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Regulation of Inflammatory Phenotype in Macrophages by a

Diabetes-Induced Long Non-coding RNA

Marpadga A. Reddy1*, Zhuo Chen

1, Jung Tak Park

1,2, Mei Wang

1, Linda Lanting

1, Qiang

Zhang1, Kirti Bhatt

1, Amy Leung

1, Xiwei Wu

1, Sumanth Putta

1, Pål Sætrom

3, Sridevi

Devaraj4 and Rama Natarajan

1*

1 Department of Diabetes and Division of Molecular Diabetes Research, Beckman Research

Institute of City of Hope, Duarte, CA. 2Current address: Department of Internal Medicine,

College of Medicine, Yonsei University, Seoul, Korea. 3Departments of Computer and

Information Science, Cancer Research and Molecular Medicine, Norwegian University of

Science and Technology, Trondheim, Norway (P.S.)., 4Department of Pathology and

Immunology, Baylor College of Medicine and Texas Children's Hospital, Houston, TX.

Running Title: Diabetes induced lncRNA in macrophages

*Correspondence to:

Rama Natarajan, PhD (Email: [email protected])

Marpadga A. Reddy, PhD (Email: [email protected])

Department of Diabetes, Beckman Research Institute of the City of Hope

1500 East Duarte Road, Duarte, CA 91010

Tel. 626-256-4673; Fax: 626-301-8136

Word Count: Abstract -200; Introduction through Discussion - 4943

Number of Figures: 6. Tables: 1

Page 1 of 57 Diabetes

Diabetes Publish Ahead of Print, published online July 9, 2014

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ABSTRACT

The mechanisms by which macrophages mediate the enhanced inflammation associated with

diabetes complications are not completely understood. We used RNA-seq to profile the

transcriptome of bone marrow macrophages isolated from diabetic db/db mice and identified

1648 differentially expressed genes compared to control db/+ mice. Data analyses revealed that

diabetes promoted a pro-inflammatory, pro-fibrotic and dysfunctional alternatively activated

macrophage phenotype possibly via transcription factors involved in macrophage function.

Notably, diabetes altered levels of several long non-coding RNAs (lncRNAs). Because, the role

of lncRNAs in diabetic complications is unknown, we further characterized the function of

lncRNA E330013P06 which was up-regulated in macrophages from db/db and diet-induced

insulin resistant type-2 diabetic mice, but not from type-1 diabetic mice. It was also up-regulated

in monocytes from type-2 diabetes patients. E330013P06 was also increased along with

inflammatory genes, in mouse macrophages treated with high glucose and palmitic acid.

E330013P06 overexpression in macrophages induced inflammatory genes, enhanced responses

to inflammatory signals, and increased foam cell formation. In contrast, siRNA-mediated

E330013P06 gene silencing inhibited inflammatory genes induced by the diabetic stimuli. These

results define the diabetic macrophage transcriptome, and novel functional roles for lncRNAs in

macrophages that could lead to lncRNA based therapies for inflammatory diabetic

complications.

Key Words: long noncoding RNA, macrophage, diabetes, inflammation.

Page 2 of 57Diabetes

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INTRODUCTION

Macrophages play important roles in the regulation of physiological and pathological

processes including metabolism, inflammation, pathogen response, tissue damage and repair.

Differentiation and polarization of macrophages into classically activated M1 phenotype or

alternatively activated M2 phenotype are essential for the plasticity needed to adapt cellular

functions in dynamic microenvironments of target tissues and disease states (1,2). In general,

M1 macrophages are involved in host defense and inflammation, and M2 macrophages in tissue

repair. They exhibit distinct gene profiles that are regulated by specific signaling cascades,

transcription factors and epigenetic factors (3,4). Recent studies have also demonstrated key

roles for microRNAs (miRNAs), such as miR-155 and miR-146a, in regulating macrophage

phenotype (5). Dysregulation of macrophage polarization and function plays a central role in the

development of several diseases including diabetes and atherosclerosis (6,7). Increased

infiltration of macrophages into various tissues including adipose, kidney, heart and vascular

tissues is observed in these disorders (8-11). Diabetes and metabolic disorders can regulate

macrophage polarization and alter the expression of genes involved in macrophage functions

such as inflammation, phagocytosis, cholesterol transport and wound healing (2,11-17).

Production of pro-inflammatory mediators by macrophages infiltration into target tissues

activates other cell types and further enhances inflammation (6,9,18,19) and this is associated

with majority of the complications of diabetes. However, a systematic analysis of how diabetes

regulates global coding and non-coding gene expression programs that modulate macrophage

phenotype linked to diabetic complications is not available.

Recent studies using high throughput RNA-sequencing (RNA-seq) and profiling of key

histone modifications have identified thousands of long non-coding RNAs (lncRNAs), once


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considered to be transcriptional noise, and also demonstrated that they play key roles in

epigenetic mechanisms and gene regulation (20,21). LncRNAs, typically ncRNAs >200

nucleotides long, can be expressed from intergenic regions, antisense strands or introns of

protein coding genes, or they can be derived by alternate splicing. Major features of lncRNAs

include their low expression and ability to form secondary structures that can act as DNA, RNA

and protein binding domains. Several different mechanisms are used by lncRNAs to regulate

gene expression at both transcriptional and posttranscriptional levels. Thus they can act as

signals to recruit or as decoys to titrate out transcription factors, as enhancers to guide chromatin

modifying enzymes to their genomic target sites, and as competitors that inhibit miRNA binding

at the 3’-UTRs of target genes (20). Furthermore, some lncRNAs can serve as host genes for

miRNAs (short ncRNAs) (22), and thereby downregulate multiple targets of these miRNAs

involved in diverse biological processes. Current estimates predict there are thousands of

lncRNAs in the human genome but to date only a few have been well characterized or their

functions identified. Some lncRNAs have recently been linked to various diseases, suggesting

their potential to be novel therapeutic targets (23). One report identified lncRNAs in human

pancreatic islets, including some affected by type 2 diabetes (T2D) (24). However, their role in

macrophages related to diabetic inflammatory complications is not yet known.

We wished to explore the roles of differentially expressed coding transcripts as well as

lncRNAs in the molecular mechanisms involved in impaired macrophage function in diabetes.

Transciptome profiling of bone marrow-derived macrophages (BMM) from diabetic db/db mice

using RNA-seq followed by bioinformatics analyses revealed that diabetes induced a

proinflammatory, dysfunctional polarization and pro-fibrotic phenotype, and also altered

expression of several lncRNAs compared to non-diabetic genetic control db/+. Further


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examination of one of the differentially expressed lncRNAs, E330013P06 (hereafter referred to

as E33) showed that it is significantly upregulated in vivo and in vitro in macrophages under

T2D conditions. Furthermore, we found that E33 regulates pro-inflammatory gene expression

and foam cell formation in macrophages, establishing a novel new functional role for a lncRNA

in processes linked to diabetic inflammatory vascular complications.

RESEARCH DESIGN AND METHODS

Animal experiments: Experiments were performed with male mice (from Jackson Laboratory,

Bar Harbor, ME) using protocols approved by our Institutional Animal Care and Use Committee

in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory

Animals. Macrophages were prepared from 10-12 weeks old T2D db/db mice (BKS.Cg-

m+/+

leprdb

/J) and age matched control littermates db/+ mice (16), as well as from Type1 (T1D)

and T2D C57BL/6J mouse models and respective controls. T1D was induced in by injecting

C57BL/6J mice with streptozotocin (50 mg/kg) for five consecutive days (25). Macrophages

were isolated four weeks post diabetes. In diet-induced T2D models, C57BL/6J mice (8 weeks

old) were fed with high fat diet (60% kcal, Research Diets, Inc., D12492i) for four weeks and

then injected with a single dose of streptozotocin (100 mg/kg) to make them T2D (HFSZ), or

with vehicle citrate buffer (HF). Mice maintained on chow diet were also injected with vehicle

(SF) as a control group. Macrophages were prepared four or more weeks after streptozotocin

injections when HFSZ mice exhibited significantly increased blood glucose and insulin

resistance. Insulin resistance was determined by glucose tolerance tests (GTTs) in mice fasted for

about 16 hrs. Mice were weighed, baseline blood glucose determined and injected intra-


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peritoneally with glucose solution (2g/kg). Blood glucose levels were determined at different

time intervals up to 2h using AlphaTrak glucometer (Abbott Laboratories, Abbott Park, IL).

Isolation, culture and treatment of mouse macrophages: Bone marrow macrophages were

prepared using published protocols with some modifications (26). Briefly, bone marrow was

expelled from femurs and tibia, and after lysis of red blood cells they were plated in Complete

medium (CM) consisting of DMEM containing 5.5 mmol/L glucose, 10% FBS, 2 mmol/L

Glutamine, Pen/Strep antibiotics, 50 µmol/L β-mercaptoethanol and 10 mmol/L HEPES, pH 7.4

supplemented with either 10 ng/ml of macrophage colony stimulating factor-1 (M-CSF) (R&D

Systems, Minneapolis MN) or 20 ng/ml of granulocyte macrophage stimulating factor (GM-

CSF) (Peprotech, Rocky Hill, NJ). Cells were allowed to differentiate for 7-8 days before further

use. Thioglycollate elicited peritoneal macrophages (PMs) were isolated as described (27). The

mouse macrophage cell line RAW264.7 (from ATCC) was used in some experiments and

maintained in CM. Where indicated, macrophages were treated with media containing normal

(5.5 mmol/L) glucose (NG), high (25 mmol/L) glucose (HG), 200 umol/L palmitic acid (PA),

HG+PA (HP), TNF-α (10 ng/ml) or LPS (100 ng/ml). PA stock solutions (10 mmol/L) were

prepared in BSA (28), diluted to the indicated concentration in the medium, incubated for 30 min

at 37oC and filtered with 0.2 um filter before adding to the cells.

Isolation of Human Monocytes: T2D patients and healthy controls (age and gender matched)

were recruited at the Baylor College of Medicine (Table 1). Following informed consent, fasting

blood (15 ml) was obtained from these subjects and monocytes isolated following Ficoll density


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gradient and negative magnetic separation (Miltenyi Biotec, San Diego, CA) as described (29).

More than 88% of cells were CD14 positive, and viability was >92%.

