4.1952 0 0 Gpx8 6.2129 1.3321 0 0.0002 Flnc 5.7935 3.4288 0 0 C1qa 5.5528 4.2871 0 0 Egln3 5.4388 3.119 0 0 Gja1 5.2862 5.2954 0 0 Peg10 5.1564 4.0888 0 0 Sox9 5.1221 2.8115 0 0.0001 Fscn1 5.0107 4.6104 0 0 AA467197 4.8455 3.3034 0 0 Prrx1 4.703 4.685 0 0 C3ar1 4.6783 4.2001 0 0 Nptx1 4.5585 4.0506 0 0 Ank 4.2669 4.7918 0 0 C1qb 4.1848 4.5883 0 0 Col3a1 4.1721 7.0599 0 0 Steap2 4.1692 3.4994 0 0 Ak1 4.0768 1.9845 0.0003 0.0234 Dnm1 3.895 2.5682 0 0 Oas3 3.8614 4.2996 0 0 Tsku 3.7976 3.41 0 0 Ier5l 3.6268 3.3468 0 0.0002 Igf2r 3.5442 4.1816 0 0 Ifi27l2a 3.5278 1.1772 0 0 Mmp14 3.5219 4.4728 0 0 Fgfr1 3.5149 4.024 0 0 Lpar1 3.4298 1.5359 0 0.0025 Sdc1 3.4041 3.2627 0 0 Slc6a8 3.3696 4.6415 0 0 Ncam1 3.3515 1.0484 0 0.0008 S100a6 3.3181 5.4606 0 0 Flrt2 3.3136 3.7034 0 0.0013 Col16a1 3.3004 1.3952 0.0005 0.0353 Htra1 3.2894 2.5939 0 0 Igfbp4 3.2701 6.4414 0 0 Msr1 3.217 5.2838 0 0 Met 3.1754 3.4567 0 0 Cxcl9 3.0665 4.0039 0 0 Serpinh1 3.0036 5.8496 0 0.0003 Adam8 2.9934 3.835 0 0 Dcn 2.979 4.9939 0 0 Slc40a1 2.939 4.5548 0 0.0017 Lamb1 2.9344 4.4204 0 0 Fam20c 2.9266 3.8891 0 0 Angptl2 2.9218 3.9139 0 0 Ppic 2.9162 2.5614 0 0.0017 Timp1 2.9146 1.0263 0.0001 0.0069 Bmp1 2.9092 2.2441 0 0.0028 Aif1 2.8862 0.3681 0 0.0003 Ddr2 2.884 4.6701 0 0 Mmp12 2.86 5.3105 0 0 Nid1 2.8548 5.5353 0 0 Hspg2 2.8509 5.4119 0 0 Dpysl3 2.8458 4.255 0 0.0003 Tead2 2.8194 2.1677 0.0001 0.0078 Vcan 2.8091 4.9717 0 0 Pla2g7 2.7578 4.067 0 0 Irf7 2.7478 4.193 0 0 Ifit1 2.7141 3.7658 0 0 Il6 2.6797 2.204 0.0002 0.021 Syngr1 2.6631 1.5191 0 0.0019 Apln 2.6533 3.355 0.0005 0.0407 Pcolce 2.6403 4.0638 0 0.0001 Pear1 2.6353 2.0763 0.0003 0.0232 Mnda 2.5989 2.9863 0 0 Mafb 2.5969 5.1555 0 0 Prkcdbp 2.5921 2.4041 0.0003 0.0244 Spry4 2.578 4.0894 0.0001 0.007 Prnp 2.5777 2.4233 0.0001 0.0137 Ptgs1 2.5761 5.377 0 0 Krt18 2.5595 2.705 0.0003 0.0274 Abcc3 2.5395 2.0842 0 0 Sdc4 2.5347 4.5725 0 0.0001 Oasl2 2.527 4.7568 0 0 Ckap4 2.5202 4.1775 0 0 Ikbke 2.5175 2.9813 0 0 Iigp1 2.5136 3.2778 0 0.0002

Genes downregulated in MacA-2wk vs MacA-N Symbol logFC logCPM PValue FDR Scgb1a1 -3.3349 8.7934 0 0.0009

