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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Journal of Hematology & Oncology 1/2017

A comprehensive data mining study shows that most nuclear receptors act as newly proposed homeostasis-associated molecular pattern receptors

Zeitschrift:
Journal of Hematology & Oncology > Ausgabe 1/2017
Autoren:
Luqiao Wang, Gayani Nanayakkara, Qian Yang, Hongmei Tan, Charles Drummer, Yu Sun, Ying Shao, Hangfei Fu, Ramon Cueto, Huimin Shan, Teodoro Bottiglieri, Ya-feng Li, Candice Johnson, William Y. Yang, Fan Yang, Yanjie Xu, Hang Xi, Weiqing Liu, Jun Yu, Eric T. Choi, Xiaoshu Cheng, Hong Wang, Xiaofeng Yang
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s13045-017-0526-8) contains supplementary material, which is available to authorized users.
Abbreviations
AIM-2
Absent in melanoma-2
ASC
Apoptosis speck-like CARD-containing protein
BPA
Bisphenol A
CARD8
Caspase recruitment domain family member 8
COX4
Cytochrome c oxidase-4
CTLA-4
Cytotoxic T-lymphocyte-associated protein 4
DAMP
Danger-associated molecular patterns
ER
Estrogen receptor
GWASs
Genome-wide association studies
HAMPs
Homeostasis-associated molecular patterns
HAMPRs
Homeostasis-associated molecular pattern receptors
HIF
Hypoxia-inducible factor
HMGB1
High-mobility group box 1
HPLC
High-performance liquid chromatography
IDH
Isocitrate dehydrogenase
IFI16
Interferon gamma-inducible protein 16
IL-1β
Interleukin-1β
KLF4
Kruppel-like factor 4
LPLs
Lysophospholipids
NOD
Nucleotide-binding oligomerization domain
NR
Nuclear receptor
Ox-LDL
Oxidized low-density lipoprotein
PAMPs
Pathogen-associated molecular patterns
PBMCs
Peripheral mononuclear cells
PHD2
Prolyl hydroxylase domain-containing protein 2
PRRs
Pattern recognition receptors
PXR
Pregnane X receptor
RAGE
Receptor for advanced glycation end products
RIG-1
Retinoid acid-inducible gene 1
SAH
S-adenosyl homocysteine
SAM
S-adenosyl methionine
SDH
Succinate dehydrogenase
TCA
Tricarboxylic acid
TCGA
The Cancer Genome Atlas
TGF-β
Transforming growth factor-β
TLR
Toll-like receptor
TNF-α
Tumor necrosis factor-α
Tregs
Regulatory T cells
VEGF
Vascular endothelial growth factor

Background

Pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) generated during microbial invasion or tissue injury act as stimuli and activate the innate immune system to respond to infection or injury [ 1]. The key cellular receptors that recognize the “threat” signals initiated by PAMPs and DAMPs are referred to as PRRs (pattern recognition receptors). One of the receptor families that are highly characterized as PRRs is the Toll-like receptor (TLR) family. Most of the TLRs are mainly located on the plasma membrane and activate inflammatory genes to counteract tissue injury and mediate repair. Moreover, TLRs work in synergy with cytosolic PRR families like NLRs (NOD (nucleotide-binding oligomerization domain)-like receptors) to recognize DAMPs, particularly in what we proposed—inflammation-privileged tissues where inflammasome component genes that initiate inflammation are not constitutively expressed [ 2, 3]. Additionally, four other PRR families including C-type lectin receptors, retinoid acid-inducible gene 1 (RIG-1), absent in melanoma-2 (AIM-2), and receptor for advanced glycation end products (RAGE, also a receptor for high-mobility group box 1 (HMGB1)) have also been characterized [ 4].
Previously, using endogenous metabolite lysophospholipids (LPLs) as a prototype, we proposed a new paradigm for the first time that certain metabolites that play cellular functions during normal physiological status can adapt as pro-inflammatory mediators at elevated concentrations. We named such metabolites as “conditional DAMPs” and their endogenous receptors as “conditional DAMP receptors.” We further pointed out significant loopholes in the current danger model which identify only the six receptors mentioned above as PRRs, which we named as “classical DAMP receptors” [ 5]. Along the line, we recently reported a series of significant findings on the expression and roles of caspase-1 in the NLR pathway in vascular inflammation [ 2, 615]. In the same publication mentioned above, we concluded that activation of inflammation by conditional DAMPs may be realized via binding to their own intrinsic receptors and may not necessarily always involve or “converge to” TLRs, NLRs, and other classical DAMP receptors [ 5].
Another significant problem associated with the current danger theory is that it fails to recognize the roles played by potential endogenous metabolites in anti-inflammatory responses, inflammation resolution, and maintenance of homeostasis. Therefore, we further advanced the current paradigm by proposing endogenous metabolites such as lysophosphatidylserine and lysophosphatidylethanolamine that not only maintain homeostasis at physiological levels, but also act as anti-inflammatory mediators to inhibit inflammation and promote inflammation resolution at pathologically elevated levels as homeostasis-associated molecular patterns (HAMPs). Furthermore, we proposed that these HAMPs bind to their receptors (HAMP receptors) to initiate anti-inflammatory/homeostatic signaling and promote inflammation resolution [ 5]. However, an outstanding issue of whether endogenous lipophilic metabolites that bind to nuclear receptors can serve as HAMPs remains unknown.
The nuclear hormone receptor superfamily has 48 lipophilic ligand-activated receptors including 32 nuclear hormone receptors (NHRs) for thyroid and steroid hormones, retinoids, and vitamin D, as well as 16 orphan nuclear receptors where the ligands are yet unknown [ 1618]. Nuclear receptors (NRs), as transcription factors, have the ability to directly bind to DNA and regulate the expression of adjacent genes [ 19, 20]. Ligands for some of these NRs have been recently identified, including lipid metabolites such as fatty acids, prostaglandins, or cholesterol derivatives. These ligands can regulate gene expression by binding to NRs [ 21]. Ligand binding to a NR results in a conformational change and activation of the receptor, leading to up- or downregulation of the target gene expression. Thus, NRs are involved in the regulation of various physiological processes including development, homeostasis, and metabolism of the organism [ 22] and pathogenesis of metabolic disease in response to metabolic/environmental changes [ 23].
However, despite the recent progress, there are many aspects of NRs that have not yet been explored: first, the expression profile of NRs under physiological conditions in various human tissues have not been studied; second, whether the expression of certain NRs are either upregulated or downregulated in atherogenic and metabolic disease-related pathological conditions are not clear; third, mechanistically, whether pro-/anti-inflammatory signaling is negatively/positively associated with the expression of NRs is not known; and fourth, whether NRs have the capacity to function as our newly proposed HAMP receptors, which suppress inflammatory responses and maintain tissue homeostasis in response to the stimulation of exogenous and endogenous PAMPs/DAMPs. To address these questions, we took a “panoramic view” at the tissue expression pattern of all 48 identified human and mouse NRs. Our results demonstrated that NRs are differentially expressed among tissues at physiological conditions, which may be regulated by oxygen sensors, vascular endothelial growth factor pathways, stem cell master regulators, innate immune sensors, and DNA hypo-/hypermethylation status. We also found that the expressions of certain NRs have less tendency to be upregulated than to be downregulated in atherogenic conditions, metabolic diseases, which may be contributed by significant regulation of innate immune sensor caspase-1/inflammasome pathway. Our findings provide novel insights into the upstream regulation of nuclear receptors in physiological, autoimmune arthritis, and cardiovascular and metabolic disease conditions.

Methods

Tissue expression profiles of genes encoding nuclear receptors

An experimental data mining strategy (Fig.  1) was used to analyze the expression profiles of mRNA transcripts of NR genes in 21 different human and 17 mouse tissues including the heart and vasculature. We utilized an experimentally verified mRNA expression in the expressed sequence tag (EST) databases of the National Institutes of Health (NIH)/National Center of Biotechnology Information (NCBI) UniGene ( http://​www.​ncbi.​nlm.​nih.​gov/​sites/​entrez?​db=​unigene) to determine the transcription profile of nuclear receptors in tissues of interest. Transcripts per million of genes of interest were normalized to that of housekeeping gene β-actin in each given tissue to calculate the arbitrary units of gene expression. A confidence interval of the expression variation of housekeeping genes was generated by calculating the mean plus two times that of the standard deviation of the arbitrary units of three randomly selected housekeeping genes (PRS27A, GADPH, and ARHGDIA in human; Ldha, Nono, and Rpl32 in mouse) normalized by β-actin in the given tissues. If the expression variation of a given gene in the tissues was larger than the upper limit of the confidence interval, the high expression levels of genes in the tissues were considered statistically significant. Gene transcripts where the expression level was lower than one per million were technically considered as no expression.

Expression profiles of nuclear receptors in disease models and cell activity

Microarray datasets were collected from the Array Express of European Bioinformatics Institute, which stores data from high-throughput functional genomics experiments ( https://​www.​ebi.​ac.​uk/​arrayexpress). These data include the information of the expression of nuclear receptors through experiments submitted directly to Array Express or imported from the NCBI Gene Expression Omnibus database. We used data from the following databases: (1) Metabolic disease: (a) adipose tissue and liver in high fat diet-induced obese mouse model versus normal diet controls, (b) aortic arch segment of the atherogenic apolipoprotein E gene knockout (apolipoprotein E (ApoE−/−)) mice versus wild-type mouse aorta controls, (c) pancreatic islets and white fat of leptin receptor mutant db/db type II diabetic mice versus control mice, (d) oxidized low-density lipoprotein (Ox-LDL)-stimulated mouse endothelial cells versus control endothelial cells, and (e) high-concentration homocysteine (Hcy)-treated human aortic smooth muscle cells (HASMCs) versus low-concentration homocysteine (Hcy)-treated vascular smooth muscle cells (VSMCs); (2) CD4+Foxp3+ regulatory T cell (Treg) polarization/differentiation—we examined the expression changes of the nuclear receptors in Tregs versus effector T cells in mice, as well as in vitro, with cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) ligation; (3) mRNA expression of NR changes due to the stimulus with pro-/anti-inflammation conditions; and (4) we screened the datasets among energy metabolic nuclear receptors of tricarboxylic acid (TCA) cycle and respiratory chain. The modulation of nuclear receptor expression in cancers were determined by analyzing the Cancer Genome Atlas database.

Application of big GWAS data to clarify the relationship between nuclear receptors and metabolic disease

Genome-wide association studies (GWASs) continue to be a widely used approach to detect genetic association with a phenotype of interest in well-defined populations. Various anthropometric measures serve as surrogates for obesity, with body mass index (BMI) (HGVPM 1111 and 564) and waist-hip ratio (HGVPM 1114) as the most frequently used markers in epidemiologic studies aimed at assessing obese disease risks. Anti-cyclic citrullinated peptide-positive rheumatoid arthritis status (HGVPM 38) and rheumatoid arthritis (HGVPM 235) are the most frequently used markers in epidemiologic studies aimed at rheumatoid arthritis risk. Fasting plasma glucose (HGVPM 825), homeostatic model assessment of β-cell function (HGVPM 827), fasting insulin (HGVPM 822), homeostatic model assessment of insulin resistance (HGVPM 826), glycated hemoglobin levels (HGVPM 1081), glycosylated hemoglobin (HGVPM 569), 2-h glucose challenge (HGVPM 769), type II diabetes status (HGVPM 4 and 5), early onset type II diabetes mellitus (HGVPM 74), proinsulin levels (HGVPM 1538), and a-glucose (HGVPM 3639) are the most frequently used markers in epidemiologic studies aimed at diabetes risks. Serum cholesterol (HGVPM 568), lipids (CH3) (HGVPM 3602), lipids (CH2) (HGVPM 3611), lipids (CH2CO) (HGVPM 3616), lipids (CH=CH*CH2CH2) (HGVPM 3640), and systolic blood pressure (HGVPM 563) are the most frequently used markers in epidemiologic studies aimed at vascular atherosclerosis risks. With the advent of large GWASs, we now have the ability to identify NRs associated with dangerous risks for specific disease.