RNA isolation and quantitative reverse transcriptase PCR (RT-QPCR): RNA was extracted

using miRNeasy columns (Qiagen, Valencia, CA) from mouse macrophages and mirVana kit

(Ambion, Austin TX) from human monocytes, and cDNA synthesis performed with 0.5-1.0 ug

RNA using Quantitect RT kit (Qiagen). Expression of genes, including lncRNAs E33 and

MIR143HG (human equivalent of E33) was analyzed by quantitative Realtime PCR (QPCR)

using SYBR green reagents (Life Technologies, Carlsbad, CA) or Taqman assays (Integrated

DNA Technologies, Coralville, IA) on an ABI 7500 instrument using indicated gene primers

(Table S2). MiRNA expression was determined by RT-QPCR using miScript (Qiagen) or qScript

(Quanta Biosciences, Gaithersburg, MD) kits. RT-QPCR data was analyzed by the 2^-∆∆Ct

method, normalized against internal controls (Actb or Ppia for mRNA and lncRNA, and U6 or

18S RNA for miRNA). Results were expressed as fold over control samples.

RNA-seq and data analysis: Whole transcriptome analysis of RNA from BMMC and BMGM

was performed using total RNA depleted of Ribosomal RNA (RiboMinus kit,Life Technologies,

Carlsbad, CA). Paired End libraries were prepared, size selected, gel purified and sequenced in

our Integrative Genomics Corein an Illumina HiSeq2000 system following manufacturer’s

protocols (Illumina Inc, San Diego, CA). Reads were mapped to the mouse genome assembly

mm9 using Tophat software (30). Each RefSeq gene’s expression was summarized and

normalized by the trimmed mean of M-value (TMM) method using EdgeR bioconductor R

package. The total reads for each gene were then scaled by exon length to obtain average


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coverage. Differentially expressed genes were identified with coverage (>=1 in at least one

sample) and fold change (>=1.5). Novel transcripts were identified using published approaches

(21,31-33) including ours (22). Specifically, after de-novo assembly of all the transcripts using

Cufflinks (34), transcripts which mapped to RefSeq genes of mouse and seven other organisms

(human, chimp, rat, rabbit, orangutan, rhesus, marmoset) were first filtered out. Filtered

transcripts were further assessed for potential open reading frames by using PhyloCSF and

searching PfamA/B databases. Multi-exonic transcripts with a score of >100, or that encoded

PfamA/B protein domains were classified as novel protein-encoding transcripts. Transcripts of at

least 200bp in length containing at least two exons, having a PhyloCSFscore of <100 and lacking

PfamA/B domains, were defined as novel lncRNAs. Biological functions and network analysis

of differentially expressed genes were performed using DAVID gene ontology and Ingenuity

Pathway Analysis (IPA)®

. Transcription factor motif analysis and enrichment were performed

using Transcription Factor Affinity Prediction (TRAP) method (35) and Gene Set Enrichment

Analysis (GSEA) respectively.

E330013P06 cloning and lentiviral transduction of macrophages: E330013P06 cDNA was

cloned downstream of the CMV promoter in the lentiviral expression vector pEZ-LV105

(Genecopea, Inc, Rockville, MD) and verified by DNA sequencing. Plasmid pEZ-LV105

expressing EGFP (LVGFP) was used as vector control. HEK293T cells were co-transfected with

lentiviral vector plasmids and three packaging plasmids using Fugene (Promega, Madison, WI).

Cell culture supernatants containing lentiviruses were collected 48-96 h post transfection and

concentrated using Lenti-X Concentrator (Clontech, Mountain View, CA). RAW264.7


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macrophages were transduced with the indicated lentiviral vectors in the presence of Polybrene

(8 ug/ml), and cells resistant to Puromycin (2 ug/ml) were selected for further analysis.

Transfection of macrophages: Macrophages were detached by incubating with Accutase

(eBioscience, San Diego, CA) for 5-10 min at 370C, washed with PBS and resuspended at a

concentration of 2.5-3.0 X 107/ml in Nucleofection reagent (Lonza, Walkersville, MD). Then,

100 ul of cell suspension was mixed with plasmid DNA or siRNA oligonucleotides (up to 6 ul)

and transfected using Nucleofector program Y-001. Transfected cells were plated in dishes for

RNA and foam cell formation assays. Specific siRNAs targeting mouse E33 were designed

using GPboost algorithm (36).

Foam cell formation assays: Macrophages were incubated in eight well chamber slides with

Acetylated Low Density Lipoprotein fluorescently labeled with 20 ug/ml 1,1'-dioctadecyl–

3,3,3',3'-tetramethyl-indocarbo-cyanine perchlorate (DiI-Ac-LDL, Biomedical Technologies,

Stoughton, MA) for 4h at 37oC. Cells were washed twice, fixed with 4% para-formaldehyde,

washed, stained with HOECHST dye and mounted on slides using Vectashield. Fluorescent

images of the cells were collected with Pixera 600 digital camera attached to an Olympus BX51

microscope using InStudio software (Pixera Corporation).

Statistical analysis: Statistical significance was calculated using unpaired Student t-tests to

compare two groups and ANOVA to compare multiple groups followed by appropriate multiple

comparison tests selected by PRISM 6.0 software (GraphPad, San Diego, CA). P values ≤0.05

were considered statistically significant.


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Data Deposition: The RNA-seq data has been deposited in the Gene Expression Omnibus

(GEO) database under accession code GSE54154.

RESULTS

Bone marrow macrophages (BMM) from diabetic db/db mice have a dysfunctional

phenotype: To examine the effects of diabetes on macrophage polarization, we differentiated

BMM from T2D db/db and non-diabetic control db/+ mice in vitro using GM-CSF (BMGM) or

M-CSF (BMMC) to obtain M1 and M2 macrophages respectively and analyzed the expression of

key macrophage markers by RT-QPCR (Fig-S1A-B, Online Supplemental Data). We then

observed that, in BMMC from db/db mice (db/dbBMMC) (M2), the expression of Il10, Tgfb1

and Arg1 (M2 associated genes) was inhibited, but expression of proinflammatory prostaglandin-

endoperoxide synthase 2 (Ptgs2) gene was increased compared to db/+BMMC, indicating that

diabetes induced a dysfunctional M2 phenotype (Fig. 1A). On the other hand, db/dbBMGM

(M1) exhibited increased expression of the pro-inflammatory genes Ccl2, Nos2, Ptgs2 and Tnf

compared to db/+BMGM (Fig. 1B), suggesting enhanced pro-inflammatory M1 phenotype.

Furthermore, db/dbBMMC also exhibited enhanced expression of inflammatory genes relative to

db/+BMMC in response to TNF-α (10 ng/ml) stimulation (Fig. 1C). We also analyzed pro-

inflammatory genes in thioglycollate-elicited peritoneal macrophages (PM) derived from these

mice. The expression of pro-inflammatory genes (Tnf, Il6, Nos2, Ccl2 and Ptgs2) was increased

and anti-inflammatory gene (Il10) was decreased in PM from db/db mice compared to db/+ (Fig.

1D), providing additional in vivo relevance. Overall, our data indicate that diabetes promoted a

dysfunctional M2 phenotype and enhanced M1 inflammatory response in macrophages.


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Diabetes impacts the transcriptome of bone marrow derived macrophages. Prior studies

using gene arrays have revealed differences between the transcriptome of BMMC and BMGM of

normal mice (37) but the impact of diabetes on the macrophage transcriptome genome-wide and

in particular on lncRNAs is unknown. We therefore next used RNA-seq to analyze differences in

global gene expression patterns in BMMs from db/db and db/+mice. RNA-seq data (Table S1)

was analyzed using an in-house data analysis pipeline and publicly available databases (Fig. 2A).

Results showed that the BMGM transcriptome in both db/+ and db/db mice was enriched with

pro-inflammatory genes (Fig. S2A), whereas genes known to be associated with alternatively

activated macrophages were elevated in BMMC (Fig. S2B), further confirming the M1 and M2

phenotype of BMGM and BMMC, respectively (37). RNA-seq data also revealed that levels of

pro-inflammatory markers (Itgax and Ly6c) were increased in BMGM (Fig. S2A), whereas

macrophage specific markers (Emr1 and Mrc1) were elevated in BMMC relative to BMGM (Fig.

S2B). Furthermore, Csf1and Csf1r genes were elevated, and GM-CSF receptors (Csf2ra, Csf2rb

and Csf2rb2) were reduced in BMMC compared to BMGM, indicating inhibition of GM-CSF

signaling in BMMC as anticipated (Fig. S2C). Flow cytometry showed significant inhibition of

F4/80 in BMGM (Fig. S3A) confirming RNA-seq results that GM-CSF inhibited macrophage

phenotype.

Interestingly, when we next compared db/+ and db/db macrophages, we observed that the

levels of 1648 transcripts including 1556 RefSeq genes (Fig. 2B) were altered in db/dbBMMC

compared to db/+BMMC indicating diabetes profoundly affected the BMMC transcriptome. In

contrast, relatively few genes were altered in db/dbBMGM relative to db/+BMGM (Fig. S4).

Further analysis of all the differentially expressed genes (DEG) in db/dbBMMC included both


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known and novel coding as well as long non-coding transcripts (Fig. 2C). Differentially-

expressed RefSeq genes included growth factors, fibrotic genes, proteases and adhesion

molecules involved in extracellular matrix (ECM) remodeling, chemokines, cytokines, and

neuroimmune signaling and lipid metabolism (GEO accession code GSE54154). Most notably,

db/dbBMMC exhibited a strong pro-fibrotic phenotype relative to db/+BMMC with marked

upregulation of several pro-fibrotic growth factors including Ctgf, Tgfb and fibrotic/ECM genes

including numerous Collagens (Fig. 2D). DAVID gene ontology analysis (Fig. 2E-F) and

Ingenuity Pathway analysis (Fig. 2G and S5) of DEGs also confirmed marked enrichment of

signaling pathways relevant to fibrosis (including TGF-β signaling), inflammation, ECM

remodeling, adhesion and growth factor/cytokine signaling and cardiovascular diseases among

top 10 categories. Motif analysis of upregulated genes by Transcription factor Affinity Prediction

(TRAP) showed that binding sites for SP1, Klf4, Egr1, MZF1_5-13 and RREB1 transcription

factors (TFs) were enriched in proximal promoters of DEG [(-250 to +50 bp relative to

transcription start site (TSS)] (Fig. 2H). In addition, GSEA also showed significant enrichment

of SRF motif in upregulated gene promoters (±2 kb flanking TSS) (Fig. S6). Furthermore, IPA

indicated that activities of several TFs including TP53, Smad3 and SRF were increased whereas

others such as Hoxa10 were inhibited (Fig. 2I). These results suggest that diabetes alters multiple

transcription programs and skews macrophages towards a pro-inflammatory and pro-fibrotic

phenotype that can contribute to vascular disorders. They also reveal a novel pro-fibrotic and

dysfunctional M2 phenotype in diabetic macrophages.