Genes upregulated in MacB3-3wk vs MacB3-2wk Symbol logFC logCPM PValue FDR Rhox2h 6.1922 1.6815 0 0.0013 Sertad4 4.1735 3.3315 0 0.0021 Prg4 3.6116 7.2485 0 0 Osr2 3.4672 4.0176 0.0001 0.0339 Ptn 3.3507 4.902 0 0 Rhox2d 3.3069 1.6999 0.0001 0.0272 Fstl3 3.298 2.7251 0.0001 0.0167 Dcn 3.2632 6.6456 0 0.0001 Cdc42ep5 3.1187 2.3035 0 0.0037 Tnc 3.0894 5.3852 0 0.0056 Prrx1 3.0647 5.744 0 0.0008 Hspb8 3.0221 2.6363 0 0.0089 Etv4 2.9153 2.5319 0 0.0092 Sdc2 2.8588 4.1438 0 0.0011 Nhsl1 2.8536 3.4476 0 0.0005 Peg10 2.8131 5.2545 0 0.0024 Stc1 2.8073 4.3516 0 0.0056 Bmp1 2.7849 4.8307 0 0.0077 Kirrel 2.7647 4.795 0 0.0026 Marco 2.7374 4.8168 0.0002 0.0476 Aspn 2.705 3.4653 0 0.0153 Dkk2 2.6991 5.6633 0 0.0008 Thbs2 2.6956 4.8353 0 0.0095 Col18a1 2.6949 6.0673 0 0.0001 Krt18 2.6619 3.8718 0.0001 0.0272 Lum 2.6587 4.4799 0.0002 0.0436 Dnm1 2.6289 4.788 0 0.0007 Tcf7l1 2.6083 3.8778 0 0.0055 Lamb1 2.6001 5.9292 0 0.0004 Islr 2.5891 3.017 0 0.0123 Apcdd1 2.5743 4.6888 0 0.0084 Dpt 2.569 4.1147 0.0001 0.0311 Hspg2 2.5455 7.7673 0 0.0003 Flnc 2.5413 5.8236 0 0.0015 Cd248 2.505 4.2082 0.0001 0.0251

Genes downregulated in MacB3-3wk vs MacB3-2wk Symbol logFC logCPM PValue FDR Fmo1 -3.0335 2.9927 0 0.0055 Cxcl13 -2.9294 3.4168 0 0.0111 Cldn5 -2.6429 3.009 0 0.0123 Tmem100 -2.54 2.7681 0 0.0114 Genes upregulated in MacB2-3wk vs MacB2-N Symbol logFC logCPM PValue FDR Ptprn 11.7066 4.6885 0 0 Dusp9 11.2658 4.2527 0 0 Lrrc15 11.2091 4.1936 0 0 Ereg 10.6789 3.6702 0 0 Kera 10.6245 3.5942 0 0 Foxg1 10.6105 3.6045 0 0 Etv4 10.5823 3.5729 0 0 Slc38a4 10.5571 3.5321 0 0 Dlx2 10.5314 3.5373 0 0 Rgs16 10.1241 3.1177 0 0 Rad51 9.8672 2.8509 0 0 Ngf 9.8436 2.8173 0 0 Hoxd8 9.7322 2.7197 0 0 Rhox2d 9.7192 2.7005 0 0 Osr2 9.6486 5.023 0 0 Twist1 9.4802 4.8644 0 0 Rhox2g 9.3929 2.3761 0 0 Pthlh 9.3847 2.3738 0 0 Fam64a 9.3004 2.2959 0 0 Dlx1 9.283 2.2784 0 0.0001 Rhox2h 9.2474 2.2336 0 0 Plac1 9.2384 2.2099 0 0 Thbs2 9.2098 5.9681 0 0 Grem1 9.1194 4.4961 0 0 Car6 9.1056 2.1059 0 0 Ptn 9.0822 5.8482 0 0 Nkx2-2 8.9484 1.945 0 0 Arg1 8.8698 7.5557 0 0 Prl2c2 8.8687 1.8735 0 0.0001 Hmga2 8.7028 6.981 0 0 Dkk2 8.6541 6.761 0 0 Sertad4 8.6134 3.9791 0 0 Prl2c3 8.4508 1.4445 0 0.0001 Oscar 8.4192 1.4231 0 0 Fhl2 8.2883 3.6631 0 0 Mcoln3 8.2853 1.2892 0 0 Bnc1 8.2579 3.6282 0 0 Gchfr 8.2317 1.2397 0 0.0001 Ak4 8.2134 1.2063 0 0.0001 Insl6 8.176 1.1848 0 0 Slc9b1 7.9733 0.9562 0 0 Pif1 7.9513 0.9477 0 0 Aif1l 7.8834 0.8806 0 0 Acsbg1 7.8834 0.8987 