Application of MGI data to clarify the abnormal mouse phenotypes in nuclear receptor knockout mouse adipose, cardiovascular, metabolism, and endocrine systems

MouseMine ( www.​informatics.​jax.​org) is a new data warehouse for accessing mouse data from Mouse Genome Informatics (MGI). The main source of MouseMine data is MGI, which includes a wealth of information about the structure and function of the mouse genome, developmental gene expression patterns, phenotypic effects caused by mutations, and annotations of human disease models. The “Human-mouse: disease connection” tool ( www.​informatics.​jax.​org/​humanDisease.​shtml) supports uploading a list of nuclear receptor gene IDs/symbols and getting back certain information about those nuclear receptors, such as those associated human diseases and abnormal mouse phenotypes reported in adipose, cardiovascular, metabolic, and endocrine systems.

Tissue SAH and SAM measurements in mice

The concentrations of S-adenosyl methionine (SAM) and S-adenosyl homocysteine (SAH) were measured in six tissues (heart, liver, lung, kidney, spleen, and brain) in C57BL/6J ( n = 4) mice from 13.4 to 18 weeks of age. Mouse tissues were collected and homogenized in 0.4 mol/L perchloric acid (PCA) solution. The homogenized tissues were centrifuged for 10 min at 2000 rpm. Supernatant was collected and stored at −80 °C. SAM and SAH levels were analyzed by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS; Institute of Metabolic Disease, Baylor Research Institute, Dallas, TX). The unit of SAH level in tissues is nanomole per gram [ 24].

Results

Nuclear receptors are differentially expressed in tissues. Nuclear receptor expression is associated with angiogenesis pathway, stem cell master genes, PRRs, and tissue hypomethylation/hypermethylation indices

As summarized in Table  1, the NR superfamily includes 48 NRs classified into seven families, such as class I-thyroid hormone receptor-like family (19 members), class II-retinoid X receptor-like family (12 members), class III-estrogen receptor-like family (9 members), class IV-nerve growth factor IB-like family (3 members), class V-steroidogenic factor-like receptor family (2 members), class VI-germ cell nuclear receptor-like family (1 member), and class O-miscellaneous family (2 members). In addition, we summarized seven common features of the NR superfamily in Table  2. One of the most striking features of NRs is that in addition to transduce steroid, thyroid, retinoid, and other hormone signals, NRs can also serve as metabolic sensors and xenobiotic sensors for high-affinity ligands and low-affinity molecular patterns [ 25]. Several reports showed that NRs not only bind to specific ligands but also recognize structural patterns (Table  3), which raises a possibility for NRs to recognize many endogenous metabolites that can act as HAMPs that are yet to be identified/characterized [ 5].
Table 1
The UniGene ID of 48 human nuclear receptors and mouse homologs
Gene name (full name)
NRNC symbol
Receptor
Ligand(s)
ID
Human
Mouse (Mm.)
(Hs.)
Class I—thyroid hormone receptor-like
 THRA
Thyroid hormone receptor, alpha
NR1A1
Thyroid hormone receptor
Thyroid hormone
724
265917
 THRB
Thyroid hormone receptor, beta
NR1A2
187861
32563
 RARA
Etinoic acid receptor, alpha
NR1B1
Retinoic acid receptor
Vitamin A and related compounds
654583
439744
 RARB
Etinoic acid receptor, beta
NR1B2
654490
259318
 RARG
Etinoic acid receptor, gamma
NR1B3
1497
1273
 PPARA
Peroxisome proliferator-activated receptor alpha
NR1C1
Peroxisome proliferator-activated receptor
Fatty acids, prostaglandins
103110
212789
 PPARD
Peroxisome proliferator-activated receptor delta
NR1C2
696032
328914
 PPARG
Peroxisome proliferator-activated receptor gamma
NR1C3
162646
3020
 NR1D1
Nuclear receptor subfamily 1 group D member 1
NR1D1
Rev-ErbA
Heme
592130
390397
 NR1D2
Nuclear receptor subfamily 1 group D member 2
NR1D2
37288
26587
 RORA
RAR-related orphan receptor A
NR1F1
 
Cholesterol
560343
427266
 RORB
RAR-related orphan receptor B
NR1F2
494178
234641
 RORC
RAR-related orphan receptor C
NR1F3
256022
4372
 NR1H3
Nuclear receptor subfamily 1 group H member 3
NR1H3
Liver X receptor-like receptor
Oxysterols
438863
22690
 NR1H2
Nuclear receptor subfamily 1 group H member 2
NR1H2
432976
968
 NR1H4
Nuclear receptor subfamily 1 group H member 4
NR1H4
282735
3095
 VDR
Vitamin D (1,25-dihydroxyvitamin D3) receptor
NR1I1
Vitamin D receptor-like receptor
Vitamin D
524368
245084
 NR1I2
Nuclear receptor subfamily 1group I member 2
NR1I2
Xenobiotics
7303
8509
 NR1I3
Nuclear receptor subfamily 1 group I member 3
NR1I3
Androstane
349642
486506
Class II—retinoid X receptor-like
 HNF4A
Hepatocyte nuclear factor 4, alpha
NR2A1
Hepatocyte nuclear factor-4 receptor
Fatty acids
116462
202383
 HNF4G
Hepatocyte nuclear factor 4, gamma
NR2A2
241529
330897
 RXRA
Retinoid X receptor alpha
NR2B1
Retinoid X receptor
Retinoids
590886
24624
 RXRB
Retinoid X receptor beta
NR2B2
388034
1243
 RXRG
Retinoid X receptor gamma
NR2B3
26550
3475
 NR2C1
Nuclear receptor subfamily 2 group C member 1
NR2C1
Testicular receptor
UD
108301
107483
 NR2C2
Nuclear receptor subfamily 2 group C member 1
NR2C2
555973
87062
 NR2E1
Nuclear receptor subfamily 2 group E member 1
NR2E1
Tailless-like receptors
UD
157688
287100
 NR2E3
Nuclear receptor subfamily 2 group E member 3
NR2E3
187354
103641
 NR2F1
Nuclear receptor subfamily 2 group F member 1
NR2F1
COUP-TF-like receptors
UD
347991
439653
 NR2F2
Nuclear receptor subfamily 2 group F member 2
NR2F2
519445
158143
 NR2F6
Nuclear receptor subfamily 2 group F member 6
NR2F6
466148
28989
Class III—estrogen receptor-like
 ESR1
Estrogen receptor 1
NR3A1
Estrogen receptor
Estrogens
208124
9213
 ESR2
Estrogen receptor 2
NR3A2
660607
2561
 ESRRA
Estrogen-related receptor alpha
NR3B1
Estrogen-related receptor
UD
110849
386776
 ESRRB
Estrogen-related receptor beta
NR3B2
435845
235550
 ESRRG
Estrogen-related receptor gamma
NR3B3
444225
89989
 NR3C1
Nuclear receptor subfamily 3 group C member 1
NR3C1
3-Ketosteroid receptors
Cortisol
122926
129481
 NR3C2
Nuclear receptor subfamily 3 group C member 2
NR3C2
Aldosterone
163924
324393
 PGR
Progesterone receptor
NR3C3
Progesterone
32405
12798
 AR
Androgen receptor
NR3C4
Testosterone
76704
439657
Class IV—nerve growth factor IB-like
 NR4A1
Nuclear receptor subfamily 4 group A member 1
NR4A1
Nerve growth factor IB-like receptors
UD
524430
119
 NR4A2
Nuclear receptor subfamily 4 group A member 2
NR4A2
563344
3507
 NR4A3
Nuclear receptor subfamily 4 group A member 3
NR4A3
279522
247261
Class V—steroidogenic factor-like
 NR5A1
Nuclear receptor subfamily 5 group A member 1
NR5A1
Fushi tarazu F1-like receptors
Phosphatidylinositols
495108
31387
 NR5A2
Nuclear receptor subfamily 5 group A member 2
NR5A2
33446
16794
Class VI—germ cell nuclear factor-like
 NR6A1
Nuclear receptor subfamily 6 group A member 1
NR6A1
Germ cell nuclear factor receptors
UD
586460
439703
Class O—miscellaneous
 NR0B1
Nuclear receptor subfamily 0 group B member 1
NR0B1
DAX-like Receptors
UD
268490
5180
 NR0B2
Nuclear receptor subfamily 0 group B member 2
NR0B2
427055
346759
UD undetermined
Table 2
The common features of nuclear receptors
Common features of nuclear receptors
PMID
1. Five domain structures including N-terminal regulatory domain, DNA binding domain, hinge region, ligand-binding domain, and C-terminal domain
10406480/10751636/12893880
2. Lipophilic ligand-activated transcription factors including orphan receptors for unknown endogenous ligands
8,807,884/10671476
3. 48 known super human family members including seven groups, mice (49), rats (47), C. elephant (270)
10219237/9460643/15059999
4. 350 co-regulators to facilitate their functions
22733267
5. Transduce steroid, thyroid, retinoid, and other hormonal signals
11729302/8521507
6. Metabolic sensors and xenobiotic sensors for high-affinity ligands and low-affinity molecular patterns
20615454
7. Serve as the targets for 13% FDA-approved drugs
17139284
Table 3
Nuclear receptors can recognize and bind many ligands which have similar structures/patterns via its ligand-binding domain
Features of nuclear receptors’ ligand-binding domain
PMID
1. Ligand-binding domains have the capacity to bind coactivator segments with LXXLL sequences, and corepressor segments with LXXXLXXX[I/L] sequences (where L = leucine, I = isoleucine, and X = any amino acid)
9808622
2. A single nuclear receptor controls the multitude of gene expressions
20148675
3. The ligand-binding domain consists of a hydrophobic pocket that can bind a hydrophobic ligand
20615454
4. Flexible ligands can contort to fit in the ligand-binding pocket
9501913
5. Pharmacological antagonists and have been shown to bind to the receptor in the ligand-binding site and to inhibit hormone-activated receptor function
 