Diabetes induces differential expression of lncRNAs in macrophages: Bioinformatics

analyses of our RNA-seq data (Fig. 3A) identified several known and novel lncRNAs expressed


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in BMMC. Of these, only 156 known (RefSeq) ncRNAs including 119 lncRNAs (≥200bp in

length) and 52 novel lncRNAs were differentially expressed in db/db relative to db/+BMMC

(Fig. 2C). The fold changes of some of these lncRNAs are depicted in Figs. 3B and 3D

(complete list shown in Tables S3-S4). Some of the differentially expressed lncRNAs such as

E330013P06 (E33) are host genes for miRNAs (Fig. 3F). Because lncRNAs can regulate nearby

genes, we also analyzed genes located within about 500 kb from the lncRNAs differentially

expressed in db/dbBMMC. This revealed that expression of some nearby genes was also

affected, suggesting their co-regulation with lncRNAs in db/dbBMMC (Fig. 3C and 3E, and

Tables S2 and S3). Interestingly, IPA indicated that these nearby genes have functions related to

inflammation and endocrine disorders (Fig. 3G). These results demonstrate for the first time that

diabetes can alter the expression of lncRNAs in macrophages, which in turn might affect the

expression of genes involved in macrophage function.

Upregulation of lncRNA E330013P06 in macrophages from diabetic mice: Next, we further

characterized lncRNA E33, which was significantly up-regulated (> 2 fold) in db/dbBMMC.

Most of the other lncRNAs were either expressed in lower amounts, and/or difficult to

clone/express due to their large sizes. Furthermore, mouse E33 genomic organization is similar

to the human gene (MIR143HG) and is a host gene for the miRNAs miR-143 and miR-145 (Fig.

3F), that are implicated in cancer, vascular disease and insulin resistance (38-40). Moreover, the

functions of E33 lncRNA have not been examined in any cell or disease type, including

macrophages under diabetic conditions.

Using RT-QPCR, we first verified that expression of E33 was significantly increased in

db/dbBMMC relative to db/+ (Fig. 4A). Next, we examined its expression in PMs from db/+ and


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db/db mice which showed that E33 was significantly increased in PM from db/db mice compared

to db/+ further establishing in vivo relevance (Fig. 4B). Because E33 is a host gene for miR-143

and miR-145, we also examined their expression. Levels of miR-143 (Fig. 4C), but not miR-145

(Fig. 4D), were upregulated in db/dbBMMC, indicating these miRNAs may be post-

transcriptionally regulated at the level of biogenesis. Recent studies showed that the combination

of high fat (HF) diet and single streptozotocin (SZ) administration can induce insulin resistance

and hyperglycemia mimicking T2D (41). We used this mouse model of diet-induced T2D

(HFSZ) to verify that observed increases in E33 in db/db versus db/+ mice were not due to

genetic differences. HFSZ mice exhibited characteristic features of T2D including increased

body weight (Fig. 4E), blood glucose (Fig. 4F) and insulin resistance (Fig. 4G) compared with

standard diet fed mice (SF). Expression of inflammatory genes Il6, Nos2 and Ptgs2 was

significantly increased in BMMC from HF and HFSZ mice (Fig. 4H-J). Furthermore, E33

expression was significantly increased in both BMMC (Fig. 4K) and PM (Fig. 4L) derived from

the HFSZ mice compared to SF, as well as in PM from HF mice (Fig. 4L). Increased levels of

E33 in the PMs of these T2D mice further supports the in vivo relevance.

On the other hand, examination of E33 levels in PMs derived from SZ-induced type 1

diabetes (T1D) mice revealed no significant changes macrophages from T1D mice (SZ) versus

controls (NS) (Fig.4M). These results demonstrate that insulin resistance and T2D can induce

E33 expression in macrophages. In order to further determine the relevance to human diabetes,

we determined the levels of MIR143HG (RefSeq NR_105060.1), the human equivalent of E33,

in monocytes from T2D patients. Results showed significantly increased expression of

MIR143HG in monocytes from T2D patients versus healthy controls (Fig. 4N).


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Sustained overexpression of E33 increases pro-inflammatory and pro-atherogenic gene

expression in RAW macrophages: We next determined the functional relevance of E33

upregulation by using RAW 264.7 (RAW) macrophages stably transduced with lentiviral vectors

that express full length mouse E33 mRNA under the control of a CMV promoter (LVE33). RAW

cells transduced with GFP (LVGFP) were used as control. E33 expression was significantly

increased in LVE33-transduced RAW cells relative to LVGFP (Fig. 5A). E33 overexpression in

RAW cells did not affect cell morphology or growth (data not shown) but clearly altered several

inflammatory genes. The anti-inflammatory gene Il10 (Fig. 5B) was downregulated whereas

several pro-inflammatory genes were upregulated (Fig. 5C-F). The chemokine Ccl2 was

inhibited (Fig. 5G), but expression of the pro-atherogenic scavenger receptor Cd36 (Fig. 5H) was

increased by E33 overexpression. We examined if E33 also modulated macrophage responses to

pro-inflammatory signals. LPS (100 ng/ml) dramatically induced the expression of pro-

inflammatory genes in LVGFP as expected. But, the expression of Il6, Tnf, Ptgs2 and Ccl2 was

further augmented in LVE33 cells treated with LPS (Fig. 5I-L), whereas Il10, Nos2 and Cd36

were not altered (Fig. 5M-O). Furthermore, transient overexpression of E33 in BMMC from

normal C57BL/6 mice also increased the expression of inflammatory genes confirming similar

pro-inflammatory effects in primary mouse macrophages (Fig. S7). Overall the data

demonstrates that E33 overexpression can upregulate pro-inflammatory and pro-atherogenic

genes and also enhance their response to pro-inflammatory signals in macrophages.

E33 overexpression in macrophages promotes the foam cell formation: Having shown that

E33 upregulates Cd36 expression, we next examined if this is functionally associated with

increased uptake of modified LDL. As shown in Fig. 5P, Dil-Ac-LDL uptake was markedly


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increased in LVE33 (Panel b) compared to LVGFP cells (Panel a) suggesting increased foam cell

formation. Nuclear staining with HOECHST confirmed similar numbers of cells in both fields

(Panels c and d). Overall, these data reveal a functional role for E33 in enhancing macrophage

uptake of modified LDL, a key event in foam cell formation and atherosclerosis.

Regulation of E33 and inflammatory genes by HG and palmitic acid (PA): Hyperglycemia,

insulin resistance and dyslipidemia, evident in our mouse models in which E33 was upregulated,

are key factors involved in diabetes and its complications. Therefore, we examined if HG and

free fatty acids such as PA, which is greatly increased in T2D, could regulate E33 expression in

macrophages. E33 and key inflammatory genes were examined in db/+BMMC treated with HG

(25 mmol/L), PA (200 umol/LM ) or HG+PA (HP) for four days. Results showed that HP

treatment significantly increased E33 expression in db/+BMMC (Fig. 6A). Furthermore, HG and

PA also increased the expression of several inflammatory genes (Fig. 6B-F) but showed

differences in the patterns of regulation of Cd36 and Nos2. HG inhibited Nos2 expression,

whereas PA inhibited Cd36 expression. These results indicate that HG and PA can regulate

common genes, including E33, but may impact the expression of others differently. HP also

increased E33 expression in BMMC of normal C57BL/6 mice (2.84±0.47SEM fold vs NG, n=2),

suggesting it is not specific to only db/+BMMC.

Silencing E33 in db/db macrophages inhibits inflammatory genes induced by HG and PA:

Next, to test the consequences of silencing E33, we transfected db/dbBMMC with siRNA

oligonucleotides containing a non-targeting control (siNTC) or a mixture of three siRNA

oligonucleotides targeting E33 (siE33). Three days post transfection the cells were treated with


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HP for 24 h and gene expression analyzed. Results showed that siE33 significantly

downregulated basal and HP induced E33 expression (Fig. 6G). E33 gene silencing did not affect

basal inflammatory gene expression but, significantly inhibited the expression of key

inflammatory genes and Cd36 induced by HP (Fig. 6H-L). Together these new results

demonstrate that E33 lncRNA is upregulated under diabetic conditions and contributes to the

enhanced macrophage inflammatory and atherogenic phenotype and functions. Knockdown of

E33 can reverse the diabetic phenotype, at least in part, especially under stimulated conditions.

DISCUSSION

Macrophage differentiation and polarization are associated with the production of pro-

inflammatory and pro-atherogenic factors. These processes are dysregulated in diabetes and can

contribute to enhanced atherosclerosis. In this study, using RNA-seq, we showed that diabetes

skewed the macrophage transcriptome towards an increased pro-inflammatory and pro-fibrotic

state, and a dysfunctional, alternatively activated phenotype. These changes were associated with

alterations in networks of genes that regulate inflammation and fibrosis. Notably, we observed

for the first time that macrophage lncRNA expression levels were also altered. Furthermore, T2D

was associated with the increased expression of lncRNA E33, which upregulated the expression

of pro-inflammatory genes, and increased lipid update in macrophages (foam cell formation).

Moreover, E33 was upregulated in vitro under metabolically challenged and diabetic conditions

(HG and PA), but silencing its expression prevented increased expression of inflammatory genes.

These results demonstrate that E33 contributes to a pro-inflammatory macrophage phenotype

thereby uncovering a lncRNA with a novel functional role in diabetic macrophages.