0 0 Glrp1 7.8834 0.8749 0 0 Sox9 7.819 6.0999 0 0 Ccl8 7.7876 0.7596 0 0 Plk1 7.6328 3.8775 0 0 Tenm4 7.6031 7.0106 0 0 Zic2 7.4761 3.7137 0 0 Ccdc136 7.4549 0.4758 0 0.0001 2200002D07.3912 0.3835 0.0001 0.0014 Flnc 7.2362 6.6522 0 0 Tnc 7.1801 6.258 0 0 Igf2bp2 7.1513 5.7205 0 0 Arntl2 7.1036 0.0854 0.0006 0.0095 Gm773 7.0632 0.0386 0.0003 0.0052 Lum 7.0592 6.0421 0 0 Ccnb2 6.9674 3.2252 0 0 Peg10 6.9662 6.3776 0 0 Prrx1 6.8975 6.9796 0 0 Lrrc17 6.7888 3.5653 0 0 Mmp12 6.7668 5.7974 0 0 Scara3 6.7532 4.2258 0 0 Kif18b 6.7046 2.9584 0 0 Gli3 6.6739 4.9679 0 0 Aspn 6.6426 4.3511 0 0 Epb4.1l3 6.6324 3.4123 0 0 Clstn3 6.5953 2.8515 0 0 Cxcl3 6.5939 7.1272 0 0 AA467197 6.4986 5.5179 0 0 Twist2 6.4388 3.2238 0 0 Wnt7b 6.3945 4.3306 0 0 Rtkn 6.3674 3.5397 0 0 Il1rl1 6.3448 5.7703 0 0 Hist1h2ah 6.2564 4.1976 0 0 Lrig1 6.2246 5.4164 0 0 Fah 6.1747 2.4228 0 0 Cd109 6.1733 4.3124 0 0 Timp1 6.1294 4.0718 0 0 Tfap2a 6.0925 4.3883 0 0 Nptxr 6.0641 3.2553 0 0 Stc1 6.0451 5.1539 0 0 Dctd 5.987 2.248 0 0 Ctsk 5.9681 5.5456 0 0 Ect2 5.9594 2.2244 0 0 Fscn1 5.9313 7.1339 0 0 Matn2 5.9063 3.8531 0 0 Fkbp11 5.898 1.3078 0 0.0001 Bub1 5.8974 3.371 0 0 Lgi2 5.8792 3.816 0 0 E2f7 5.8425 3.33 0 0 Ube2c 5.8282 5.3389 0 0 Cpe 5.8146 4.627 0 0 Spp1 5.7829 8.1197 0 0 Dnph1 5.7816 2.0487 0 0 Serpinf1 5.7768 5.6292 0 0 Hist1h3g 5.7464 5.095 0 0 Gpnmb 5.7345 7.591 0 0 Gja1 5.6798 7.334 0 0 Spc24 5.5666 1.8337 0 0 Sdr39u1 5.5335 2.3452 0 0 Nav3 5.5119 3.8057 0 0 Six4 5.5009 4.2197 0 0 Col3a1 5.4624 9.8468 0 0 Nuf2 5.4614 2.6461 0 0 Nptx1 5.4606 6.3665 0 0 Phgdh 5.4375 3.5745 0 0 Uhrf1 5.4092 4.6189 0 0 Mapk8ip1 5.4008 3.5533 0 0 Apcdd1 5.3828 5.4484 0 0 Steap2 5.3695 5.1809 0 0 Cda 5.3684 2.5432 0 0 Rab34 5.346 2.5369 0 0 Nos2 5.3139 4.3403 0 0 Hist1h3c 5.3086 6.1465 0 0 Col18a1 5.2973 6.7855 0 0 Flrt2 5.2966 5.5295 0 0 Tnfrsf22 5.2692 2.0822 0 0 Mmp9 5.2618 8.287 0 0 Lrr1 5.2436 0.6516 0 0.0003 Spats2 5.2291 3.8275 0 0 Ccna2 5.2276 5.1983 0 0 Inhba 5.2219 4.9374 0 0 Rcor2 5.1934 3.3347 0 0 Prune2 5.1814 5.2962 0 0 Col8a1 5.1342 4.2438 0 0 Cdc20 5.1209 3.7323 0 0 Sgol1 5.1164 1.9243 0 0 Arhgap22 5.1063 4.6377 0 0 Pdgfc 5.0882 4.2251 0 0 Mmp14 5.0738 7.6307 0 0 Ccbe1 4.9841 5.9946 0 0 Spint1 4.9712 4.5021 0 0 Mfsd2a 4.9709 0.3988 0.0002 0.0031 Egln3 4.9551 5.8238 0 0 