(1). NR1A1 ligand-binding domain can bind 3,5-dimethyl-3-isopropylthyronine except thyroid hormone
8523397
(2). NR1B3 ligand-binding domain can bind to all-trans retinoic acid except vitamin A and related compounds
7501014
(3). NR3A1 ligand-binding domain can bind to estradiol and raloxifene
9338790
To determine whether tissues have functional differences in sensing metabolic stressors and xenobiotic stressors via NRs, we hypothesized that various tissues express differential levels and certain types of NRs under physiological conditions. To examine this hypothesis, the expression of 48 NR genes in 21 human tissues and 17 mouse tissues were examined (fewer mouse tissues were examined due to unavailability of gene expression data for four types of mouse tissues, i.e., nerve, trachea, stomach, and vascular tissues in the NIH UniGene database) (Additional file  1: Figure S1). The results showed that some human tissues such as muscle (17), trachea (14), and nerve (10) express a large variety of NRs at high expression levels (Tables  4 and 5). This data suggests that the gene expression, differentiation, and function of these tissues may largely be regulated by NRs under normal physiological levels. Comparatively, eyes (7), adrenal gland (6), kidney (5), and adipose tissue (5) express more variety of NRs than the heart, liver, and pancreas (Table  4). Similarly, when comparing the human NR expression profile to that of the mouse, human tissues express much more types of NRs at high expression levels than mice. For example, although human and mouse muscles contain more variety of NRs at high levels relative to other tissues studied, human muscle expresses 17 NRs whereas mouse muscle expresses only 7 NRs. Among the 17 human muscle-expressed NRs, the higher expression of THRB, RORA, ESR1, ESRRA, NR3C2, and NR4A3 in human muscle is not seen in mouse muscle (Tables  4 and 5). Therefore, this indicates that these receptors were evolutionally gained, and addition of these NRs in humans may be responsible for the development of new muscle functions in response to environmental changes/nutritional changes that humans face. Furthermore, nearly half of the tissues examined (including the heart, liver, pancreas, brain, and lymph node) did not contain a large variety of NRs at high expression levels. These results suggested that the gene expression, differentiation, and function of these tissues may be largely dependent on those expressed NRs rather than the non-expressed NRs. Similarly, the human skin, spleen, stomach, vascular, blood, and lung tissue had minimal varieties of nuclear receptors in physiological conditions, since less than 4 out of 48 nuclear receptors are highly expressed (Additional file 2: Figure S2).
Table 4
28 out of 43 nuclear receptors in classes I–IV are highly expressed in the human muscle, trachea, nerve, and other tissues
Gene
Human tissues
Adipose tissue
Adrenal gland
Brain
Eye
Heart
Intestine
Kidney
Liver
Lymph node
Muscle
Nerve
Pancreas
Skin
Spleen
Stomach
Trachea
Class I—thyroid hormone receptor-like (15 out of 19)
THRA
   
*
*
*
       
*
*
         
THRB
                 
*
         
*
RARA
                             
*
RARB
     
*
   
*
*
             
*
RARG
                             
*
PPARA
 
*
       
*
*
 
*
*
         
PPARD
                   
*
       
*
PPARG
*
       
*
       
*
         
NR1D1
     
*
         
*
*
*
     
*
NR1D2
     
*
         
*
           
RORA
 
*
             
*
   
*
     
RORC
 
*
     
*
*
*
*
*
         
*
NR1H3
*
     
*
       
*
     
*
   
NR1H2
                 
*
           
 VDR
                             
*
Class II—retinoid X receptor-like (5 out of 12)
RXRA
                 
*
           
RXRB
                             
*
NR2C2
               
*
*
         
*
NR2F2
*
   
*
         
*
*
*
       
NR2F6
     
*
                       
Class III—estrogen receptor-like (5 out of 9)
ESR1
 
*
             
*
         
*
ESRRA
           
*
 
*
*
 
*
       
NR3C2
     
*
*
       
*
       
*
*
 PGR
       
*
 
*
*
 
*
*
         
 AR
                   
*
*
     
*
Class IV—nerve growth factor IB-like (3 out of 3)
NR4A1
*
                             
NR4A2
 
*
*
             
*
       
*
NR4A3
*
*
             
*
*
         
*High expression
Table 5
15 out of 41 nuclear receptors in classes I–VI are highly expressed in the mouse muscle, skin, and other tissues
Gene
Mouse tissues
Adrenal gland
Blood
Brain
Eye
Heart
Intestine
Kidney
Liver
Lung
Lymph node
Muscle
Pancreas
Skin
Spleen
Class I—thyroid hormone receptor-like (7 out of 19)
 Thra
   
*
 
*
*
       
*
 
*
 
 Rara
                 
*
 
*
*
*
 Ppara
     
*
 
*
*
*
   
*
*
 
*
 Nr1d1
*
                 
*
     
 Nr1d2
*
                 
*
     
 Nr1h2
               
*
     
*
 
 Vdr
                       
*
*
Class II—retinoid X receptor-like (5 out of 12)
 Rxra
*
                 
*
     
 Nr2c1
       
*
             
*
 
 Nr2c2
                   
*
     
 Nr2f2
*
       
*
         
*
   
 Nr2f6
       
*
     
*
   
*
   
Class III—estrogen receptor-like (2 out of 9)
 Nr3c1
                   
*
 
*
*
 Ar
 
*
                       
Class VI—germ cell nuclear factor-like (1 out of 1)
 Nr6a1
*
                   
*
*
*
*High expression
Based on the distribution pattern of highly expressed NRs among the tissues, we classified NRs into following four groups: very highly distributed, highly distributed, moderately distributed, and scarcely distributed (Table  6). In order to determine whether very highly distributed and highly distributed groups of NRs have any functional differences from that of moderately distributed and scarcely distributed group of NRs, we analyzed the potential signaling pathways with the Ingenuity Pathway Analyzer for these two major groups of NRs. The results in Table  6 show that among the top 10 pathways examined for each group, the two major NR groups share four signaling pathways such as FXR/retinoid X receptor (RXR) activation, hepatic cholestasis, aryl hydrocarbon receptor signaling, and RAR activation. The very highly distributed and highly distributed group of NRs have six specific top pathways including peroxisome proliferator-activated receptor (PPAR) signaling, glucocorticoid receptor signaling, melatonin signaling, estrogen receptor signaling, adipogenesis pathway, and PPARα/RARα activation. In contrast, the moderately distributed and scarcely distributed groups of NRs have another six specific top pathways including Oct4 stem cell pluripotency, pregnane X receptor (PXR)/RXR activation, LPS/IL-1-mediated inhibition of RXR function, retinoic acid-mediated apoptosis signaling, 25-dihydroxyvitamin D3 (vitamin D3) receptor (VDR)/RXR activation, and liver X receptor (LXR)/RXR activation. Of note, the NRs that have vitamin A, vitamin D, and retinoids as ligands are all included in the scarcely distributed group. Therefore, these data suggest that the tissue expression levels and distribution pattern of NRs can be used as an indicator of functional differences in tissues.
Table 6
Nuclear receptors can be classified into four groups including very highly, highly, moderately, and scarcely distributed based on their distribution in tissues. Very highly/highly distributed nuclear receptors and moderately/scarcely expressed nuclear receptors regulate different signal pathways
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab6_HTML.gif
A previous paper reported mouse nuclear receptor tissue expression profile using nucleic acid-binding-based RT-PCR technique [ 26, 27]. However, NR superfamily expression using more accurate DNA sequencing-based technique has not been profiled in human tissues. Comparing with that reported for mouse NR expression by Bookout et al. [ 26], our results on highly expressed NRs have the following features (Table  7): (1) our expression sequence tag (EST)-based data were more precise; (2) our data included 21 human tissues, but the previous report only examined mouse tissues; and (3) our data implicated that there are significant differences between human and mouse NR expressions, which had never been investigated before. Our data shows that humans have more NRs expressed in the central nerve system (CNS, 19 versus 11), metabolic system (40 versus 13), and cardiovascular system (19 versus 8). Therefore, our data of NR tissue expression profiles have provided valuable insight over potential NR functions in human tissues.
Table 7
Several findings in this study are significantly novel in comparing to what is published
Items
Expression profile of Nuclear receptors
Our findings
Cell paper (PMID: 16923397)
The number of nuclear receptors
48 known human NR
49 known mouse NR
Species
Human and mouse
Mouse
The number of tissues
21 human tissues and 17 mouse tissues
Only 39 mouse tissues
Analysis method
cDNA cloning and DNA sequencing experiments (EST database)
RT-PCR (high-throughput capacity)
Advantage of the method
More precise
NR groups based on their tissue distribution
Restricted (11), widespread (17), all tissues (21)
NR groups based on the expression level of nuclear receptors
Super high (12), high (5), low (3), super low activation (28)
Tissue groups based on number of highly expressed nuclear receptors
Super high (3/2 in human/mouse), high (4/5 in human/mouse), low (3/5 in human/mouse), supper low varieties (6/2 in human/mouse)
CNS (# human/mouse)
Brain, eye, nerve (19/1)
Eye, brainstem, cerebellum, cerebrum, corpus striatum, olfactory bulb, spinal cord, hypothalamus, and pituitary (11)
Gastroenteric system (# human/mouse)
Stomach, pancreas (5/5)
Tongue, stomach, duodenum, jejunum, ileum, colon, and gall bladder (13)
Metabolic system (# human/mouse)
Liver, kidney, adrenal gland, adipose, intestine, and muscle (40/14)
Liver, kidney, brown and white adipose, and muscle (13)
Immune system (# human/mouse)
Spleen and lymph node (4/6)
Spleen and thymus (2)
Cardiovascular system (# human/mouse)
Heart, lung, blood and trachea (19/6)
Aorta, heart, and lung (8)
Structural system (# human/mouse)
Skin (1/7)
Bone and skin (5)
Based on the variety of NRs expressed in tissues, we classified tissues examined into three categories (Fig.  2), high variety (expressed NRs n ≥ 10; n = number of different types of highly expressed NRs), moderate variety (expressed NRs 5 ≤  n < 10), and low variety (expressed NRs n ≤ 4) in a new nuclear receptor pyramid model shown in Fig.  2 in humans. Similarly, we classified mouse NR pyramid model as high variety (expressed NRs n ≥ 7; n = numbers of the highly expressed NRs), moderate variety (expressed NRs 3 ≤  n < 7), and low variety (expressed NRs n < 3) (Fig.  2). These results suggested that the super high variety and moderate variety of NRs are found in tissues such as the muscle, trachea, and nerves in humans and in the muscle and skin in mice. Therefore, it can be concluded that these tissues may use NR pathways the most to regulate gene expression in response to developmental, physiological, and environmental stimulation. However, a high variety expression of NRs in the trachea has not been extensively reported [ 28]. It has been reported that NRs regulate skeletal muscle mitochondrial function [ 29] and the nervous system [ 30]. In addition, those tissues that have low variety of NRs may need fewer variety of NR pathways to regulate genes in response to developmental, physiological, and environmental stimuli; thus, they may also have other redundant pathways to carry out similar functions to that of NRs.

Correlation with oxygen sensors, angiogenic genes, and stem cell master regulators in human tissues

As shown in Table  6, NR functions in tissues may be involved in metabolism and stem cell-mediated tissue regeneration. However, it has been poorly characterized whether oxygen sensor genes such as prolyl hydroxylase domain-containing protein 2 (PHD2), hypoxia-inducible factor 1B (HIF1B), HIF1A, and HIF2A regulate NR expressions in tissues [ 31]. To determine the extents to which factors and NRs are related, we conducted correlation studies, with the hypothesis that if there is a positive functional correlation, the expression of the given factor (such as oxygen sensors, genes that regulate angiogenesis pathway, stem cell master genes, PRR, and inflammasome components) and the NR will increase or decrease together [ 1]. Similarly, we analyzed the correlation between NR expression and tissue methylation indices determined by the ratios between S-adenosyl methionine (SAM—the universal methyl donor)/S-adenosyl homocysteine (SAH—a methyltransferase inhibitor) and SAH levels [ 32].
As shown in Fig.  3a, b, we examined whether highly expressed NR potential (highly expressed NRs/total NRs × 100%) in tissues are correlated with tissue expression of four oxygen-sensing genes including PHD2, HIF1B, HIF1A, and HIF2A and seven vascular endothelial growth factor (VEGF) pathway genes including VEGFA, VEGFB, VEGFC, FIGF, FLT1, KDR, and FLT4, as well as six stem cell master genes including CD34, KIT, and four Yamanaka’s inducible pluripotent stem cell (IPSC) genes such as Myc, Kruppel-like factor 4 (KLF4), POU5F1 (octamer-binding transcription factor 4 (Oct4)), and sex determining region Y (SRY)-box 2 (Sox2) [ 33]. As shown in Fig.  3b, c, among 17 genes examined, the correlation of seven genes achieved statistical significance ( p < 0.05). The highly expressed NR potentials were highly correlated with oxygen-sensing genes PHD2, HIF1B, and stem cell master regulator gene Sox2 (high correlation r 2 > 0.7). A moderate correlation was observed between highly expressed NRs and HIF1A, VEGFB, and KIT genes (0.5 ≤  r 2 ≤ 0.7). Low level correlation was observed between FLT1 and highly expressed NRs ( r 2 < 0.5). These results suggested that the expression of oxygen-sensing genes PHD2, HIF1B, and HIF1A, VEGF pathway gene VEGFB and stem cell master gene SOX2, and KIT have a positive correlation with NR expression, and these genes may be either upstream regulators or downstream targets of NR signaling pathways.