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Imbalances between the M1 and M2 functions of macrophages can result in chronic

inflammation and tissue damage (1,2). Our RNA-seq results indicated diabetes had a profound

effect on the macrophage transcriptome by enhancing pro-inflammatory genes and inhibiting

genes associated with alternatively activated M2 phenotype. Several pro-fibrotic genes and

related TFs were upregulated, which may affect various target tissues related to complications,

and also lead to adipose tissue remodeling, systemic inflammation, insulin resistance, wound

healing defects, and atherosclerotic plaque formation/instability. db/dbBMMC also had lower

levels of Nos2, a key enzyme required for phagocytosis (42), suggesting impaired host defense.

Furthermore, Csf1 expression was enhanced in db/dbBMMC, which may promote monocyte-

VSMC interactions and foam cell formation (18) to confer atherogenic phenotypes in diabetes.

CSF-1 signaling through CSF-1R is not only essential for macrophage survival but could

promote macrophage proliferation in atherosclerotic environments (43). Thus, the diabetic milieu

can augment the macrophage inflammatory phenotype and proliferative capacity, which can

exacerbate atherosclerosis. In support of this, recent studies demonstrated monocytosis in

hyperglycemia (44) and macrophage proliferation in atherosclerosis (45). The key anti-

inflammatory and immunomodulatory cytokine Il10 was downregulated in db/dbBMMC, along

with interferon gamma-induced genes such as guanylate binding proteins, further suggesting

macrophages in diabetes have increased inflammatory activities and defective immune

responses. Bioinformatics and in silico modeling revealed that diabetes can affect the function of

key TFs involved in cytoskeletal gene programs (SRF) (46) and macrophage differentiation

(SP1, Egr1 Klf4 and MZF) (3,47). SRF regulates gene expression by interaction with distal

enhancers (46) and our study indicates that this could be affected in diabetes. Overall, the power


19

of RNA-seq reveals enormous changes to the macrophage transcriptome that could lead to

multiple pathologies associated with diabetes.

Our RNA-seq data also revealed that several lncRNAs are differentially expressed in

BMMC from db/db mice compared to db/+. The expression of several genes located within

500kb from differentially expressed lncRNAs was also altered in db/db macrophages, indicating

potential regulation by the lncRNAs. These transcripts included both coding and noncoding

genes, including miRNAs. In addition, bioinformatic analysis indicated that the nearby genes

were involved in inflammation, highlighting the potential importance of gene regulation by

lncRNAs in diabetes. Our hypothesis was that lncRNAs could have a functional role and regulate

genes involved in macrophage dysfunction. We therefore further characterized the function of

lncRNA E33 which was significantly upregulated in db/dbBMMC and has a genomic

organization similar to the human locus. The function and regulation of lncRNA E33 has not

been examined in any cell type or disease condition. We found lncRNA E33 was upregulated in

macrophages from T2D mice, but not T1D mice. In contrast to T1D, T2D is associated with

insulin resistance, hyperglycemia and dyslipidemia, suggesting E33 expression might be affected

by multiple diabetogenic factors including free fatty acids. This was supported by our data

showing that E33 expression was also increased in macrophages from HFSZ and HF mice (Fig.

4K-L) and in macrophages treated with HG+PA (Fig. 6A). Furthermore, MIR143HG expression

was higher in monocytes from T2D patients relative to controls (Fig. 4N), demonstrating

relevance to human disease. Most notably, we found that E33 overexpression significantly

upregulated inflammatory genes and downregulated the anti-inflammatory cytokine, Il10.

Conversely, gene silencing by siRNAs prevented the increased expression of inflammatory genes

induced by HG/PA in db/db macrophages. Interestingly, E33 also upregulated Cd36, a key


20

scavenger receptor involved in oxidized-LDL uptake and foam cell formation. Consistently,

expressing E33 in macrophages greatly increased their LDL uptake, further supporting a key role

for this lncRNA in pro-atherogenic responses of macrophages.

Because E33 is also a host gene for miR-143/145, effects of increased E33 could also be

mediated by these miRNA. However, our E33 expression vector expressed only the exons of

E33 lncRNA, and hence it does not express miR143 or -145, which are located in introns. In

addition, transfection of macrophages with oligonucleotide mimics of miR-143 (which was

increased in db/db mice) did not affect inflammatory genes (data not shown). Therefore, lncRNA

E33 regulates macrophage function independently of the miRNAs that are derived from it.

We also showed that E33 gene silencing with siRNAs significantly inhibited HP induced

inflammatory genes, but not basal levels. HP induced lncRNA E33 might regulate gene

transcription various mechanisms that include modulating recruitment/activity of epigenetic

enzymes to alter chromatin structure and access to transcription factors under insulin resistant

conditions. However, it is known that HG alone can regulate inflammatory genes via activation

of transcription factors such as NF-κB through multiple signaling and epigenetic mechanisms

(48). Furthermore, PA can also regulate gene expression through activation of nuclear receptors,

Toll-Like Receptors and NF-κB to promote inflammation in diabetes (8,49,50). Thus, novel

lncRNA dependent mechanisms identified in our study could fine-tune the inflammatory

phenotype of macrophages in concert with these other pathways activated by the diabetic milieu.

Because E33 was not increased in T1D macrophages although pro-inflammatory genes were

elevated relative to non-diabetic mice (data not shown), E33 dependent mechanisms might

operate only under T2D conditions. Thus, E33, like most non-coding RNAs, may play a fine-


21

tuning modulatory role along with the other known factors, to augment macrophage

inflammatory states and lipid uptake in T2D.

The dysregulation of lncRNA expression and function is increasingly being implicated in

various diseases, and related therapeutic strategies are gradually being explored (23). We have

shown for the first time a role for one diabetes induced lncRNAE33 in macrophage dysfunction,

which suggests that, in the future such lncRNAs can serve as novel therapeutic targets to

attenuate inflammation and atherogenic potential of macrophages and concomitant diabetic

complications.


22

ACKNOWLEDGMENTS

These studies were supported by grants from the National Institutes of Health (R01 DK 065073,

HL106089 and DK058191 to RN). The authors are grateful to Dr. H. Gao and J-H. Wang

(Integrative Genomics Core) for assistance with RNA-seq, L. Brown (Analytical Cytometry

core) for Flow cytometry, Dr. R. Chandran (Department of Virology) for analysis with Flowjo

software, and Dr. M. Morgan (Office of Faculty and Institutional Support) for editing the

manuscript (all from the Beckman Research Institute of City of Hope, Duarte, CA). The authors

declare no conflict of interests.

AUTHOR CONTRIBUTIONS

M. A. R., conceived the idea, designed the experiments, performed all the experiments, analyzed

the data, wrote and edited the manuscript. Z. C. analyzed RNA-seq data, performed

bioinformatics analysis, wrote and edited the manuscript. M. W., L. L., Q. Z. and K. B.

performed experiments and analyzed data. J. T. P. and S. P. assisted with macrophage isolation.

X. W. analyzed RNA-seq data. A. L. assisted with analysis of RNA-seq data. P. S. designed

siRNAs. J. T. P., K. B., A. L., P. S. and S. P. reviewed the manuscript. S. D. provided

monocytes from diabetic patients and controls and reviewed the manuscript. R.N. conceived the

idea, designed the experiments, analyzed the data, wrote and edited the manuscript. M. A. R. and

R. N. are the guarantors of this work, as such, had full access to the data in the study and take

responsibility for the integrity of the data and the accuracy of the data analysis.


23

FIGURE LEGENDS

Figure 1. Diabetes induces dysfunctional macrophage polarization. A-B. Quantitative RT-

PCR (RT-QPCR) analysis of gene expression in bone marrow macrophages derived from

diabetic db/db mice and control db/+ mice , which were differentiated in vitro with either MCSF

(BMMC, panel A) or GMCSF (BMGM, panel B). Bone marrow was isolated from 10-12 weeks

old db/db and db/+ mice. Blood glucose levels were 479±28 mg/dL in db/db mice vs 162±8

mg/dL in db/+ mice. C. TNF-α induced gene expression in db/+ and db/dbBMMCs. BMMC

were serum depleted for 4 hours and stimulated with TNF-α (10 ng/ml) for one hour. D. Gene

expression analysis of thioglycollate-elicited peritoneal macrophages (PM) from db/+ (PMdb/+)

and db/db (PMdb/db) mice Gene expression was analyzed by RT-QPCR and results expressed as

fold over db/+ cells. Data are represented as Mean ± SEM (*,p<0.05, n=3-4).

Figure 2. RNA-Seq analysis of gene expression in macrophages from db/+ and db/db mice.

A. Data analysis Pipeline. B. Scatter plot of differentially expressed RefSeq genes including

small nc RNAs (<200bp). C. Pie chart showing differentially expressed genes (DEG) in

db/dbBMMC relative to db/+BMMC (excluding small nc RNAs< 200bp). D. Bar graph showing

fibrotic genes increased in db/dbBMMC compared to db/+ . Data represents log2 ratio

(db/dbBMMC-db/+BMMC). E. Top 10 Biological processes enriched among DEGs in

db/dbBMMC by DAVID analysis. Enrichment score of an annotation cluster refers to the

geometric mean of EASE scores (modified Fisher Exact P value in form of –log10) of all the

biological processes in the cluster. (F) Top 10 KEGG pathways enriched among DEGs (DAVID

analysis). G. Top 10 envolved diseases enriched in DEG of db/dbBMMC by Ingenuity Pathway

Analysis®

(IPA). In panels F and G, y-axis represents p- values (–log10) from Fisher exact tests

with Benjamni-Hochberg (B-H) adjustments. H. Diagrammatic representation of top five


24

transcription factor motifs enriched in promoters (-250 to +50 bp relative to TSS) of upregulated

genes. TRAP was used to find high affinity transcription factor binding sites enriched in the

promoters (-250 bp to 50 bp relative to transcription start site) of upregulated genes. I. IPA

showing activation of the indicated transcription factors in db/dbBMMC. Vasc-vascular; SKS-

skeletal sytem; Emb-embryonic; ECM-extracellular matrix; AA-amino acid; CV-

Cardiovascular; OIA-Organismal Injury and abnormalities; GI-gastrointestinal; CT-connective

tissue; SKM-skeletal and muscular.