Bmp1 4.9364 5.4824 0 0 Col16a1 4.9243 4.5689 0 0 Nr2f1 4.9073 3.234 0 0 Ccl7 4.9026 4.1382 0 0 Dpysl3 4.8877 6.2663 0 0 Rgs17 4.8684 2.619 0 0 Prkg2 4.8559 4.155 0 0 Obsl1 4.7761 3.7246 0 0 Rundc3a 4.7655 2.5109 0 0.0002 Cxcl1 4.7431 5.5 0 0 Ptges 4.7359 5.8376 0 0 Cenpf 4.7285 4.6675 0 0 Birc5 4.7224 3.8731 0 0 Dnm1 4.6993 5.5774 0 0 Col5a1 4.689 6.8952 0 0 Fgfr1 4.6775 6.5946 0 0 Dcn 4.6741 7.8546 0 0 Hspb8 4.6733 3.8086 0 0 Serpinb2 4.6612 4.3419 0 0 Eya1 4.6422 3.881 0 0 Mt2 4.6304 4.7335 0 0 Slc30a4 4.6252 5.3191 0 0 Htra1 4.6052 5.5315 0 0 Tk1 4.5787 2.9175 0 0 Krt18 4.5613 4.9455 0 0 Fcrlb 4.5609 3.3245 0 0 Il33 4.5427 3.9994 0 0 Lamb1 4.5236 7.0224 0 0 Bcat1 4.5192 3.4631 0 0 Spry2 4.5133 5.0449 0 0 Adamtsl3 4.5073 4.0974 0 0 Hspg2 4.5007 8.7475 0 0 Gpc1 4.4951 6.2911 0 0 Zfp428 4.4921 1.3412 0.0001 0.0016 Spc25 4.4705 2.2215 0 0 Il7r 4.4676 4.0195 0 0 Tpm2 4.4659 4.2576 0 0 Arhgef40 4.4343 5.0657 0 0 Ak1 4.4251 3.3954 0.0002 0.0036 Btbd11 4.4035 3.794 0 0 Adamts14 4.3888 4.2276 0 0 Adm 4.3543 3.1015 0 0 Tnfrsf9 4.327 1.8568 0 0.0002 Actn1 4.3135 7.4011 0 0 Prrg4 4.3108 3.273 0 0 Medag 4.3098 2.4987 0 0 Top2a 4.3062 5.5318 0 0 Lhfpl2 4.2721 5.392 0 0 Asns 4.2687 4.0027 0 0 Igfbp4 4.2674 8.958 0 0 Sh3bgrl2 4.2574 4.568 0 0 Ssc5d 4.2508 4.4998 0 0 Pcolce 4.2339 6.5853 0 0 Unc5a 4.2303 3.0095 0 0 Spsb1 4.2292 4.5923 0 0 Islr 4.2159 4.019 0 0 Saa3 4.2157 3.73 0 0.0009 C77080 4.2108 5.4385 0 0 Pcbp4 4.2102 3.8356 0 0 Kcnq5 4.2005 4.1431 0 0 Smyd2 4.1817 3.4973 0 0 Usp43 4.1787 2.9401 0 0 Chpf 4.1757 5.6875 0 0 Hist1h3b 4.1684 4.6463 0 0 Map1b 4.1681 7.7376 0 0 Gpr153 4.1651 4.232 0 0 Nrn1 4.1592 2.1558 0 0.0003 Ccnf 4.1573 3.3262 0 0 Cdca3 4.1468 1.95 0 0 Ahnak2 4.1468 6.0525 0 0 Kirrel 4.1401 5.6768 0 0 Pcdh7 4.1341 4.7825 0 0 Vegfb 4.0746 3.9495 0 0 Ltc4s 4.0448 3.4939 0 0 Myc 4.0384 8.8914 0 0 Cdca8 4.0359 2.9089 0 0 Syn1 4.0344 4.0143 0 0 Cdk1 4.0313 2.223 0 0 Slc9b2 4.0308 1.2602 0.0001 0.0014 Anpep 4.0276 6.5413 0 0 Foxm1 3.9954 4.4522 0 0 Tpx2 3.9918 4.4814 0 0 2810417H13.9487 3.8625 0 0 Mki67 3.9474 6.831 0 0 Pdpn 3.9361 6.1125 0 0 Myo1e 3.9344 5.8057 0 0 Hist1h2bm3.93 4.1364 0 0 Rgma 3.928 4.6289 0 0 Aebp1 3.9232 6.164 0 0 Rai14 3.9092 5.5816 0 0 Rhov 3.9061 4.5172 0 0 Sdc1 3.9058 6.2789 0 0 BC055324 3.9053 1.7202 0 0 Anln 