Correlation with PRRs in human tissues

Additionally, we addressed the question whether the highly expressed NRs have a positive correlation with the expression of PRR genes such as NLRs, AIM-2 (absent in melanoma-2), and IFI16 (interferon gamma-inducible protein 16) or genes of inflammasome components such as ASC (apoptosis speck-like CARD-containing protein) and CARD8 (caspase recruitment domain family member 8) [ 2, 8, 34]. As shown in Fig.  4, among 14 inflammasome-related genes examined, four PRR genes achieved statistically significant correlations ( p < 0.05). The highly expressed NR potentials were highly correlated with microbial infection-sensing NOD1 [ 35] (high correlation r 2 > 0.7), moderately correlated with NOD2 and NOD4 (0.5 ≤  r 2 ≤ 0.7), and a weak correlation with nuclear DNA damage-sensing PRR IFI16 ( r 2 < 0.5) [ 36].
It has been reported that nucleotide-binding oligomerization domain (NOD) proteins such as NOD1 and NOD2 are founding members of the NLR family, sense conserved motifs in bacterial peptidoglycan, and induce pro-inflammatory and anti-microbial responses [ 35]. It should be noted that three out of four PRRs, which include NOD1, NOD2, and NOD4 that are positively correlated with highly expressed NRs, activate inflammatory cascade independent of caspase-1 inflammasome complex. Nevertheless, IFI16, which is a PRR dominantly localized in the nucleus is a constituent of caspase-1 inflammasome complex, but it has a weak correlation with highly expressed NRs [ 37, 38]. Furthermore, NLRP3 [ 34], a PRR that is well identified as a component of caspase-1 inflammasome complex also failed to achieve a statistically significant correlation with highly expressed NRs. Therefore, this suggests that PRRs such as NOD1, NOD2, and NOD4 that function independently of caspase-1 are either upstream regulators or downstream targets of highly expressed NRs.

Correlation with methylation index in mouse tissues

DNA methylation has been recognized as one of the regulatory mechanisms underlying the expression of some NRs [ 39]. However, the question remains whether tissue methylation status regulates NR expression. There are two main intermediate compounds that determine the potential for methylation/demethylation in biological systems. S-adenosyl methionine (SAM) acts as a major methyl donor for many cellular methylation reactions of DNA, RNA, proteins, and lipids. In contrast, S-adenosyl homocysteine (SAH) is a potent inhibitor of biological transmethylation [ 40].
To determine whether tissue methylation level determines NR expression, first we measured the tissue levels of SAH and SAM in six mouse tissues including the liver, brain, heart, kidney, lung, and spleen using liquid chromatography-electrospray ionization-tandem mass spectrometry [ 41]. We then analyzed the potential correlation between highly expressed NRs and the tissue hypomethylation determined by SAH (methyltransferase inhibitor) levels. Similarly, we examined whether a positive correlation exists between highly expressed NRs and tissue hypermethylation status determined by SAM/SAH (Fig.  5). Our data implicated that the NRs that undergo expression changes based on tissue methylation and demethylation status are mutually exclusive as we reported before [ 32].
As shown in Fig.  5a, among 48 NRs examined, 6 NRs showed a statistically significant positive correlation between NR expression and tissue hypomethylation status ( p < 0.05). Two NRs including Nr1c1 (Pparα) and Nr1h3 were highly correlated with SAH levels in six tissues including the liver, brain, heart, kidney, lung, and spleen (high correlation r 2 > 0.9); four NRs including Nr1c3 (Pparγ), Nr1i2, Nr1i3, and Nr0b2 were moderately correlated with SAH levels (0.7 ≤  r 2 ≤ 0.9). Notably, most of the receptors that had increased expression levels in the presence of hypomethylation fall to class I NRs. Previously, it was shown that nutritional status can alter the methylation status of the PPARα gene and subsequently regulate its expression level both in rodent models and in humans [ 42]. It is highly likely that function of these receptors may also be increased during hypomethylation status as it provides easy access to these NRs to reach their response elements. Despite the observation that expression of certain class I NRs are increased in hypomethyation status, further experiments are needed to validate whether the function of these receptors are also enhanced.
In addition, as shown in Fig.  5c, d, among 48 NRs examined, 4 NRs achieved statistically significant correlation ( p < 0.05) with tissue hypermethylation status. Two NRs including Nr2a2 and Nr4a1 were highly correlated with the SAM/SAH ratio in six tissues including the liver, brain, heart, kidney, lung, and spleen (high correlation r 2 > 0.9); two NRs including Nr4a3 and Nr6a1 show moderate correlation with the SAM/SAH ratio (0.8 ≤  r 2 ≤ 0.9). These results suggested that tissue hypermethylation status differentially regulates the tissue expression of NRs, and the tissue expression of five NRs may be significantly upregulated by hypermethylation. These results have demonstrated for the first time that tissue hypomethylation and hypermethylation status may have an impact on expression levels of two mutually exclusive groups of NRs [ 43].
We acknowledge that our data is not adequate to conclude that the NR expression changes that we observed are due to direct hypermethylation/hypomethylation of the particular NR gene. Tissue methylation status may regulate NR expression indirectly via other mediators. Though we did not observe expression changes on estrogen receptor-alpha (ER-α) in our mouse dataset, previously it was shown that methylation status of the ER-α gene determines its expression in the colon, blood, lung, heart, prostrate, and ovary in humans [ 4448]. This was extensively studied in human breast cancer cell lines, where loss of ER expression and acquired hormone resistance was attributed to hypermethylation of the ER gene [ 44, 49]. Therefore, to conclude whether the NR expression changes we observed are due to direct methylation of the particular genes requires further experimental validation. Furthermore, pathophysiological relevance of the methylation status of the tissues and NR expression need to be tested in the future. Also, it is possible that upregulation of certain NRs may regulate the tissue methylation status via unknown pathways.
It should be noted that the values that determined the correlation tiers in Fig.  5 are different from those of Figs.  3 and 4. This is because the basal levels were different as Fig.  5 demonstrates the data obtained from mice and Figs.  3 and 4 depict the data obtained from human tissues. Also, when analyzing the correlation, NR potential was taken into account in Figs.  3 and 4, while the correlation was calculated for each and individual NR in Fig.  5.

Nuclear receptor sequence changes and mutations are associated with increased risk for development of metabolic, cardiovascular, and autoimmune diseases, hormone insensitivity/resistance, and cancers

Genome-wide association studies (GWASs) have investigated potential genetic factors that explain inter-individual variations in response to NR ligand stimulations in various pathologies [ 50]. Given that susceptibility to complex human metabolic diseases is likely a result of genes operating as part of functional modules rather than individual effects, association analysis methods hold promise in discovering additional associations from existing GWAS data [ 51]. Previous GWAS studies have been reported for NRs in some diseases such as liver injury [ 50], osteoporosis, sarcopenia, and obesity. However, it is unclear whether the GWAS data on NRs are associated with globally increased genetic risks for metabolic diseases and autoimmune disease, such as rheumatoid arthritis, obesity, diabetes, and vascular atherosclerosis in human populations.
To address this issue, we examined the GWAS database ( http://​www.​gwascentral.​org/​) for all the NRs. As shown in Tables  8 and 9, 45 out of 48 NRs with sequence changes or mutations were associated with rheumatoid arthritis, obesity, diabetes, and vascular disease and atherosclerosis. In addition, two NRs such as PPARA and NR3C2 variations were associated with certain lipid metabolite traits (Table  8). Despite the fact that AR exerts pro-inflammatory effects like PPARD and RXRA, it was much less associated with development of obesity and diabetes unlike PPARD and RXRA (Table  8). Finally, NR2F2 variations were not associated with the diseases examined except in one diabetes study.
Table 8
45 out of 48 nuclear receptors with sequence changes or mutations are associated with increased risks of human rheumatoid arthritis, obese, diabetes, and metabolic vascular diseases
Gene
Diseases
Rheumatoid arthritis
Obese
Diabetes
Vascular dis. and atherosclerosis
Phenotype ID (HGVPM)
Phenotype ID (HGVPM)
Phenotype ID (HGVPM)
Phenotype ID (HGVPM)
38
235
564
1111
1114
4
5
74
822
825
826
827
569
769
1081
1538
3639
563
568
3602
3611
3640
3616
THRA
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
   
*
*
       
THRB
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
 
*
*
       
RARA
*
 
*
*
*
*
   
*
*
*
*
*
*
*
*
 
*
*
       
RARB
*
 
*
*
       
*
*
*
*
*
 
*
*
             
RARG
     
*
*
     
*
*
*
*
 
*
*
*
             
PPARA
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
*
*
*
 
PPARD
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
PPARG
   
*
*
*
 
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR1D1
   
*
*
*
     
*
*
*
*
*
 
*
*
 
*
*
       
NR1D2
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
RORA
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
*
*
*
       
RORB
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
RORC
*
*
*
*
*
*
   
*
*
*
*
*
*
*
*
 
*
*
       
NR1H3
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR1H2
     
*
*
     
*
*
*
*
   
*
*
             
NR1H4
*
*
*
*
 
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
VDR
*
*
*
*
       
*
*
*
*
*
 
*
*
             
NR1I2
*
*
*
*
 
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR1I3
*
*
*
*
*
*
   
*
*
*
*
*
*
*
*
 
*
*
       
HNF4A
*
*
*
*
       
*
*
*
*
*
 
*
*
   
*
       
HNF4G
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
RXRA
*
*
*
*
 
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
RXRG
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR2C1
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR2C2
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR2E1
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR2E3
     
*
*
     
*
*
*
*
 
*
*
*
             
NR2F1
*
*
*
*
*
*
   
*
*
*
*
*
*
*
*
 
*
*
       
NR2F2
           
*
                               
NR2F6
     
*
*
     
*
*
*
*
 
*
*
*
             
ESR1
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
ESR2
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
ESRRA
       
*
     
*
*
*
*
 
*
*
               
ESRRB
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
ESRRG
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR3C1
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR3C2
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
*
*
*
*
PGR
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
AR
*
*
*
     
*
         
*
       
*
*
       
NR4A1
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR4A2
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR4A3
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR5A1
*
*
*
*
*
*
   