Figure 3. Identification of Differentially expressed lncRNAs in Macrophages from db/db

mice. A. Pipeline used to identify novel lncRNAs in BMMC. B-C. Representative known

(RefSeq) lncRNAs differentially expressed (B) and nearby (±500kb flanking regions)

differentially expressed genes (C) in db/dbBMMC. D-E. Novel differentially-expressed

lncRNAs (>200 bp with at least two exons) (D) and nearby genes with altered expression in

db/dbBMMC (E). F. Schematic showing the genomic structure of lncRNA E330013P06 and the

alignment of RNA-seq reads in db/+BMMC (db/+) and db/dbBMMC (db/db) . Arrow indicates

the direction of transcription. Closed rectangles are exons and open rectangles are miRNAs

expressed from these genomic locations. G. IPA of differentially expressed nearby genes.

Figure 4. Increased Expression of E330013P06 in macrophages from diabetic mice and

monocytes from type 2 diabetic patients A-B. RT-QPCR analysis shows that E33 is

significantly increased in (A) BMMC (n=6) and (B) PM (n=6) from db/db compared to db/+

mice. C-D. RT-QPCR of miR-143 (C) and miR-145 (D) in db/+ and db/dbBMMC (n=5). Gene

expression was analyzed by RT-QPCR and reults expressed as fold over macrophages from db/+

mice. Data are represented as Mean ± SEM (*,p<0.05). E-G. Body weight (E), blood glucose


25

levels (mg/dL) in basal (F) and after intraperitoneal injection of glucose (G) in the control (SF),

high fat fed (HF) and diet induced type2 diabetic (HFSZ) mice. C57BL/6 mice were maintained

on standard (SF) or high fat (HF) diet for 4 weeks. Then half the HF mice were injected with a

single dose of streptozotocin (100 mg/kg) to make them type 2 diabetic (HFSZ). Glucose

tolerance tests (GTTs) were performed 4 weeks later. H-K. Increased expression of inflammatory

genes and E33 in BMMC from HF and HFSZ mice relative to SF mice. L. E33 expression in PM

from indicated mice. BMMC (n=5-7) and PM (n=5-6). Gene expression was analyzed by RT-

QPCR and results expressed as fold over macrophages from SF mice. Data are represented as

Mean ± SEM (*,p<0.05). M. RT-QPCR analysis of PM from streptozotocin induced Type 1

diabetic mice (SZ) showed no changes in E33 levels relative to controls (NS) mice (Mean ±

SEM, n=7-14). Blood glucose levels were 527.1 ±114 mg/dL in SZ (n=14) vs 186.4± 42.6

mg/dL in control NS mice (n=7). N. Increased expression of MIR143HG (human equivalent of

E33) in monocytes from type 2 diabetic (T2D) patients vs healthy controls (Control). RT-QPCR

results were expressed as fold over Control samples. Mean ± SEM (*,p<0.05, n=4 each).

Figure 5. Regulation of inflammatory genes and foam cell formation in RAW macrophages

stably overexpressing E330013P06. A-H. Expression of E33 and the indicated genes in RAW

macrophages that either stably overexpressed lncRNA E33 (LVE33) or a control vector that

expressed GFP (LVGFP). RAWcells were transduced with LVE33 or LVGFP and Puromycin (2

ug/ml) resistant cells were used for further analysis by RT-QPCR. Results are expressed as fold

increase over LVGFP (GFP). I-O. RAW macrophages stably expressing E33 (LVE33) or GFP

(LVGFP) were treated with LPS (100 ng/ml) for 24 hours. Expression of indicated genes was

analyzed by RT-QPCR . Results are expressed as fold over GFP control. Data in A-O represents

Mean ± SEM (*,p<0.05, n=4-9). P. Enhanced foam cell formation in macrophages


26

overexpressing E33. Indicated RAW cells were incubated with Dil-Ac-LDL (panels a-b, Red

fluorescence) and fluorescent images were collected as described in Methods. Blue fluorescence

shows nuclear staining with HOECHST dye.

Figure 6. Regulation of E330013P06 and inflammatory genes in diabetic conditions and its

inhbition by E330013P06 gene silencing in macrophages. A-F. BMMC from db/+ mice were

treated with normal (5.5 mmol/L) glucose (NG), high (25 mmol/L) glucose (HG), 200 umol/L

palmitic acid (PA) or HG+PA (HP) for four days. Levels of E33 (A) and the indicated

inflammatory genes (B-F), as well as Cd36 (F), were determined by RT-QPCR. Results are

expressed as fold over gene expression levels in NG. G-L. BMMC from db/db mice were

transiently transfected with siRNA targeting E33 (siE33) or a non-targeting control (siNTC). 72

hours post transfection BMMC were stimulated with HG+PA (HP) for another 24 hours. The

expression of indicated genes was analyzed by RT-QPCR and results expressed as fold over the

siNTC transfected cells. Data are represented as Mean ± SEM (*, p<0.05, n=3).


27

Table 1. Characteristics of Healthy Controls and T2D patients. For the isolation of human

monocytes, healthy controls (age and gender matched) and Type 2 diabetic patients were

recruited at the Baylor College of Medicine (Table 1). T2D was diagnosed if fasting blood

glucose was > 126 mg/dL on 2 occasions or HbA1c>6.5% (48 mmol/mol) and subjects were

recruited if they were newly diagnosed or on metformin therapy. All subjects had normal

complete blood count, no other chronic diseases; normal kidney, liver, and thyroid function and

no inflammatory disorders. Data represents Mean±SEM. *, p<0.05 vs Control (n=4).

Age

(years)

BMI

(kg/m2)

BG

(mg/dL)

HbA1c

(%)

HbA1c

(mmol/mol)

Control 37.5±3.8 23.9±0.47 89.75±3.3 5.2±0.07 33.3±0.9

T2D 43.25±6.3 26.28±1.1 133.8±12.1* 8.2±0.4

* 66.5±4.3

*


28

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Figure 1. Diabetes induces dysfunctional macrophage polarization. A-B. Quantitative RT-PCR (RT-QPCR) analysis of gene expression in bone marrow macrophages derived from diabetic db/db mice and control db/+ mice , which were differentiated in vitro with either MCSF (BMMC, panel A) or GMCSF (BMGM, panel B). Bone marrow was isolated from 10-12 weeks old db/db and db/+ mice. Blood glucose levels were

479±28 mg/dL in db/db mice vs 162±8 mg/dL in db/+ mice. C. TNF-α induced gene expression in db/+

and db/db BMMCs. BMMC were serum depleted for 4 hours and stimulated with TNF-α (10 ng/ml) for one

hour. D. Gene expression analysis of thioglycollate-elicited peritoneal macrophages (PM) from db/+ (PMdb/+) and db/db (PMdb/db) mice Gene expression was analyzed by RT-QPCR and results expressed as

fold over db/+ cells. Data are represented as Mean ± SEM (*,p<0.05, n=3-4). 85x87mm (300 x 300 DPI)


Figure 2. RNA-Seq analysis of gene expression in macrophages from db/+ and db/db mice. A. Data analysis Pipeline. B. Scatter plot of differentially expressed RefSeq genes including small nc RNAs (<200bp). C. Pie chart showing differentially expressed genes (DEG) in db/dbBMMC relative to db/+BMMC (excluding small nc

RNAs< 200bp). D. Bar graph showing fibrotic genes increased in db/db BMMC compared to db/+ . Data represents log2 ratio (db/dbBMMC-db/+BMMC). E. Top 10 Biological processes enriched among DEGs in db/dbBMMC by DAVID analysis. Enrichment score of an annotation cluster refers to the geometric mean of EASE scores (modified Fisher Exact P value in form of –log10) of all the biological processes in the cluster. (F) Top 10 KEGG pathways enriched among DEGs (DAVID analysis). G. Top 10 envolved diseases enriched in DEG of db/dbBMMC by Ingenuity Pathway Analysis®(IPA). In panels F and G, y-axis represents p- values

(–log10) from Fisher exact tests with Benjamni-Hochberg (B-H) adjustments. H. Diagrammatic representation of top five transcription factor motifs enriched in promoters (-250 to +50 bp relative to TSS) of upregulated genes. TRAP was used to find high affinity transcription factor binding sites enriched in the

promoters (-250 bp to 50 bp relative to transcription start site) of upregulated genes. I. IPA showing activation of the indicated transcription factors in db/dbBMMC. Vasc-vascular; SKS-skeletal sytem;

Emb-embryonic; ECM-extracellular matrix; AA-amino acid; CV-Cardiovascular; OIA-Organismal Injury and abnormalities; GI-gastrointestinal; CT-connective tissue; SKM-skeletal and muscular.

170x161mm (300 x 300 DPI)


Figure 3. Identification of Differentially expressed lncRNAs in Macrophages from db/db mice. A. Pipeline used to identify novel lncRNAs in BMMC. B-C. Representative known (RefSeq) lncRNAs differentially expressed (B)

and nearby (±500kb flanking regions) differentially expressed genes (C) in db/dbBMMC. D-E. Novel

differentially-expressed lncRNAs (>200 bp with at least two exons) (D) and nearby genes with altered expression in db/dbBMMC (E). F. Schematic showing the genomic structure of lncRNA E330013P06 and the alignment of RNA-seq reads in db/+BMMC (db/+) and db/dbBMMC (db/db). Arrow indicates the direction of transcription. Closed rectangles are exons and open rectangles are miRNAs expressed from these genomic

locations. G. IPA of differentially expressed nearby genes. 106x64mm (300 x 300 DPI)