3.9037 4.6847 0 0 Adcy3 3.8974 4.1687 0 0 Lpar1 3.8768 3.6338 0 0 Loxl3 3.8693 4.1162 0 0 Ckap2 3.8652 3.1133 0 0 Fkbp10 3.8638 4.8907 0 0 Cdc42ep5 3.86 3.1307 0 0 Chaf1a 3.8524 3.4426 0 0 Cks1b 3.8501 3.7849 0 0 Ank3 3.8409 4.5664 0 0 Nek6 3.837 4.5436 0 0 A930004D 3.8155 1.3861 0 0.0003 Ppic 3.8152 4.7786 0 0 Csgalnact1 3.8028 3.8337 0 0 Zfp827 3.7881 4.4503 0 0 Tspan4 3.7838 3.1344 0 0 Mif 3.7807 7.7256 0 0 Fam92a 3.7802 1.9763 0 0.0002 Ptpla 3.7802 2.0116 0 0 Rbpms2 3.7765 2.9511 0 0.0008 Marco 3.7655 4.9706 0 0.0001 B3gnt9 3.7639 3.0108 0 0 Wisp1 3.7595 3.5137 0.0001 0.0012 Syngr1 3.7531 2.9617 0 0 Unc5b 3.751 5.145 0 0 Plau 3.7435 4.8218 0 0 Serpine1 3.7429 6.15 0 0 Ccl12 3.7338 1.9569 0 0.0001 Prc1 3.719 3.8132 0 0 Spdl1 3.7182 1.7609 0 0 Fstl1 3.7151 5.4118 0 0 Col6a3 3.7127 7.5836 0 0 Wfdc21 3.71 1.9438 0 0 Igf2r 3.706 6.1984 0 0 Cdc7 3.6973 1.5304 0 0 Cald1 3.6837 8.2872 0 0 Traf4 3.6728 4.2782 0 0 Slc16a1 3.6722 5.6341 0 0 Nkain1 3.6532 3.4701 0 0.0002 Stac2 3.6015 3.4195 0 0 Akr1b8 3.5927 3.388 0 0 Acp5 3.5903 6.9213 0 0 Psrc1 3.5847 1.8381 0 0.0001 Dcbld2 3.5792 4.0566 0 0 Hic1 3.5777 5.6207 0 0 Chst11 3.5619 5.8294 0 0 Gas1 3.5599 4.2436 0 0 Nid1 3.5468 7.7034 0 0 Ntn1 3.5306 6.9928 0 0 Hmgn3 3.5248 4.1513 0 0.0001 Hist1h2af 3.5242 2.795 0 0 Trp53i11 3.5048 5.4732 0 0 Egfr 3.5035 5.4842 0 0 Angptl2 3.5031 5.9293 0 0 Vasn 3.5026 4.8072 0 0 Slc6a8 3.5021 5.8095 0 0 Basp1 3.5015 7.2405 0 0 C3ar1 3.501 7.1931 0 0 Parp16 3.4994 2.6216 0 0 Hoxa7 3.4983 3.5975 0.0004 0.0062 Il18rap 3.497 4.5685 0 0 Mrps6 3.4925 3.1662 0 0 Col6a1 3.4604 6.5858 0 0 Prkcdbp 3.448 4.3628 0 0 Hist1h2bl 3.4421 2.1249 0.0003 0.0053 Shmt2 3.4392 5.8565 0 0 Nusap1 3.4285 3.5827 0 0 Farp1 3.4274 5.3185 0 0 Tgfb3 3.4226 4.8096 0 0 Clmp 3.4192 5.3027 0 0 Il1r1 3.4181 5.3039 0 0 Cul7 3.4169 5.7477 0 0 Sdc4 3.4154 6.9772 0 0 Cd63 3.4135 3.9622 0 0 Tyms 3.4104 3.3241 0 0 Nphp1 3.3991 2.0814 0 0.0001 Rbfox2 3.3975 5.5115 0 0 Bhlhe41 3.3931 4.9249 0 0 Rrm2 3.3761 4.7312 0 0 Dab2 3.3714 7.8092 0 0 Plod2 3.3648 5.0417 0 0 Krt8 3.3638 3.6529 0 0.0003 Cit 3.3607 3.8252 0 0 Cd276 3.3585 5.3069 0 0 Nme4 3.3525 1.7674 0.0001 0.0017 Nt5e 3.3484 3.8866 0 0 C330027C03.3468 2.2516 0 0 Oaf 3.343 5.2127 0 0 Leprel2 3.3344 3.0169 0 0 Mcm6 3.3206 5.495 0 0 Tsen15 3.3139 2.6525 