*
*
*
*
*
*
*
*
 
*
*
       
NR5A2
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
NR6A1
*
*
*
*
*
*
*
 
*
*
*
*
*
*
*
*
 
*
*
       
Nuclear receptors marked with bold have a pro-inflammatory role
Table 9
Study ID and phenotype ID from Table 8
Disease
Study ID (HGVST)
Study name (GWAS)
Phenotype ID (HGVPM)
Phenotype property
Title (phenotype HGVPM)
Rheumatoid arthritis
27
Rheumatoid arthritis
38
Anti-cyclic citrullinated peptide-positive rheumatoid arthritis
38: Stage 1 anti-CCP-positive rheumatoid arthritis status
185
Rheumatoid arthritis in the Spanish population
235
Rheumatoid arthritis
235: Rheumatoid arthritis
Obese
640
Body mass index
1111
Body mass index
1111: Phenotype method for body mass index
308
Adult body mass index in a British population
564
Body mass index
564: Adult body mass index measurement
641
Meta-analysis of 32 genome-wide association studies for waist-hip ratio adjusted for body mass index
1114
Waist-hip ratio
1114: Phenotype method for waist-hip ratio
Diabetes
463
Glycemic traits
825
Fasting glucose-related: fasting plasma glucose
825: Phenotype method for fasting glucose-related: fasting plasma glucose
463
Glycemic traits
827
Fasting glucose-related: homeostatic model assessment of beta-cell function
827: Phenotype method for fasting glucose-related: homeostatic model assessment of beta-cell function
463
Glycemic traits
822
Fasting insulin-related: fasting insulin
822: Phenotype method for fasting insulin-related: fasting insulin
463
Glycemic traits
826
Fasting insulin-related: homeostatic model assessment of insulin resistance
826: Phenotype method for fasting insulin-related: homeostatic model assessment of insulin resistance
618
Glycated hemoglobin levels
1081
Glycated hemoglobin levels
1081: Phenotype method for glycated hemoglobin levels
313
Log10 glycosylated hemoglobin in a British population
569
Log10 glycosylated hemoglobin
569: Log10 glycosylated hemoglobin measurement
433
Glucose levels 2 h after an oral glucose challenge
769
2-h glucose challenge
769: Phenotype method for 2-h glucose challenge
5
Type II diabetes mellitus
4
Type II diabetes
4: T2D status
3
Type II diabetes mellitus
5
Type II diabetes
5: T2D status
52
Type II diabetes mellitus in American Indians
74
Early onset type II diabetes mellitus
74: Phenotype method forearly onset type II diabetes mellitus
907
Proinsulin levels
1538
Proinsulin levels
1538: Phenotype method for proinsulin levels
1827
Metabolite quantitative traits
3639
a-Glucose
3639: Phenotype method for a-glucose
Metabolic vascular disease
312
Serum cholesterol levels in a British population
568
Serum cholesterol
568: Serum cholesterol measurement
1827
Metabolite quantitative traits
3602
Lipids (CH3)
3602: Phenotype method for Lipids (CH3)
1827
Metabolite quantitative traits
3611
Lipids (CH2)
3611: Phenotype method for Lipids (CH2)
1827
Metabolite quantitative traits
3616
Lipids (CH2CO)
3616: Phenotype method for Lipids (CH2CO)
1827
Metabolite quantitative traits
3640
Lipids (CH=CH*CH2CH2)
3640: Phenotype method for Lipids (CH=CH*CH2CH2)
307
Systolic blood pressure in a British population
563
Systolic blood pressure
563: Systolic blood pressure measurement
These results suggest that NRs may be very important factors in determining the susceptibility and progression of metabolic disorders including obesity, diabetes, and atherosclerosis. Also, our GWAS analysis suggests that NRs may play an important role in the progression of autoimmune disorders such as rheumatoid arthritis. Most interestingly, the NR mutations associated with various metabolic disorders and autoimmune diseases are different. This observation can be supported by multiple publications that had demonstrated NRs play an important role in immune cells. Especially, the PPARs are highly expressed in human CD4+ T cells [ 52], and the role of PPAR agonists in the treatment of autoimmune disorders had been extensively discussed [ 53, 54]. It was shown that activation of T cells was dramatically decreased in the presence of PPARA and PPARG agonists and suppressed pro-inflammatory cytokine secretion [ 52]. In addition to PPARs, other NRs such as ROR-γt were found to regulate differentiation of CD4+ T helper 17 (Th17) subset [ 55]. However, RAR/RXR dimerization exerts contrasting effects to that of ROR-γt by enhancing Foxp3 transcription factor positive inducible T-regulatory cells (Tregs) while inhibiting Th17 differentiation [ 56]. Therefore, it is evident that the cross talk between NRs play a critical role in the immunity and development of autoimmune disorders.
In addition to the GWAS analysis, we further performed the cause-effect analysis using the mouse genome informatics (MGI) database ( www.​mousemine.​org) that contains a comprehensive compilation of genomic and phenotypic data from NR transgenic and gene knockout mouse models. In Table  10, 26 NR deficiencies lead to four groups of abnormalities, including (1) hormone insufficiency/insensitivity/resistance, (2) cancers, (3) autoimmune/immunodeficiency diseases, and (4) metabolic cardiovascular diseases. The NRs in the hormone insufficiency/insensitivity/resistance group include NRs THRA, THRB, RARB, NR1H3, VDR, NR2E3, NR2F1, NR2F2, ESR1, ESRRB, NR3C1, NR3C2, PGR, AR, NR4A2, NR4A3, NR5A1, and NR0B1. The NRs in the cancer group include RARA (acute promyelocytic leukemia), NR1H4 (hepatocellular carcinoma), ESR1 (breast cancer), and AR (prostate cancer). The NRs in the autoimmune/immunodeficiency disease group include PPARG (systemic lupus erythematosus), RORC (immunodeficiency), and RXRA (systemic lupus erythematosus). The NRs in the metabolic cardiovascular diseases group include RARA (cardiomyopathy), PPARD (diabetes mellitus), PPARG (diabetes mellitus, lipodystrophy, obesity, pulmonary hypertension), HNF4A (diabetes mellitus), NR2F2 (congenital heart defects), ESR1 (myocardial infarction), NR3C2 (hypertension), AR (obesity), and NR0B2 (obesity). Taken together, the results of the GWAS association studies and MGI casual analysis suggest that NRs play significant roles in maintaining the homeostasis and suppressing various hormonal diseases, cancers, autoimmune diseases/immunodeficiency, and metabolic cardiovascular diseases.
Table 10
The Mouse Genome Informatics (MGI) database shows that 26 nuclear receptor deficiencies lead to abnormal metabolism and endocrine and cardiovascular phenotypes in mice
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab10_HTML.gif
#Disease association of human genes are from the NCBI mim2gene_medgen file and include annotations from OMIM, NCBI curation, Gene. *Abnormal mouse phenotypes. Of note, red fonts indicate cancers, blue fonts indicate autoimmune diseases, and green fonts indicate metabolic disorders such as obesity
To further determine whether many NRs serve as homeostasis-associated molecular pattern receptors (HAMPRs) by inhibiting inflammation, we conducted an extensive literature survey to find out experimentally validated data to prove our hypothesis. As shown in Table  11, the level of 10 hormone ligands of NRs was changed with inflammatory diseases. The ligands of class-I thyroid hormone receptor-like group including vitamin A, fatty acids, and prostaglandins levels were reduced in the presence of inflammatory disorders, suggesting that they have the potential to exert anti-inflammatory effects. In addition, retinoids and estrogen inhibited inflammatory intestinal disease and atherosclerosis respectively. Moreover, testosterone suppressed Crohn’s disease.
Table 11
Hormone ligand level changes are associated with inflammatory diseases
NRNC symbol
Ligand(s)
Inflammatory disease
Ligand level change
PMID
Class I—thyroid hormone receptor-like
 NR1A1
Thyroid hormone
Inflammatory bowel diseases
8562993
 NR1A2
 NR1B1
Vitamin A
Chronic obstructive pulmonary disease
26339144
 NR1B2
 NR1B3
 NR1C1
Fatty acids, prostaglandins
Inflammatory bowel disease
27631140
 NR1C2
 NR1C3
 NR1F1
Cholesterol
Atherosclerotic cardiovascular disease
21686232
 NR1F2
 NR1F3
 NR1H3
Oxysterols
Inflammatory bowel diseases
24024145
 NR1H2
 NR1H4
Class II—retinoid X receptor-like
 NR2B1
Retinoids
Inflammatory intestinal disease
23690441
 NR2B2
 NR2B3
Class III—estrogen receptor-like
 NR3A1
Estrogens
Atherosclerosis
12816884
 NR3C1
Cortisol
Obesity
12466357
 NR3C2
Aldosterone
Renal fibrosis
26730742
 NR3C4
Testosterone
Crohn’s disease
26020563
Finally, we also searched for the evidence in the literature where gene knockout and activation approaches of NRs were used to determine the pathological phenotypes. Twelve out of 15 NRs including NR1A1, NR1C3, NR1D1, NR1H3, NR1H2, NR1H4, NR2F2, NR3A1, NR3B2, NR4A1, NR4A3, and NR0B2 have anti-inflammatory roles as shown in Table  12. The three NRs NR1C2, NR2B1, and NR3C4 did not show any anti-inflammatory properties. Taken together, these results suggested that most human and mouse NRs have anti-inflammatory functions in various tissues and cell types.
Table 12
12 out of 15 nuclear receptors have anti-inflammatory roles reported in the literature
Gene name (full name)
NRNC symbol
Tissue/cell type
Purpose
Treat
Suppressed
Induced
PMID
Inflam.
Cytokines/signaling
Class I—thyroid hormone receptor-like
 THRA
NR1A1
Aorta macrophages
Atherosclerosis
KO
IL-1β, NFκB, TNF-α
24797634
Anti
 PPARG
NR1C3
Mouse cancer model
Tumor growth and angiogenesis
Act
IL-17
23619236
Anti
 NR1D1
NR1D1
Peritoneal macrophages
Aging- or obesity-associated impairment of clockwork and inflammation
Act
Ccl2, ERK, p38
24307731
Anti
Mice macrophages
Circadian clockwork and inflammatory disease
KO
IL-6
22184247
 NR1H3
NR1H3
Mice plasma and kidney
Normal and diabetic kidney
KO
Nox2, Ncf1, MDA, TLR2, ICAM1, IL-1β, CD68
24201575
Anti
 NR1H2
NR1H2
Mice plasma and kidney
Normal and diabetic kidney
KO
Nox2, Ncf1, TLR2, ICAM1, IL-1β, CD69, MDA(urinary)
24201575
Anti
Macrophage cell line
LPS treat
Act
TNF-α, IL-1β, IL-6, IL-12p40
23099324
ob/ob mouse liver
Cellular lipid metabolism
Block
Cox-2, MCP-1, MIP-2
24206663
 NR1H4
NR1H4
Obese mice liver
Obesity-related metabolite disorder
Ace
Mmp13, Cxcl2, Cxcl8, Cxcl14, IL-1β, IL-6, TNF-α
25425577
Anti
 PPARD
NR1C2
Epithelial cells
 