Figure 4. Increased Expression of E330013P06 in macrophages from diabetic mice and monocytes from type 2 diabetic patients A-B. RT-QPCR analysis shows that E33 is significantly increased in (A) BMMC (n=6) and (B) PM (n=6) from db/db compared to db/+ mice. C-D. RT-QPCR of miR-143 (C) and miR-145 (D) in

db/+ and db/dbBMMC (n=5). Gene expression was analyzed by RT-QPCR and reults expressed as fold over macrophages from db/+ mice. Data are represented as Mean ± SEM (*,p<0.05). E-G. Body weight (E), blood glucose levels (mg/dL) in basal (F) and after intraperitoneal injection of glucose (G) in the control (SF), high fat fed (HF) and diet induced type2 diabetic (HFSZ) mice. C57BL/6 mice were maintained on standard (SF) or high fat (HF) diet for 4 weeks. Then half the HF mice were injected with a single dose of streptozotocin (100 mg/kg) to make them type 2 diabetic (HFSZ). Glucose tolerance tests (GTTs) were

performed 4 weeks later. H-K. Increased expression of inflammatory genes and E33 in BMMC from HF and HFSZ mice relative to SF mice. L. E33 expression in PM from indicated mice. BMMC (n=5-7) and PM (n=5-6). Gene expression was analyzed by RT-QPCR and results expressed as fold over macrophages from SF mice. Data are represented as Mean ± SEM (*,p<0.05). M. RT-QPCR analysis of PM from streptozotocin


induced Type 1 diabetic mice (SZ) showed no changes in E33 levels relative to controls (NS) mice (Mean ± SEM, n=7-14). Blood glucose levels were 527.1 ±114 mg/dL in SZ (n=14) vs 186.4± 42.6 mg/dL in control NS mice (n=7). N. Increased expression of MIR143HG (human equivalent of E33) in monocytes from type 2 diabetic (T2D) patients vs healthy controls (Control). RT-QPCR results were expressed as fold over Control

samples. Mean ± SEM (*,p<0.05, n=4 each). 146x250mm (300 x 300 DPI)


Figure 5. Regulation of inflammatory genes and foam cell formation in RAW macrophages stably overexpressing E330013P06. A-H. Expression of E33 and the indicated genes in RAW macrophages that

either stably overexpressed lncRNA E33 (LVE33) or a control vector that expressed GFP (LVGFP). RAWcells

were transduced with LVE33 or LVGFP and Puromycin (2 ug/ml) resistant cells were used for further analysis by RT-QPCR. Results are expressed as fold increase over LVGFP (GFP). I-O. RT-QPCR analysis of indicated genes in RAW macrophages stably expressing E33 (LVE33) or GFP (LVGFP) treated with LPS (100 ng/ml) for 24 hours. Results are expressed as fold over GFP control. Data in A-O represents Mean ± SEM (*,p<0.05, n=4-9). P. Enhanced foam cell formation in macrophages overexpressing E33. Fluorescent images of indicated RAW cells incubated with Dil-Ac-LDL (panels a-b, Red fluorescence). Blue fluorescence shows

nuclear staining with HOECHST dye (panels c-d). 159x213mm (300 x 300 DPI)


Figure 6. Regulation of E330013P06 and inflammatory genes in diabetic conditions and its inhbition by E330013P06 gene silencing in macrophages. A-F. BMMC from db/+ mice were treated with normal (5.5 mmol/L) glucose (NG), high (25 mmol/L) glucose (HG), 200 umol/L palmitic acid (PA) or HG+PA (HP) for

four days. Levels of E33 (A) and the indicated inflammatory genes (B-F), as well as Cd36 (F), were determined by RT-QPCR. Results are expressed as fold over gene expression levels in NG. G-L. BMMC from db/db mice were transiently transfected with siRNA targeting E33 (siE33) or a non-targeting control (siNTC). 72 hours post transfection BMMC were stimulated with HG+PA (HP) for another 24 hours. The expression of indicated genes was analyzed by RT-QPCR and results expressed as fold over the siNTC transfected cells.

Data are represented as Mean ± SEM (*, p<0.05, n=3). 178x372mm (300 x 300 DPI)


Online Supplemental Data

Regulation of Inflammatory Phenotype in Macrophages by a

Diabetes-Induced Long Noncoding RNA

Marpadga A. Reddy1*

, Zhuo Chen1, Jung Tak Park

1,2, Mei Wang

1, Linda Lanting

1, Qiang

Zhang1, Kirti Bhatt

1, Amy Leung

1, Xiwei Wu

1, Sumanth Putta

1, Pål Sætrom

3, Sridevi

Devaraj4 and Rama Natarajan

1*

1 Department of Diabetes and Metabolic Diseases Research, Division of Molecular Diabetes

Research, Beckman Research Institute of City of Hope, Duarte, CA. 2Current address:

Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea.

3Departments of Computer and Information Science, Cancer Research and Molecular Medicine,

Norwegian University of Science and Technology, Trondheim, Norway (P.S.)., 4Department of

Pathology and Immunology, Baylor College of Medicine and Texas Children's Hospital,

Houston, TX.


2

Figure S1. Expression of pro-inflammatory genes and those associated with alternatively activated macrophages in BMMC(MC) and BMGM (GM). A-B. Bar graphs showing gene

expression in BMGM from db/+ (A) and db/db (B) mice relative to respective BMMC. BMMs

from db/+ and db/db mice were cultured in 10 ng/ml MCSF (BMMC) or 20 ng/ml GM-CSF

(BMGM) for 7 days and expression of indicated genes were analyzed by RT-QPCR. Results

were expressed as Fold over BMMC. Data showed elevated levels of inflammatory genes and

reduced levels of alternatively activated genes in BMGM from both db/+ (A) and db/db (B)

mice, consistent with M1 and M2 phenotypes of GM-CSF and MCSF differentiated cells

respectively. Bar graph for Arg1 is shown truncated because its levels were >100 fold in

BMGM vs BMMC. Mean±SEM (*, p<0.05, n=4).

.


3

Figure S2. Results of RNA-seq analysis showing expression of Macrophage M1/M2 markers in BMMC, and differentially expressed genes in BMGM. A. Typical M1 gene

expression levels in BMGM relative to BMMC. B. Typical M2 gene expression levels in

BMMC relative to BMGM. C. Csf1 and Csf1R expression levels are elevated in BMMC relative

to BMGM. In contrast, expression of GM-CSF receptors (Csf2rb and Csf2rb2) was markedly

reduced in BMMC. Global gene expression in BMMC and BMGM isolated from db/+ and db/db

mice was analyzed by RNA-seq as indicated in the Methods section. Results shown further

support the M1 and M2 phenotypes of BMGM and BMMC respectively. Relative levels of

indicated genes are shown. Data represents log2 ratio as indicated on the ‘X’ axis.


4

Figure S3. Flow cytometry data showing protein levels of macrophage markers in BMM. BMMC and BMGM from db/+ and db/db mice were stained with F4/80 (A), CD115 (B), CD11b

(C) and CD11c (D) antibodies and FACS analyses were performed. Data represents % of cells

positive for the indicated antibodies relative to total number of DAPI stained cells. A *, p<0.05

(n=4). B-D (n=2). Flow cytometry experiments revealed that majority of BMMC were positive

for F4/80, CD115 (CSF1R), CD11b and CD11c. Whereas in BMGM, cells positive for F4/80

and CD115 were reduced, but those positive for CD11c were moderately increased. BMMs were

detached using Accutase, washed and resuspended in 100 ul of PBS containing 0.5% BSA

(FACS buffer, FB). BMMs were blocked with anti-mouse CD16/32 Antibody (TruStain fcX™,

Biolegend, San Diego, CA) for 20 min, then incubated for 30 min at 40C with the indicated

fluorescently labeled antibodies (purchased from Biolegend). Cells were then washed three times

with FB, stained with DAPI and FACS analysis was performed by the Analytical Flow

Cytometry core facility on an LSR Fortessa X-20 (BD Bioscience, San Diego, CA), with color

compensation without dye, and single color stained cells. Data was analyzed using FlowJo

software and results are shown as the % of DAPI stained cells.


5

Figure S4. Scatter plot of differentially expressed RefSeq genes in db/dbBMGM vs

db/+BMGM. RNA-seq data of BMGM from db/+ and db/db mice were analyzed as decribed in

Methods section. Data represents log2 ratio (db/dbBMGM-db/+BMGM).


6

Figure S5. Canonical Signaling Pathways enriched among differentially expressed genes (DEG) in db/dbBMMC. DEG (with minimum coverage of 1 and ≥ 1.5 fold change) were

analyzed by Ingenuity Pathway Analysis (IPA). Bar graph shows top 9 significantly enriched

pathways.


7

Figure S6. Enriched motifs (FDR < 5%) identified by GSEA among differentially expressed genes (DEG) in db/dbBMMC. All RefSeq genes were ranked based on fold change (log2 ratio)

in each gene’s expression between db/dbBMMC and db/+BMMC and analyzed by pre-ranked

GSEA for enriched transcription factor binding sites. Results showed that the promoters (± 2 kb

from transcription start sites) of upregulated genes were significantly enriched (FDR < 5%) with

Serum Response Factor (SRF) motifs.


8

Figure S7. Regulation of inflammatory genes by E330013P06 in BMMC. (A-B) BMMC from

normal C57BL/6 mice were transiently transfected with E33 or GFP control (CV) expression

vectors. Expression of E33 mRNA (A) and mRNAs of the indicated inflammatory genes (B) was

determined by RT-QPCR 48 h post transfection. Results were expressed as fold over GFP

transfected cells (CV). Data are represented as Mean±SEM (n=2).


9

Table S1. Alignment Summary of RNA-seq Data Analysis.

db/+BMGM db/dbBMGM db/+BMMC db/dbBMMC

Total Reads 50,280,104 49,644,878 54,959,198 49,598,692

Aligned Reads 39,869,517 39,800,076 42,441,619 39,777,530

Exon Reads 14,382,802 15,150,865 13,524,428 14,580,453

rRNA Reads 13,928,610 12,049,716 14,176,581 11,977,409

% Aligned 79.29% 80.17% 77.22% 80.20%

% Exon 36.07% 38.07% 31.87% 36.65%

%r RNA 34.94% 30.28% 33.40% 30.11%


10

Table S2. Sequences of primers used in PCR and siRNA oligonucleotides used in this

study.