0 0 Nek2 3.3116 2.9274 0 0 Tnfrsf23 3.3003 3.945 0 0 Gpr124 3.2982 5.1195 0 0 Gjc1 3.2853 2.7605 0.0002 0.0033 Tpm1 3.2836 6.7156 0 0 Procr 3.2819 3.6707 0 0 Kifc1 3.2602 2.5762 0 0 Sh3pxd2b 3.2564 8.2326 0 0 Tcf7l1 3.2554 4.8234 0 0 Dpt 3.2543 5.2837 0 0 Fhl3 3.2519 5.3976 0 0 Ltbp3 3.2388 5.7623 0 0 Nrp2 3.235 7.7059 0 0 2700081O13.2349 4.3324 0 0 Tubb3 3.2147 2.6933 0.0004 0.0062 Osr1 3.2062 3.4719 0.0003 0.0046 Cad 3.2058 4.9378 0 0 Lrp12 3.1881 4.4244 0 0 Serpinh1 3.1827 7.5376 0 0 Arhgap10 3.1781 4.7062 0 0 F7 3.1664 4.0147 0 0 Hist1h2ab 3.1535 6.6506 0 0 Mcm7 3.1496 4.5553 0 0 Sema3a 3.1494 4.6818 0 0.0011 S100a8 3.1422 3.4794 0 0.0003 Clip3 3.1356 4.1879 0 0.0003 Maged1 3.1352 6.3763 0 0 Mpzl1 3.1345 3.6672 0 0.0007 Sept5 3.1339 1.7088 0.0001 0.0019 Cd248 3.1244 5.3628 0 0 Col6a2 3.1122 6.1198 0 0 Fblim1 3.0967 5.1111 0 0 Epb4.1l1 3.0951 5.3625 0 0 Dagla 3.088 4.6802 0 0 Hist1h2bk 3.086 2.5834 0 0 Sdc2 3.0728 5.3479 0 0 Ccne1 3.0716 1.368 0 0.0007 Prg4 3.0715 7.7563 0 0 Trim47 3.0708 4.3201 0 0 Fbn1 3.066 7.1398 0 0 Bag2 3.0602 2.8504 0.0003 0.0047 Atp6v0d2 3.0553 5.3093 0 0 Gnb4 3.0551 4.0487 0 0 Crtap 3.0458 4.5239 0 0 Rab4a 3.0427 2.6769 0 0.0002 Slc41a2 3.0393 3.6466 0 0 Wdr34 3.0338 1.3257 0.0004 0.0069 Src 3.0292 5.1725 0 0 Serpinb6a 3.0262 4.5637 0 0 Lama5 3.0178 5.6032 0 0 Pf4 3.0079 5.1052 0 0 Serf1 2.9984 3.3537 0 0 Etv1 2.9968 3.9447 0 0.0001 Hells 2.9961 4.1345 0 0 F3 2.9946 3.4174 0.0003 0.0047 Ero1l 2.9845 5.4929 0 0 Gpx8 2.9783 3.2474 0.0005 0.0078 Itpr3 2.9722 5.769 0 0 Hmgn1 2.9695 5.1822 0 0 Lig1 2.9679 3.5572 0 0 Emp1 2.9668 7.8497 0 0 Kdelr3 2.963 2.2428 0.0027 0.0326 Pcgf2 2.9426 3.9432 0 0 Micall2 2.9421 4.7846 0 0 Il1a 2.9416 4.6123 0 0 Tcf19 2.9369 3.4924 0 0 Pard3 2.9288 4.3156 0 0 Ccl24 2.9281 3.2203 0 0.0004 Pdgfa 2.9258 4.0469 0 0 Mast4 2.9223 6.669 0 0 Tipin 2.9205 2.0163 0.0002 0.0029 Dst 2.9154 7.4012 0 0 Mad2l1 2.9114 4.1576 0 0 1810011O12.891 6.2183 0 0 Creb5 2.886 5.0225 0 0 Ankrd13b 2.874 4.122 0 0 Lmna 2.8452 7.6234 0 0 Rcn1 2.8407 4.6211 0 0.0001 Etv5 2.8407 4.6537 0 0 Sema4c 2.8363 5.3005 0 0 Fcrls 2.8362 3.75 0 0 Synpo 2.8346 5.9818 0 0 Spred3 2.8278 3.9701 0 0 Il1rn 2.8246 8.1814 0 0 Ptpn14 2.823 5.6249 0 0 Cd72 2.8221 3.1413 0 0 Vdr 2.8127 4.1432 0 0 Ramp3 2.8056 2.2622 0.0001 0.0016 Gys1 