Act
COX-2
24763687
Pro
Class II—retinoid X receptor-like
 RXRA
NR2B1
Spleen macrophages
Age-related disease
Act
COX-2, NF-kB, IL-6
24051096
Pro
 NR2F2
NR2F2
Prostate cancer
Prostate cancer
Act
TGF-β
23201680
Anti
Class III—estrogen receptor-like
 ESR1
NR3A1
Male mice
Obesity
KO
IL-10
IL-1β, TNF-α, IL-6
25373903
Anti
Astrocytes
Neuroprotective
Act
CCL2, CCL7
23804112
 ESR3
NR3B2
Mice
Intestine tumor
KO
TGF-β
24104551
Anti
 AR
NR3C4
Prostate cancer cells
Prostate tumorigenesis
KD
AKT
25527506
Pro
Hepatocellular carcinoma cells
Cell adhesion and migration
KO
PI3K/AKT
24944078
Class IV—nerve growth factor IB-like
 NR4A1
NR4A1
Macrophages
Atherosclerotic lesions
KO
IL-4
23288947
Anti
Bone marrow-derived macrophages (BMM)
Atherosclerotic lesions
KO
IL-12, IFN-δ, SDF-1α
22194623
Macrophage
Atherosclerotic lesions
KO
TNF-α, TLR-4, NFκB
22194622
 NR4A3
NR4A3
Mast cells
Vascular biology and inflammation
KO
IL-13, MCP-1, TNF-α
24586680
Anti
Hematopoietic stem cells
Atherosclerotic lesions
KO
Ly6C(+) monocytes
24806827
Endothelial cells
Atherosclerotic lesions
KO
VCAM-1, ICAM-1
20558821
Class O—miscellaneous
 NR0B2
NR0B2
Mice kidney
Inflammasome
KO
IL-1β, IL-18, NLRP3, ASC
25655831
Anti
Abbreviations: KO knockout, Act activation, Ace acetylation, Cxcl Cxc ligand, IL interleukin, MCP monocyte chemotactic protein, Mmp matrix metallopeptidase, TLR Toll-like receptor, VCAM vascular cell adhesion molecule, ICAM intercellular adhesion molecule, Inflam inflammation, Anti anti-inflammatory, Pro pro-inflammatory

Nuclear receptors have the tendency to be downregulated than being upregulated in autoimmune and metabolic diseases and cancers

In order to determine the overall roles of NRs in modulating the pathogenesis of human autoimmune diseases, metabolic diseases, and cancer, we examined the expression changes of 48 NRs in eight human diseases using the microarray datasets ( https://​www.​ncbi.​nlm.​nih.​gov/​gds/​) deposited by other investigators in the NIH-GEO dataset database. The microarray datasets we analyzed were conducted on various pathological settings including autoimmune disease rheumatoid arthritis, and five metabolic diseases such as familial hypercholesterolemia, type 2 diabetes, type 1 diabetes, obesity, hyperhomocysteinemia, and also hypertension. We analyzed The Cancer Genome Atlas (TCGA) database to determine NR expression changes in human cancers.
As shown in Table  13 (A), three NRs were upregulated but nine NRs were downregulated in the synovial tissue of patients with rheumatoid arthritis. Similarly, in Table  13 (B), 7 NRs were upregulated and 11 NRs were downregulated in T cells from patients with familial hypercholesterolemia. Also, we analyzed the monocytes isolated from patients with familial hypercholesterolemia, peripheral blood from patients with metabolic syndrome, arterial tissue from patients with type 2 diabetes, peripheral blood mononuclear cells from patients with type 1 diabetes, adipose stem cells and omental adipose tissue from morbidly obese patients, aortic smooth muscle cells from patients with hyperhomocysteinemia, and carotid artery atheromatous plaques from patients with hypertension. The results showed that NRs have the tendency to be downregulated during metabolic disorders and autoimmune disorders rather than being upregulated. However, this trend was not observed in morbidly obese patients where equal numbers of NRs were upregulated and downregulated (Table  13 (B)). To further consolidate the finding, we analyzed the NR expression changes in the presence of proatherogenic stimulus oxidized low-density lipoprotein (Ox-LDL) in human aortic endothelial cells (HAECs). This analysis also showed that NRs tend to be downregulated than upregulated with prolonged Ox-LDL treatment (Table  13 (C)). Taken together, these results suggested that NRs have the tendency to be downregulated than upregulated during human autoimmune rheumatoid arthritis and metabolic diseases, and this tendency of NRs was more obvious in autoimmune arthritis than in metabolic diseases.
Table 13
Nuclear receptors are more downregulated than upregulated in human diseases
Disease
Tissue/cell type
Number
Upregulated gene
 
Downregulated gene
 
PMID/GEO ID
Up
Down
         
A. Nuclear receptors have the tendency to be downregulated than being upregulated in rheumatoid arthritis
         
Fold*
 
Fold*
 
         
 
 
Rheumatoid arthritis
Synovial tissue
3
9
NR1H3
2.08
NR1A1
−20
24690414/GSE55235
NR1I1
2.12
NR1C3
−2.56
NR3A1
2.59
NR1D1
−20
   
NR2F1
−2.94
   
NR3C3
−2
   
NR3C4
−2.22
   
NR4A1
−4.76
   
NR4A2
−10
   
NR4A3
−3.85
B. Nuclear receptors are more downregulated than upregulated in metabolic diseases in humans
         
Fold**
 
Fold**
 
         
 
 
Family hypercholesterolemia
T cells
7
11
NR1B3
1.41
NR1A1
−1.3
–/GSE6088
NR1C3
1.94
NR1B1
−1.22
NR1I1
1.75
NR1B2
−1.72
NR3C4
1.97
NR1C1
−1.72
NR4A2
2.03
NR1F1
−2
NR4A3
1.5
NR1F2
−1.79
NR0B1
2.36
NR1H3
−1.28
   
NR3B3
−2.08
   
NR3C1
−1.22
Family hypercholesterolemia
Monocytes
10
12
NR1B1
1.23
NR1A1
−2.27
19040724/GSE6054
NR1F3
2.38
NR1B2
−1.72
NR1I1
1.31
NR1C1
−1.59
NR2F6
2.61
NR1F1
−1.52
NR3B2
1.33
NR1H3
−1.28
NR4A1
1.94
NR2A2
−2.13
NR4A2
2.06
NR2C1
−1.47
NR5A1
2.1
NR2C2
−1.19
NR6A1
1.62
NR2E3
−2.63
NR0B1
2.02
NR3A1
−2
   
NR3A2
−1.79
   
NR3C1
−1.28
Metabolic syndrome
Peripheral blood
0
1
   
NR4A3
−1.54
21368773/GSE23561
Type 2 diabetes
Arterial tissue
0
2
   
NR1B2
−1.28
22340758/GSE13760
   
NR3C3
−1.12
Type 1 diabetes
Peripheral blood mononuclear cell
1
2
NR3C4
1.2
NR1F1
−1.89
–/GSE55100
   
NR4A3
−1.2
Morbidly obese
Adipose stem cells
3
3
NR4A1
8.06
NR1A1
−1.2
24040759/GSE48964
NR4A2
12.64
NR1D1
−1.2
NR4A3
4.44
NR2C2
−1.25
Morbidly obese
Human omental adipose tissue
1
1
NR4A2
3.5
NR2B3
−2.44
20678967/GSE15773
Homocysteine (100 μM)
Human aortic smooth muscle cells
3
3
NR2B3
1.2
NR1H3
−1.33
18602108/GSE9490
NR3A2
1.61
NR2F2
−1.7
NR4A3
1.49
NR4A2
−1.24
Hypertension
Carotid artery atheromatous plaques
0
2
   
NR1A2
−2.04
23660665/GSE43292
   
NR3C3
−2
C. Nuclear receptors are significantly downregulated than upregulated in human aortic endothelial cells (HAECs) treated with oxidized low-density lipoproteins (Ox-LDLs) in a time-dependent manner
         
Fold*
 
Fold*
 
         
 
 
Treated with Ox-LDL for 6 h
HAEC
4
3
NR1I2
2.18
NR1B2
−3.57
19279231/GSE13139
NR2A2
3.8
NR1F1
−5.26
NR3A2
3.04
NR4A1
−2.56
NR5A2
4
   
Treated with Ox-LDL for 12 h
0
7
   
NR1B1
−2.86
   
NR1B2
−5.56
   
NR1F1
−2.86
   
NR1H4
−5.88
   
NR2A2
−2.04
   
NR3A1
−2.94
   
NR3C3
−2.33
Treated with Ox-LDL for 24 h
5
7
NR1C1
2.24
NR1B1
−2.56
NR1I1
2.01
NR1B2
−2.17
NR3A2
5.09
NR1B3
−2.04
NR3B2
2.34
NR1H4
−4.55
NR5A2
5.81
NR1I2
−2.5
   