Gene ID Forward Reverse

QPCR Primers (Mouse)

Actb ACCTTCTACAATGAGCTGCG CTGGATGGCTACGTACATGG

Arg1 TGCATATCTGCCAAAGACATCG CGTAGAAGTGTTCCCCAGGGTCTACG

Ccl2 AGGTCCCTGTCATGCTTCTGG CAGCACTTCTTTGGGACACCTGCTG

Cd36 GATGACGTGGCAAAGAACAG TCCTCGGGGTCCTGAGTTAT

E33 GGCCTCCACCTCCGAAGA GAATCCTACCAGCCATCACTC

Il6 ACAAAGCCAGAGTCCTTCAGAG ACCACAGTGAGGAATGTCCAC

Il10 TGCTTCTATGCAGTTGATGAAGAT AGCTCCAAGACCAAGGTGTC

Nos2 CCTGGAGACCCACACACTGG CACAGCCACATTGATCTCCG

Pip2a TTCTTGCTGTCTTTGGAACTTTGTC ATGGTCAACCCCACCGTGT

Ptgs2 CGGTTTTGAGTTTGAAGTGGTAACCG GAGCAACTATTCCAAACCAGCA

Tgfb GGGGTCTACAACCAACACAACCCG GCAGGAGCGCACAATCATGT

Tnf TGTTGCCTCCTCTTTTGCTT TGGTCACCAAATCAGCGTTA

Taqman Assay Primers (Mouse)

E330013P06 TGTGACAGTATCCGGCCAATGCTA ACACTATGGGAAAGCCACTGGGAT

E330013P06-Probe ATTTCCACCRATCCTCCTATGCTCTGC

siRNAs (Mouse)

siE33-Pr-1 GAGACAUCUUCUGAAGACAUU UGUCUUCAGAAGAUGUCUCUU

siE33-Pr-2 GUUCUGUGACAGUAAUAAAUU UUUAUUACUGUCACAGAACUU

siE33-Pr-3 GAAGACAUUUCUGAAGAGAUU UCUCUUCAGAAAUGUCUUCUU

siNTC ON-TARGET Plus Non-targeting siRNA #1 (Thermo Scientific)

QPCR primers (Human)

MIR143HG AGGGCCAGCAGCAGGC TCAGGAAATGTCTCTGGCTGTG

PPIA GACTGAGTGGTTGGATGGCAAG CGCTTATTCCTGGACCCAAAGCG


Table S3. Differentially expressed Refseq lncRNAs identified by RNA-seq in db/dbBMMC relative to db/+BMMC. Table

showing differentially expressed known lncRNAs and nearby differentially-expressed genes (DEG) including miRNAs (coverage >=1

in at least one sample and fold change ≥1.5) located within 500kb flanking regions of lncRNAs in db/dbBMMC.