2.804 4.7885 0 0 Vcam1 2.8026 5.2467 0 0 Dut 2.7991 3.908 0 0 Nedd4 2.7955 7.6221 0 0 Col1a1 2.7728 7.224 0 0 Cenpv 2.7664 2.0555 0 0.0001 Ctsl 2.7662 7.0409 0 0 Ndc1 2.7528 3.586 0 0 Ltbp1 2.752 5.7251 0 0 Zfp462 2.7465 5.6971 0 0 Sac3d1 2.7451 2.7452 0 0 Smo 2.7432 4.4355 0 0.0001 Cxcl9 2.7371 5.2424 0 0 Rhoc 2.7368 5.9984 0 0 Cdca7 2.7357 2.4835 0 0 Cep170b 2.7326 4.9063 0 0 Tead1 2.7305 6.556 0 0 Hist1h2bh 2.726 4.1766 0 0 Ppfibp1 2.7185 5.4438 0 0 Ifitm1 2.7179 5.5832 0 0 Srgap3 2.7128 4.3305 0 0 Lamc1 2.7123 7.2088 0 0 Lrg1 2.7097 4.6606 0 0 Mmp11 2.7034 2.0653 0.0017 0.0215 Mt1 2.7015 6.9297 0 0 Tes 2.6971 6.9146 0 0 Ddr2 2.6946 6.0279 0 0 Rhbdf1 2.6913 3.6497 0 0 Ctxn1 2.6827 3.2923 0.0001 0.0015 Ttyh2 2.6821 4.2588 0 0 B3gnt3 2.6737 5.1312 0 0 Plk4 2.6721 3.324 0 0 Mcm2 2.6704 4.3195 0 0 Ppa1 2.6664 4.2598 0 0 Bnip3 2.6599 5.4776 0 0 Slc27a1 2.6588 3.7735 0 0 Srm 2.6558 5.1183 0 0 Lgals1 2.6446 7.1401 0 0 Apbb2 2.6443 5.9518 0 0 Rfc3 2.6401 2.9097 0 0 Slc39a14 2.6381 5.8836 0 0 Slc20a2 2.6355 3.9603 0 0 Slc2a1 2.6314 8.6414 0 0 Cxxc5 2.6283 3.8795 0.0001 0.0012 Hmga1 2.6212 7.9257 0 0 Dbn1 2.6056 3.1952 0.0006 0.0094 Rnf128 2.6048 3.3728 0 0 Cbx5 2.5846 5.9754 0 0 Trib3 2.5838 2.8019 0 0 Snai1 2.5776 5.6521 0 0 Efemp2 2.5661 3.7999 0 0 Six1 2.5654 2.274 0.0002 0.0037 Dennd2a 2.5642 4.4799 0 0 Gpr84 2.5637 2.9236 0 0 Rrm1 2.5605 4.7463 0 0 Bace1 2.5379 4.6695 0 0.0005 Prnp 2.536 4.1436 0.0002 0.0033 Hist1h4f 2.5304 5.6081 0 0 Dmkn 2.5284 3.0276 0 0.0009 Mcm5 2.5199 4.4145 0 0 Dlg5 2.5199 4.4022 0.0004 0.0059 Map3k12 2.5124 3.247 0 0.0002 Ccr5 2.5101 6.7221 0 0 C1qb 2.5001 7.3935 0 0

Genes downregulated in MacB2-3wk vs MacB2-N Symbol logFC logCPM PValue FDR Fgfr4 -7.4816 0.315 0.0001 0.0012 Hba-a2 -6.8118 2.2316 0.0001 0.0021 Cyp2a5 -6.0149 1.3373 0 0.0008 Clic3 -4.8134 2.6302 0 0.0006 Hbb-b1 -4.6941 5.8159 0 0.0003 Hba-a1 -4.6325 2.389 0.0006 0.0086 Cyp2f2 -4.2497 6.6145 0 0 Tmem100 -4.2128 4.883 0 0 Ctla2a -4.1046 2.2357 0.0006 0.0094 S1pr5 -3.8802 4.9019 0 0 Fmo1 -3.8725 5.1349 0 0 Hbb-b2 -3.7486 3.4454 0.0017 0.0221 Cyp4b1 -3.706 4.5348 0 0 Aldh1a1 -3.6927 3.8258 0 0 Cd300e -3.6835 4.2289 0 0 Gsta3 -3.6632 2.1536 0.0011 0.015 Palmd -3.6036 3.1418 0 0 Cldn18 -3.5576 4.972 0 0.0002 Gjb2 -3.5159 3.0034 0 0 Sec14l3 -3.4423 5.1126 