NR2A2
−2.13
   
NR2F6
−4.55
Abbreviations: HAECs human aortic endothelial cells, Ox-LDL oxidized low-density lipoprotein
*Fold change > 2
**Fold change > 1.2
Specifically, our data shows that NR1C1 (PPARα) is among the downregulated genes in familial hypercholesterolemia. NR1C1 is one of the primary modulators in fatty acid oxidation and apolipoprotein synthesis [ 23]. This receptor was also found abundantly in the vascular wall and in human macrophages and was shown to exert anti-inflammatory and anti-atherogenic effects [ 57]. Therefore, downregulation of this gene may contribute to hypercholesterolemia and also to progression of atherosclerotic events. PPARα agonists are widely used to correct hyperlipidemia and were shown to reduce mortality and morbidity due to cardiovascular events [ 58]. Furthermore, we observed that NR1C3 (PPARγ) is downregulated in patients with rheumatoid arthritis. Previously, PPARγ was reported to have a negative effect on oxidative stress, and therefore, it was suggested that concomitant use of PPARγ agonists with other treatments will give additional therapeutic benefits against rheumatoid arthritis [ 59].
NRs play an important role in the development and progression of cancers. For an example, the roles of androgen receptors in breast and prostate cancers are well documented [ 6062]. We analyzed the NR expression in 17 different types of cancers in TCGA database. Similar to the observation we elaborated above, our data revealed that the tendency of NRs to be downregulated is more than being upregulated. NR1H2 receptor was downregulated in as many as seven types of cancers, NR2B2 in six types, and NR1B1 and NR1A1 in five types of cancers (Table  14). However, specifically NR1A1 (RAR α) and also NR1B1 (RAR β) are associated with progression of estrogen-dependent breast cancers [ 63]. This is contrasting to our observation of the expression of these two receptors in other types of cancers. Nevertheless, activation of NR1H2 which falls in to liver X receptors was shown to inhibit proliferation of HT29 colorectal cancer cells [ 64]. Therefore, this suggests that NR1H2 can be a potential therapeutic target for the treatment of many types of cancers.
Table 14
More nuclear receptors are downregulated in 17 different types of human cancers
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab14_HTML.gif
The numbers in the cells represent fold change (≥ 1.5); positive symbol means upregulation while negative symbol means downregulation. Red color means significant upregulation while blue color means significant downregulation ( p < 0.05). No color means no significance ( p ≥ 0.05), and blank means no data is available
Abbreviations: TCGA The Cancer Genome Atlas, Pro provisional, AML acute myeloid leukemia, AC adrenocortical carcinoma, BUC bladder urothelial carcinoma, EC esophageal carcinoma, LHC liver hepatocellular carcinoma, LA lung adenocarcinoma, LSCC lung squamous cell carcinoma, Mes mesothelioma, PA pancreatic adenocarcinoma, PTC papillary thyroid carcinoma, PP pheochromocytoma and paraganglioma, PRA prostate adenocarcinoma, Sar sarcoma, SCM skin cutaneous melanoma, SA stomach adenocarcinoma, TGCC testicular germ cell cancer, UCEC Uterine Corpus Endometrial Carcinoma
*Reference
To determine the features of those human diseases-modulated NRs, we performed Venn analysis as we previously reported [ 15]. The Venn analysis/diagram is a very useful analytical tool as it helps to clearly visualize the NRs that are shared between the different diseases analyzed. In Fig.  6a, b, the results show that NR expression changes in human diseases are not shared. In four human diseases analyzed by the Venn analysis, 13 NRs were upregulated, 20 NRs were downregulated, and 15 NRs were not changed in their expression levels (Fig.  6c). Of note, 10 out of 13 upregulated NRs in human diseases were from the scarcely distributed group shown in Table  6, 11 out of 20 NRs downregulated in human diseases were from the very highly distributed and highly distributed groups in Table  6, and 13 out of 15 NRs whose expressions were not changed in human diseases were from the moderately distributed and scarcely distributed groups shown in Table  6. Notably, the tissue expression of 3 NRs out of 20 disease-mediated downregulated NRs including NR1C1, NR1H3, and NR1C3 were correlated with tissue hypomethylated index SAH levels (Fig.  5b), 4 out of 15 NRs whose expressions were not changed were correlated with hypomethylated index SAH levels, and none of the NRs in the disease-upregulated group were correlated with hypomethylated index SAH levels (Fig.  6c). These findings are in a good correlation with tissue hypomethylation function in promoting inflammation as we reported [ 32, 65], suggesting that hypomethylation-promoting hyperhomocysteinemia may facilitate inflammation via inhibiting the expression of those human disease-downregulated NRs and also keep the stable expression of those non-disease-changed NRs.
In order to determine functional significances of disease-modulated NR expression, we analyzed the top 10 signaling pathways with the disease-upregulated NRs, disease-downregulated NRs, and non-disease-modulated NRs using the Ingenuity Pathway Analyzer ( https://​www.​qiagenbioinforma​tics.​com/​products/​ingenuity-pathway-analysis/​). As shown in Fig.  6d; in addition to the shared pathways among three groups of NRs, two pathways, Wnt/β-catenin signaling, and Nur77 signaling were specifically associated with the disease-upregulated NRs; two other pathways such as peroxisome proliferator-activated receptor (PPAR) signaling and thyroid hormone receptor (TR)/retinoid X receptor (RXR) activation were specifically associated with the downregulated NRs; and four pathways including LPS/IL-1 inhibition, pregnane X receptor (PXR)/RXR activation, liver X receptor (LXR)/RXR activation and 1α, and 25-dihydroxyvitamin D3 (vitamin D3) receptor (VDR)/RXR activation were specifically associated with the non-disease-modulated NRs. These results have provided novel insight on the potential functions of various NRs in modulating the pathogenesis of human autoimmune arthritis and metabolic diseases.
Since we found that upregulation and downregulation of certain NRs can be shared in several human diseases (Fig.  6a), we examined whether the NRs shared in human diseases can be used as biomarkers for the diseases and complications. To test this issue, we organized the analysis results in Fig.  6e. The results showed that upregulation of three NRs such as NR1H3, NR1I1, and NR3A1 can be used as biomarkers for rheumatoid arthritis and that upregulation of NR1I1 alone and downregulation of only two NRs NR1A1 and NR3C3 can be used as potential biomarkers for rheumatoid arthritis with familial hypercholesterolemia. In addition, upregulation of NR3C4 can be used as potential biomarker for type 2 diabetes whereas downregulation of two NRs including NR3C3 and NR4A3 can only be used as biomarkers for diabetes with rheumatoid arthritis as a complication. Moreover, upregulation of three NRs such as NR4A1, NR4A2, and NR4A3 can be used for the potential biomarkers for obesity while downregulation of NR1D1 can be used as a potential biomarker for obesity with rheumatoid arthritis as a complication. Finally, downregulation of NR1A1 alone can be used as the biomarker for obesity complicated with rheumatoid arthritis and familial hypercholesterolemia. The results suggest that the NRs shared in human diseases may be highly valuable in serving as potential biomarkers for detection of autoimmune arthritis, metabolic diseases, and their complications.

The expression of nuclear receptors are regulated by numerous inflammation-modulating pathways and mitochondrial energy metabolic enzymes

We then hypothesized that the expression of NRs is regulated by numerous inflammation-modulating pathways. To test this hypothesis, we examined the expression of NRs in various gene-deficient mouse models and cells with overexpression of genes of interests. First, two NRs such as Nr1h3 and Nr1i1 were found to be upregulated, and four other NRs were downregulated in the aortic arch in apolipoprotein E (ApoE)−/− mice fed with 24 weeks of high fat diet (Table  15). However, only one NR, Nr2f1, was found to be downregulated in the aortic arch of ApoE−/− mice fed with 8 weeks of high fat diet. These findings suggest that NR modulation in ApoE−/− in the aortic arch requires prolonged high fat diet feeding.
Table 15
A large nuclear receptor is downregulated in proatherogenic mouse models ApoE−/−, LDL-R−/−, and type 2 diabetes mouse model db/db
Disease
Tissue/cell type
Number
Upregulated gene
Fold*
Downregulated gene
Fold*
PMID/GEO ID
Up
Down
 
 
ApoE−/− 8-week HFD
Aortic arch
0
1
   
Nr2f1
−1.2
20577049/GSE18443
ApoE−/− 24-week HFD
2
4
Nr1h3
1.26
Nr1a2
−1.25
   
Nr1i1
1.27
Nr1b2
−1.2
       
Nr1d1
−1.28
       
Nr3a1
−1.22
LDL-R−/− VS. WT
Macrophages of aorta
2
9
Nr1c3
1.29
Nr1a2
−1.3
21868699/GSE24342
Nr3a1
1.3
Nr1b2
−1.2
   
Nr1d2
−1.28
   
Nr1i1
−1.3
   
Nr2b3
−1.25
   
Nr3c1
−1.2
   
Nr3c4
−1.27
   
Nr4a1
−1.32
   
Nr4a2
−1.28
db/db VS. WT
Glomerular endothelial cell
1
3
Nr1b2
1.74
Nr1i1
−1.47
20706631/GSE21324
   
Pgr
−1.52
   
Ar
−1.67
Abbreviations: WT wild type, ApoE−/− apolipoprotein E-deficient mice; LDL-R−/− low-density lipoprotein receptor deficient mice, db/db mice leptin receptor gene mutant mice, HFD high fat diet, VS. versus
*Fold change > 1.2
In another study, it was shown that expression of NRs were upregulated and nine NRs were downregulated in aortic macrophages of low-density lipoprotein receptor (LDL-R)-deficient mouse aortic macrophages relative to wild type (Table  15). In a separate study, one NR Nr1b2 was found to be upregulated but three NRs were downregulated in diabetic db/db glomerular endothelial cells (Table  15). These results suggest that once again in proatherogenic models and type 2 diabetes model, there is a less tendency for NRs to be upregulated than downregulated.
Second, we examined whether inflammatory cytokine signaling pathways can downregulate NR expression. In Table  16, in interferon-γ (IFN-γ)-stimulated endothelial cells, interleukin-1β (IL-1β)-stimulated endothelial cells, IL-1β-stimulated human peripheral mononuclear cells (PBMCs) and tumor necrosis factor-α (TNF-α)-stimulated PBMCs, the NR expressions were either upregulated and downregulated in similar numbers or less upregulated than downregulated.
Table 16
Pro-inflammatory cytokine signaling negatively regulates the expression of nuclear receptors
Disease
Tissue/cell type
Number
Upregulated gene
Fold*
Downregulated gene
Fold*
PMID/GEO ID
Up
Down
IFN-γ stimulation
EC
2
2
NR2F1
1.39
NR2B1
−1.26
19553003/GSE3920
     
NR3C1
1.53
NR2F6
−1.36
IL-1β stimulation
EC
3
7
NR1H4
5.32
NR1B2
−3.84
21469100/GSE19240
NR3A1
2.21
NR1B3
−4.93
NR5A2
6.57
NR1F1
−4.72
   
NR1F2
−2.58
   
NR3A2
−4.94
   
NR3B3
−11.03
   
NR4A1
−3.43
Human PBMCs-IL-1β and TNF-α stimulations
IL-1β 2 h
PBMC
0
3
   
NR1A1
−14.93
23104095/GSE40838
   
NR1I1
−5.1
   
NR3A2
−6.54
IL-1β 6 h
0
1
   
NR5A2
−23.26
TNF-α 2 h
1
1
NR1B2
38.32
NR2A1
−21.11
TNF-α 6 h
2
3
NR1B2
27.28
NR1A2
−23.92
NR3B3
4.92
NR1B3
−16.22
   
NR4A1
−3.86
Abbreviations: IFN-γ interferon gamma, IL-1β interleukin-1β, TNF-α tumor necrosis factor-α-like, PBMCs peripheral blood mononuclear cells
Third, in Table  17, we examined whether anti-inflammatory cytokine pathways and inhibition of the pro-inflammatory transcription factor regulate NR expressions. We observed that NR expressions were modulated in hepatocellular carcinoma cells stimulated with transforming growth factor-β (TGF-β), palatal mesenchyme cells from TGF-β knockout (KO) mice, in conventional T cells stimulated with anti-CTLA-4 (cytotoxic T-lymphocyte-associated protein 4, also known as CD152, a T cell co-suppressor) antibody, in regulatory T cells (Tregs) stimulated with anti-CTLA-4 antibody and in hearts extracted from cardiac-specific transgenic PPARα mice. In a study with NF-kB inhibitor-treated cells, six NRs were upregulated and five NRs were downregulated. These results demonstrated that immune suppressor pathways CTLA-4, NF-kB inhibitor, and Treg suppress inflammation by significantly upregulating the expression of nine NRs including NR4A1 (5.6–8.3 folds), NR4A2 (6.7–15.8 folds), NR4A3 (4.6–8.1 folds), NR1B2 (6.7 folds), NR1D1 (3.4 folds), NR2A2 (3.2 folds), NR1H4 (2.7 folds), NR2C1 (5.4 folds), and NR3A2 (4.2 folds).
Table 17
Anti-inflammatory cytokine signaling and Tregs positively regulate the expression of nuclear receptors
Disease
Tissue/cell type
Number
Upregulated gene
Fold*
Downregulated gene
Fold*
PMID/GEO ID
Up
Down
TGF-β stimulation
HCC Huh-7 cells
8
7
NR1B1
1.19
NR1B2
−1.21
19723656/GSE10393
NR1H2
1.37
NR1C3
−1.23
NR2B1
1.75
NR1D2
−1.35
NR2B2
1.23
NR1F1
−1.42
NR2F1
1.27
NR1H4
−1.45
NR2F2
1.36
NR3C1
−1.21
NR2F6
1.37
NR5A2
−1.29
NR0B2
1.59
   
TGF-β KO
PM cells
2
3
NR2C2
1.21
NR2C1
−1.26
23975680/GSE46150
NR3C1
1.23
NR2F6
−1.3
   
NR3C4
−1.67
Tconv stimulate by anti-CTLA-4
Spleen and lymph node
7
3
NR1D2
1.69
NR1B3
−1.14
23277554/GSE42267
NR1H3
1.2
NR2B1
−1.18
NR2C2
1.23
NR2C1
−1.28
NR3C4
1.64
   