Symbol Raw Reads Normalized Coverage log2 Ratio DEG(s) * located <500kb

miRNAs located < 500kb

db/+ db/db db/+ db/db Symbol log2 Ratio within lncRNA Nearby

Gm6578 0 12 0 1.06 3.52

Olfr856-ps1 5 34 0.29 1.8 2.41

2010009K17Rik 4 17 0.49 2.58 2.04

Gm16596 6 30 0.37 1.68 2.02 Eml1, Hhipl1 1.1, 1.27 miR345

2700086A05Rik 2 11 0.23 1.15 1.87 Hoxa10, Hoxa5,

Hoxa9

1.92, 1.46, 0.69 miRa,

miR196b

Gm6634 7 28 0.37 1.35 1.73

D030025P21Rik 18 66 0.43 1.45 1.69 Ltbp2 1.34

Dnm3os 218 769 2.61 8.45 1.69 miR199a-2,

miR214

miR199a-2,

miR214

0610007N19Rik 25 89 1.6 5.22 1.66 Snord123 0.9

4933436C20Rik 6 24 0.46 1.6 1.65 Mmp2, Irx5 1.37, 1.1

Has2as 5 20 0.98 3.37 1.61 Has2 1.17

3110045C21Rik 6 21 0.5 1.61 1.52 Ddr2, Rgs4,

Hsd17b7

1.57, 2.62, 0.63

2610203C20Rik 179 545 6.6 18.41 1.47 miR125b-1 miR100, miRlet7a-2,

miR125b-1

Gm11149 15 47 1.12 3.19 1.46 Ncam1 1.2

2810408I11Rik 7 23 0.94 2.8 1.46 Ccnyl1 0.89

E330013P06 28 80 1.95 5.12 1.36 Arhgef37, Ppargc1b,

LOC545261

-0.81, -0.65, 0.59 miR145,

miR143

miR145, miR143

Rps4y2 31 86 3.13 7.95 1.32

4930429B21Rik 25 65 1.17 2.9 1.27

A330069E16Rik 10 28 1.06 2.73 1.27 Pacsin3 1.36

2810047C21Rik1 27 69 1.56 3.64 1.2


12

St5 125 312 2.61 5.98 1.19

Gm7334 60 150 4.19 9.59 1.18 Btg3 1.18

2810403D21Rik 8 21 0.75 1.81 1.16

C130083M11Rik 17 40 0.78 1.67 1.06 Sod3 1.39

1700007L15Rik 16 38 2.03 4.3 1.04 Heg1 0.89 miR1947

Gm2115 125 275 6.37 12.79 1 9930013L23Rik,

Fah, Arnt2

1.6, 0.98, 0.71

Tubb2a-ps2 16 36 2.16 4.45 1

E130310I04Rik 25 54 0.63 1.25 0.96 Mylk, Kalrn 1.24, 0.96

C630043F03Rik 36 74 1.27 2.4 0.89

A130040M12Rik 85 172 2.5 4.64 0.88 Gm5141 0.6 miR713

Gm10941 7 15 0.94 1.84 0.88 miR1930

4921511C10Rik 24 49 1.56 2.91 0.87

B430319G15Rik 24 49 0.56 1.05 0.87 Plod2, Plscr2 1.5, 1.46

D4Ertd617e 26 51 2.95 5.5 0.87 Ptprf, Mpl 0.71, 0.61

4930447K03Rik 24 47 2.2 3.94 0.82

9130206I24Rik 33 64 1.09 1.94 0.81

D430020J02Rik 63 121 2.07 3.64 0.8

2610019E17Rik 19 36 6.09 10.57 0.76 Dnase1l2, Slc9a3r2 -1.12, 0.79 miR5134

4732491K20Rik 28 52 0.84 1.43 0.74

E130304I02Rik 4 8 0.54 1 0.73 Zfp507 1.02

LOC100642166 39 71 2.39 3.98 0.72 Gm15787, Gm10416 -0.66, -0.78

4933412E12Rik 47 85 2.59 4.29 0.72 Kcnmb4 0.99

Fcrla 13 24 0.72 1.21 0.71 Olfml2b, Adamts4 1.13, 0.67

Snhg1 185 329 36.21 58.99 0.7 miR5136

Pvt1 95 168 2.93 4.75 0.69 Myc 0.65

Ccl25 27 48 2 3.26 0.68 Ctxn1, Cd209d 1.33, 1.05

4930581F22Rik 76 132 3 4.78 0.66 Cdon 1


13

9130024F11Rik 30 52 0.76 1.21 0.65

Gm15421 575 979 67.44 105.18 0.64

Gm14005 44 75 0.93 1.45 0.63

Gm14057 18 31 0.8 1.26 0.63 Gfra4 -1.77 miR103-2

Gm10791 40 68 2.29 3.57 0.63 miR155

1700084J12Rik 5 9 0.66 1.07 0.62 Sdc2 1.56

Gm13939 23 39 1.62 2.51 0.61 Lgr4 1.33

A830082N09Rik 113 189 1.37 2.1 0.61 Fam26e, Col10a1,

Frk

1.4, 0.86, 0.98

Npff 43 72 4.07 6.25 0.61 Sp7, Hoxc10, Hoxc9,

Hoxc6, Igfbp6

1.49, 1.07, 1.46,

1.2, 0.93 miR688,

miR196a-2,

miR615

3000002C10Rik 16 27 1.19 1.84 0.6

LOC545261 24 40 3.67 5.59 0.59 E330013P06,

Arhgef37, Ppargc1b

1.36, -0.81, -0.65 miR145, miR143

AI507597 21 35 2.31 3.52 0.59 Epha2,

B330016D10Rik,

Gm10565,

Hspb7

1.6,

-0.63,

-0.71,

0.77

Gm5434 46 33 3.01 1.98 -0.59

Alms1-ps2 20 14 1.61 1.04 -0.6 miR705

Gm11747 27 19 1.56 1 -0.6

Gm10677 661 474 29.87 19.62 -0.61

A530088E08Rik 254 181 13.45 8.78 -0.61

C330013E15Rik 40 28 4.32 2.77 -0.62 AB099516, Mettl7a2 -1.18, -0.97

B330016D10Rik 154 108 6.8 4.37 -0.63 Epha2, Hspb7,

AI507597

1.6, 0.77, 0.59

4930513N10Rik 132 91 4.44 2.83 -0.65 Ndrg4 1.82 miR1186b

LOC626693 25 17 1.53 0.95 -0.65 miR568

Gm9920 51 35 5.16 3.24 -0.65 Dscc1, Enpp2, Nov 1.82, 0.95, -0.79

Gm13845 15 10 1.48 0.9 -0.65 miR29a, miR29b-1


14

4931440J10Rik 28 19 2.96 1.84 -0.66 Fam124a, Serpine3 1.52, 0.87

Gm11648 67 46 2.33 1.47 -0.66

Gm15787 119 82 8.09 5.1 -0.66 LOC100642166,

Gm10416

0.72, -0.78

Pldi 18 13 2 1.21 -0.66 Pcbd1, Unc5b,

Adamts14

-1.01, 0.93, 0.7

LOC100859962 21 14 3.81 2.32 -0.67 Blm, Kif7 0.65, 0.81 miR1965

4930441O14Rik 43 29 1.41 0.87 -0.67

1810012K16Rik 11 7 1.93 1.12 -0.69 Plat, Thsd1 1.16, 0.89

4931403E22Rik 11 7 1.56 0.91 -0.69 Vldlr 1.46

Gm10390 129 86 3.08 1.88 -0.7 Sds -2.57

2900060B14Rik 59 39 9.38 5.69 -0.71

Gm10565 74 49 3.99 2.42 -0.71 AI507597 0.59

1110006O24Rik 31 20 3.84 2.26 -0.73 Msi1 2.14

5730405O15Rik 54 35 5.78 3.43 -0.73 Gm4984 1.25

5031425F14Rik 235 153 13.58 8.13 -0.74

Gm17745 16 10 1.77 1.01 -0.74 Ntn4, Ccdc38 1.65, 0.73 miR1931

Gm15880 44 28 1.45 0.84 -0.76 Blm, Zscan2, Nmb 0.65, -0.91, 0.6 miR1965

2010001M06Rik 10 6 1.88 1.03 -0.76 Vps37d, Stx1a 1.25, 0.65

Gm10416 18 11 2.66 1.49 -0.78 Gm15787,

LOC100642166

-0.66, 0.72

Gm9199 40 25 2.95 1.69 -0.78 miR687

D930015M05Rik 34 21 2.71 1.53 -0.79 Syt13, Mdk 1.28, 0.6 miR1955

Gm16617 15 9 2 1.09 -0.79 Snord58b, Arhgef40,

G630016D24Rik

-1.54, 0.62, -0.88

Gm16386 28 17 4.45 2.47 -0.81

A430088P11Rik 124 76 2.62 1.47 -0.82 miR5113

G730013B05Rik 48 29 1.59 0.88 -0.83

2900005J15Rik 177 108 8.28 4.63 -0.83 Smarcd3 0.76

1700025N23Rik 27 16 2.84 1.53 -0.84 Klrg2 0.78


15

1700012D14Rik 47 28 7.07 3.86 -0.85 Adm 1.57

4930405A21Rik 11 6 1.05 0.52 -0.88 Myl9 1.36

Bc1 1 0 1.23 0 -0.96

Gm9054 28 15 3.75 1.85 -0.97 Ctsk, Ecm1, Bola1 1.19, 0.59, -0.79

A930007I19Rik 48 26 1.97 0.97 -0.98 Gm9895 -1.94 miR101b

Gpx2-ps1 25 13 2.34 1.11 -1.01 P4ha3, Dnajb13,

Plekhb1

1.52, -0.74, -0.73

Gm13807 35 18 2.56 1.21 -1.04 Syt13, Trp53i11,

Prdm11

1.28, -0.95, -0.68

Slc22a13b 20 11 1.3 0.6 -1.04 Plcd1 0.93 miR26a-1

A730085K08Rik 7 3 1.18 0.46 -1.09 miR138-1

2610035D17Rik 169 87 8.68 4.05 -1.09

Gm6277 19 9 1.27 0.55 -1.11 miR1901

2610203C22Rik 35 17 2.66 1.18 -1.12 Mybl1, Snord87 0.85, 0.77

3300005D01Rik 24 11 1.77 0.75 -1.17 Ldhal6b 0.81

D930028M14Rik 18 7 1.59 0.57 -1.35 Ceacam2, Ceacam10 -0.62, -0.9

Gm12060 16 6 1.94 0.66 -1.38

4932441J04Rik 9 3 1.18 0.36 -1.41

AI463170 9 3 1.58 0.48 -1.41 miR5135

4930520O04Rik 31 12 2.34 0.83 -1.41 Susd5, Cmtm8,

Bcl2a1c

2.35, -0.65, 0.73

4930471G03Rik 9 2 1.28 0.25 -1.81 Hbegf,

2010001M09Rik,

Nrg2, Spata24,

0610010O12Rik

1.01,

3.13,

-1.08, 1.02,

-0.69

Gm9895 10 2 1.43 0.26 -1.94 Slc1a1,

A930007I19Rik

1.87, -0.98 miR101b

Rprl1 3 0 1.17 0 -1.94

Gm16833 8 1 1.12 0.13 -2.21 Arhgap42 2.23

A930041C12Rik 21 3 3.52 0.48 -2.47 Tgfbr3, Gfi1 0.84, -0.94


16

Table S4. Differentially expressed novel lncRNAs identified by RNA-seq in db/dbBMMC relative to db/+BMMC. Table

showing differentially expressed novel lncRNAs and nearby differentially-expressed genes (DEG) including miRNAs (coverage >=1

in at least one sample and fold change ≥1.5) located within 500kb flanking regions of lncRNAs in db/dbBMMC.

Symbol Chr

Gene

Start

Gene

End Strand Normalized coverage

log2

Ratio DEG(s) * located <500kb

db/+ db/db Symbol log2 Ratio

XLOC_009934 chr14 53988397 54080873 + 0.46 1.19 1.26

XLOC_008488 chr13 52655656 52668673 + 2.73 1.41 -0.93

XLOC_022091 chr3 1.17E+08 1.17E+08 - 1.65 0.89 -0.8

XLOC_027444 chr6 52108422 52112151 + 4.4 8.44 0.93 Hoxa10, Hoxa5,

2700086A05Rik, Hoxa9

1.92, 1.46,

1.87, 0.69

XLOC_013863 chr17 26122835 26125865 + 0.68 1.45 1.06 Arhgdig 0.7

XLOC_025108 chr5 64436202 64439498 + 1.04 1.78 0.71

XLOC_005286 chr11 1.22E+08 1.22E+08 + 1.29 0.68 -0.9

XLOC_016083 chr18 5141738 5142989 - 0.89 1.88 0.91 Zeb1 0.59

XLOC_018358 chr2 75616068 75620823 + 1.11 0.55 -1 Ttc30a2 0.61

XLOC_003162 chr10 11019155 11028604 - 2.38 3.87 0.7

XLOC_018419 chr2 90910643 90912849 + 3.34 1.82 -0.87 Pacsin3, A330069E16Rik 1.36, 1.27

XLOC_011486 chr15 96291630 96294025 + 1.45 2.32 0.66

XLOC_013712 chr17 17232861 17290922 + 0.91 1.51 0.76

XLOC_006812 chr12 8516892 8528038 + 1.76 2.75 0.64 Wdr35 0.78

XLOC_032799 chr8 64391485 64392225 - 2.96 1.18 -1.25

XLOC_032233 chr8 94326573 94328652 + 0.18 1.14 2.46 Irx3, Rpgrip1l 1.70, 0.98

XLOC_000064 chr1 23263129 23269201 + 0.91 2.61 1.49


17

XLOC_015981 chr18 61799279 61805019 + 3.75 5.76 0.62 E330013P06, Arhgef37,

Ppargc1b, LOC545261

1.36, -

0.81, -

0.65, 0.59

XLOC_023056 chr4 1.17E+08 1.17E+08 + 2.31 3.86 0.74 Tmem53 1.2

XLOC_022689 chr4 16091268 16120188 + 2.13 4.34 1.02 Ripk2 0.88

XLOC_018094 chr2 27951034 27966815 + 2.26 1.39 -0.64 Col5a1 1.53

XLOC_023859 chr4 94863603 94873653 - 1.23 0.66 -0.8

XLOC_026751 chr5 1.50E+08 1.50E+08 - 0.78 1.39 0.83 6330406I15Rik 2.04

XLOC_000226 chr1 58562003 58568784 + 0.5 1.26 1.24 Aox1 1.01

XLOC_036089 chrX 1.48E+08 1.48E+08 + 5.98 10.15 0.76 Ribc1 0.66

XLOC_034159 chr9 78276728 78277861 + 0.77 1.29 0.67 Cd109, Gsta4 1.28, 1.80

XLOC_013680 chr17 12511770 12518247 + 0.96 2.6 1.4 4732491K20Rik 0.74

XLOC_001697 chr1 1.56E+08 1.56E+08 - 1.99 3.66 0.87 Rgs16 1.06

XLOC_015899 chr18 46774320 46776318 + 1.97 0.48 -1.86 Cdo1 2.15

XLOC_024009 chr4 1.26E+08 1.26E+08 - 2.21 0.82 -1.4

XLOC_015977 chr18 61607867 61609225 + 1.68 0.59 -1.51 Pdgfrb, E330013P06,

Arhgef37, Ppargc1b,

LOC545261

1.41, 1.36,

-0.81, -

0.65, 0.59

XLOC_020121 chr2 1.74E+08 1.74E+08 - 1.57 0.28 -2.36 Pmepa1 0.94

XLOC_030950 chr7 1.21E+08 1.21E+08 - 1.06 2.01 0.92 Rras2, Insc 1.35, 1.42

XLOC_023440 chr4 1.49E+08 1.49E+08 + 4.3 6.56 0.61 Clstn1, Spsb1 0.87, 0.73

XLOC_028216 chr6 54680226 54710351 - 1.6 0.82 -0.95 Fkbp14, Prr15, Ggct 1.36, -

1.33, 0.88


18

XLOC_021413 chr3 1.31E+08 1.31E+08 + 2.08 1.34 -0.63 Sgms2 1.15

XLOC_006144 chr11 1.02E+08 1.02E+08 - 1.86 1.19 -0.64 Itga2b, Fzd2, Cd300lg, Gjc1 0.92, 0.86,

-0.73, 0.94

XLOC_025312 chr5 1.06E+08 1.06E+08 + 0.85 1.38 0.69 Gbp6, Gbp10, Gbp11 -1.15, -

1.21, -1.00

XLOC_006970 chr12 36023137 36098218 + 1.43 2.52 0.79

XLOC_027746 chr6 1.18E+08 1.18E+08 + 1.59 2.5 0.64 Ret, Rasgef1a 1.22, -0.60

XLOC_029893 chr7 1.24E+08 1.24E+08 + 0.77 1.89 1.27

XLOC_034053 chr9 60905344 60906877 + 0.77 2.3 1.43 Uaca 0.65

XLOC_036042 chrX 1.31E+08 1.31E+08 + 0.42 1.01 1.13 .Armcx2, .Armcx1,

.Timm8a1, .Armcx6

1.33, 1.50,

0.77, 0.69

XLOC_005445 chr11 31607033 31612704 - 0.97 1.93 0.94 Stc2 1.6

XLOC_026139 chr5 53921133 53932057 - 2.03 1.28 -0.67

XLOC_014384 chr17 73215869 73216863 + 2.12 0.94 -1.14

XLOC_023885 chr4 1.06E+08 1.06E+08 - 3.45 2.07 -0.71

XLOC_017563 chr19 40711748 40713128 - 4.25 2.55 -0.72 Sorbs1, Pdlim1, Aldh18a1 1.37, 1.01,

0.75

XLOC_035826 chrX 47890566 47903555 + 1.68 2.92 0.73

XLOC_021059 chr3 69411799 69415091 + 0.94 1.48 0.59

XLOC_007906 chr12 1.14E+08 1.14E+08 - 0.6 1.6 0.96 Crip2, Tmem121 1.14, 0.88

XLOC_004434 chr11 57906304 57916714 + 1.13 0.75 -0.72