0 0 Adrb1 -3.4246 3.2576 0 0 Bmp6 -3.4219 4.2144 0 0 Scgb1a1 -3.3968 10.1718 0 0.0001 Nebl -3.3959 3.3866 0 0.0002 Sstr4 -3.3767 1.5723 0 0.0002 Nckap5 -3.371 3.8381 0 0 Tspan7 -3.3613 5.8693 0 0.0001 Wnt11 -3.3221 4.3068 0 0 Epas1 -3.3204 9.2396 0 0 Scn7a -3.2784 5.6621 0 0 Cyyr1 -3.2766 5.3347 0 0 Tek -3.2197 4.1679 0 0.0001 Mapt -3.1845 3.6797 0 0.0005 Sema3g -3.1685 4.2663 0 0.0001 Myct1 -3.1514 3.7146 0 0.0005 Chad -3.1358 2.1933 0.0001 0.0024 Cd55 -3.1286 3.2865 0 0 Myzap -3.1249 2.4638 0 0.0001 Glp1r -3.1199 3.5708 0.0003 0.005 Npnt -3.1125 5.1682 0 0 Ces1d -3.0884 1.4584 0.0014 0.0188 Hpgd -3.0803 7.2134 0 0 Clic5 -3.0738 5.7352 0 0 Fmo2 -3.0641 5.4175 0 0.0001 Tspan18 -3.0561 5.7765 0 0 Clec14a -3.0512 5.1387 0 0.0003 Zbtb16 -3.0351 5.3478 0 0 Scgb3a2 -3.0072 5.176 0.0002 0.0029 Pcp4l1 -2.9902 3.1681 0 0 Inmt -2.9821 5.0637 0 0.0002 Sema3c -2.9523 5.3956 0 0.0001 Cldn5 -2.9327 4.928 0 0.0001 Il27 -2.9292 1.3098 0.002 0.0255 Cox4i2 -2.9263 2.1329 0.001 0.0144 Arl4d -2.9207 2.5749 0.0002 0.004 Gpr116 -2.9109 7.5155 0 0 Aox3 -2.892 1.6999 0.0002 0.0031 Hs3st1 -2.8624 2.7624 0.0006 0.0088 Cadm3 -2.8483 2.3794 0 0 Limch1 -2.8423 4.9258 0 0 Hmcn1 -2.8315 4.3928 0 0.0002 Ager -2.8198 4.0395 0.0006 0.009 Slc46a3 -2.801 4.5848 0 0 Itga8 -2.7967 3.9672 0 0.0001 Prrg1 -2.7904 1.9817 0.0005 0.0081 Akap5 -2.7868 4.6067 0 0 Gucy1a3 -2.77 4.2983 0.0003 0.0046 Sftpa1 -2.7569 4.7897 0 0.0009 Rem1 -2.7521 1.9231 0 0.0003 Clec4b1 -2.7349 1.1989 0 0.0004 Skint3 -2.734 4.6554 0 0 Prx -2.7337 4.5468 0 0.0004 Emr4 -2.73 6.7307 0 0 Myh14 -2.7224 3.0291 0.0001 0.0019 Ace -2.6815 9.7759 0 0 Cited4 -2.6801 3.3046 0 0.0003 Scnn1a -2.6632 5.4611 0 0 Faim3 -2.6531 2.049 0.0002 0.0035 Spn -2.648 8.2636 0 0 Cxcl15 -2.6344 4.22 0.0003 0.0045 Rnf144a -2.6338 5.441 0 0 Pglyrp1 -2.6266 5.7248 0 0 Aff3 -2.6261 3.3703 0.0002 0.0034 Cers4 -2.6125 2.8405 0.0005 0.0077 Rasgrf2 -2.6106 3.2165 0 0 Prelp -2.605 6.4873 0 0 Eltd1 -2.6022 4.5243 0.0003 0.0057 Tmem204 -2.6006 4.0515 0.0002 0.0037 Postn -2.5944 3.6 0.0003 0.0052 Ccm2l -2.5867 2.8201 0.0025 0.03 Ppp1r16b -2.5767 4.9033 0.0001 0.0014 Foxf1 -2.5701 4.3097 0.0001 0.002 Tbc1d8 -2.5672 6.5356 0 0 Gimap4 -2.5454 4.2116 0.0004 0.0066 Nrarp -2.5424 4.3359 0 0 Ptprb -2.5103 7.1286 0 0.0002 Ldlrad3 -2.5044 7.6523 0 0