NR4A1
5.61
   
NR4A2
15.78
   
NR4A3
8.12
   
Treg stimulated with anti-CTLA-4
Spleen and lymph node
5
4
NR1D2
1.53
NR1B1
−1.25
23277554/GSE42267
NR1F1
2.4
NR1B3
−1.32
NR3C4
2.36
NR1F1
−1.23
NR4A1
3.3
NR3A1
−1.23
NR4A2
6.67
   
Cardiac-specific transgenic (Tg-PPARα) mice
Heart
5
13
NR1F2
1.22
NR1A1
−1.27
22055503/GSE33101
NR1H3
1.46
NR1C1
−1.67
NR1I2
0.83
NR1F1
−3.84
NR2A1
1.24
NR1F3
−2
NR0B2
 
NR2B1
−1.22
   
NR2B2
−1.22
   
NR2B3
−1.59
   
NR2F2
−1.22
   
NR2F6
−1.27
   
NR3B1
−1.22
   
NR3B2
−1.23
   
NR3C1
−1.25
   
NR3C3
−1.27
NFκB inhibitor
4 h
EKC
6
5
NR1B1
2.33
NR1C2
−2.81
15722350/GSE2489
NR1B2
6.73
NR1F1
−6.54
NR1D1
3.36
NR1I3
−2.93
NR2A2
3.16
NR3A2
−2.91
NR2C1
2.01
NR5A2
−5.17
NR4A1
8.34
   
48 h
4
4
NR1H4
2.71
NR1C3
−5.46
NR2C1
5.35
NR2A1
−3.63
NR3A2
4.2
NR2F1
−14.03
NR4A3
4.56
NR3B3
−4.82
Abbreviations: HCC hepatocellular carcinoma cells, TGF-β transforming growth factor-β, PM palatal mesenchyme, Tconv conventional T cells, Treg regulatory T cell, EKC epidermal keratinocytes
Fourth, in Table  18, we examined whether a key enzyme of tricarboxylic acid (TCA) cycle, isocitrate dehydrogenase (IDH), regulates the expression of nuclear receptors. The results showed that IDH mutation in isogenic epithelial cells results in significant upregulation of six NRs and downregulation of eight NRs. Fifth, in Table  19, we examined whether four key enzymes of the mitochondrial respiratory chain including nicotinamide adenine dinucleotide (quinone) (NADH) dehydrogenase (Nd2), succinate dehydrogenase (SDH), cytochrome c oxidase-4 (COX4), and mitochondrial respiratory chain complex IV regulate the expression of nuclear receptors. The results showed that the mutations of these enzymes result in significant changes in the expression of NR2F2 (Nd2 mutation induced sevenfold upregulation), NR5A2 (Nd2 mutation induced 27-fold downregulation), NR1A2 (COX4 mutation induced 46.5-fold upregulation), and NR2F6 (Cox4 mutation induced 3.3-fold downregulation). Taken together, these results suggest that the expressions of nuclear receptors are regulated by numerous inflammation-modulating pathways and mitochondrial energy metabolic enzymes.
Table 18
A key enzyme of tricarboxylic acid (TCA) cycle, isocitrate dehydrogenase, regulates the expression of nuclear receptors
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab18_HTML.gif
Table 19
Key enzymes of the mitochondrial respiratory chain regulate the expression of nuclear receptors
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab19_HTML.gif

Innate immune sensor inflammasome/caspase-1 pathway plays a critical role in regulating the expression of most nuclear receptors

In Fig.  4, we found that innate immune sensor PRRs such as NOD1, NOD2, NOD4, and IFI16 may either act as upstream regulators or downstream targets of NRs in tissues and that the NLR-mediated regulation on NR expression in tissues are evolutionally conserved and mainly act toward suppression of NRs during microbial infection-triggered inflammations. To consolidate this finding, we determined whether gene deficiencies of caspase-1 and other inflammasome components affect the expression of NRs.
As shown in Table  20, deficiency of caspase-1 in ApoE−/− mouse aorta, adipose tissue, deficiency of caspase-1 in associated speck-like protein containing a CARD (ASC)−/− background, deficiency of histone deacetylase and caspase-1 substrate sirtuin 1 (Sirt1) in Tregs, and deficiency of NLRP3 in adult and children PBMCs led to mostly upregulation of NRs instead of downregulation of NRs. For example, the deficiency of NLRP3 led to upregulation of 26 NRs (54%) but downregulation of 9–11 NRs (19–23%). These results suggest that caspase-1/NLRP3 inflammasome pathways play a critical role in regulating the expression of NRs. In addition, in Table  21, we also noticed that the inflammasome/caspase-1 deficiencies upregulated 29 NRs (60%), downregulated 10 NRs (21%), but did not change the expression of 9 NRs (19%).
Table 20
Nuclear receptors are significantly changed in caspase-1 and Sirt1 knockout mice, indicating that caspase-1-Sirt1 pathway negatively regulates nuclear receptor expression
Disease
Tissue/cell type
Number
Upregulated gene
Fold*
Downregulated gene
Fold*
PMID/GEO ID
Up
Down
AopE−/−/Casp1−/− vs. ApoE−/−
Aorta
3
0
NR1A2
1.22
   
GSE72448
NR1D2
1.17
   
NR2C2
1.19
   
Adipose
10
1
NR1A1
1.35
NR6A1
−1.32
NR1C3
1.84
   
NR1F1
1.78
   
NR1I3
1.22
   
NR2A1
2.24
   
NR2B1
1.52
   
NR3A1
1.39
   
NR3C1
1.41
   
NR3C4
1.66
   
NR4A2
1.16
   
Casp1−/−/ASC−/− vs. ASC−/−
White adipose tissue
7
0
NR1A1
1.35
   
21876127/GSE25205
NR1C3
1.84
   
NR1I3
1.22
   
NR2A1
1.17
   
NR2B1
1.52
   
NR3C4
1.65
   
NR4A2
1.16
   
Sirt1−/− vs. WT
Treg
4
0
NR1B1
1.2
   
21199917/GSE26425
NR1F1
1.29
   
NR2F6
1.22
   
NR4A3
1.29
   
Cardiac-specific transgenic (Tg-Sirt1) mice
Heart
2
6
NR3C4
1.52
NR1B1
−3.57
22055503/GSE33101
NR0B2
1.35
NR1B3
−1.25
   
NR1F3
−2
   
NR1I1
−1.27
   
NR2B3
−1.22
   
NR2C1
−1.22
NLRP3 mutation
Adult control
PBMC
26
9
NR1B1
1.22
NR1F1
−2.93
–/GSE43553
NR1B2
1.47
NR1F2
−1.4
NR1B3
1.43
NR2C1
−1.93
NR1C2
1.18
NR3C1
−1.38
NR1C3
1.2
NR3C2
−1.68
NR1I1
1.73
NR4A1
−1.32
NR1I2
1.2
   
NR2A1
1.53
   
NR2B3
1.25
   
NR2E1
1.31
   
NR2E3
1.57
   
NR2F1
1.2
   
NR3A1
1.61
   
NR3A2
1.28
   
NR3B1
1.34
   
NR4A3
1.27
   
NR5A1
1.42
   
NR6A1
1.3
   
NR0B2
1.34
   
Children control
26
11
NR1A1
1.4
NR1D1
−1.82
NR1A2
1.24
NR1D2
−1.56
NR1B1
1.31
NR1F1
−2.53
NR1B2
1.51
NR2B2
−1.44
NR1B3
1.49
NR2C1
−2.25
NR1C3
1.32
NR2C2
−1.29
NR1I1
1.69
NR3C1
−1.3
NR1I2
1.27
NR3C2
−1.53
NR2A1
1.66
NR4A1
−1.37
NR2A2
1.22
NR4A2
−4.23
NR2E1
1.21
   
NR2E3
1.55
   
NR2F1
1.24
   
NR2F6
1.26
   
NR3A1
1.23
   
NR3A2
1.41
   
NR3B1
1.65
   
NR3C3
1.21
   
NR4A3
1.23
   
NR5A1
1.41
   
NR6A1
1.29
   
NR0B2
1.34
   
Abbreviations: ApoE−/− apolipoprotein E-deficient mice, Casp1−/− caspase-1-deficient mice, HFD high fat diet, ASC−/− PYD and CARD domain-containing deficient mice, Sirt 1−/− sirtuin 1-deficient mice; WT wild-type mice, NLRP3 NLR family pyrin domain containing 3 deficient mice; vs. versus
Table 21
The expression changes of NRs in the presence of inflammasome/caspase-1 deficiencies
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab21_HTML.gif
Moreover, we noticed that among top 10 pathways identified with the Ingenuity Pathway Analysis for the inflammasome/caspase-1 deficiency-upregulated NRs, three pathways including aryl hydrocarbon receptor signaling, RAR activation, and estrogen receptor signaling were unique. Similarly, among the top 10 pathways identified with the Ingenuity Pathway Analysis for the inflammasome/caspase-1 deficiency-downregulated NRs, seven pathways including circadian rhythm signaling, thyroid cancer signaling, Nur77 signaling in T lymphocytes, calcium-induced T-lymphocyte apoptosis, melatonin signaling, T helper cell differentiation, and non-small cell lung cancer signaling were specific. Furthermore, among the top 10 pathways identified with the Ingenuity Pathway Analysis for the inflammasome/caspase-1 deficiency-non-changed NRs, three pathways including LPS/IL-1-mediated inhibition of RXR function, LXR/RXR activation, and Toll-like receptor signaling were unique. Therefore, these pathways may not play a significant role in progression of inflammatory pathologies. Taken together, these results suggest that inflammasome/caspase-1 pathway deficiencies regulate the expressions of most NRs (81%) and that inflammasome/caspase-1 innate immune sensors control the expression of most NRs.

We propose a new paradigm that most nuclear receptors are anti-inflammatory HAMPs for regulating the balance of inflammation, inhibition of inflammation, and resolution of inflammation

HAMPs (homeostasis-associated molecular pattern molecules), the new concept we proposed, are designated for mitigating the progression of inflammation or inhibition of inflammation under sterile inflammation. These HAMP receptors initiate anti-inflammatory/homeostatic signaling and promote inflammation resolution [ 5]. Since most endogenously metabolite-nuclear receptor signals inhibit inflammation and maintain the tissue homeostasis, we propose that most NRs act as HAMP receptors. To consolidate this new hypothesis, we conducted an extensive literature search (Parts 2, 3, and 4 in Fig.  1). We found the following supporting evidences.
The first supporting evidence for classifying most of nuclear receptors as HAMP receptors is presented in Tables 8, 9, and 10 (Part 2 in Fig.  1): (1) Mutations in NR significantly increase the risk for development of human metabolic diseases (Tables 4, 5, 6, and 7), suggesting that NR sequence changes may weaken the NR functions in suppressing human metabolic diseases and inflammation; (2) NR deficiencies lead to abnormal mouse phenotypes and inflammation from the MGI database (Tables 8 and 9), suggesting that NRs’ expression and functions are essential for maintaining the homeostasis and inhibition of inflammation and that NR deficiencies increase the likelihood of developing metabolic diseases in mice and potentially in humans.
The second supporting evidence for classifying most of nuclear receptors as HAMP receptors is presented in Table  12 (Part 3 in Fig.  1). NRs inhibit inflammation signaling gene functions and inflammation readouts. Of note, 12 out of 15 NRs have anti-inflammatory roles verified by published papers (Table  12).
The third supporting evidence for classifying most NRs as HAMP receptors is demonstrated in Tables  13, 14, and 15 (Part 4 (1) and (2) in Fig.  1). NRs were less upregulated than downregulated during the progression of metabolic, cardiovascular, and autoimmune diseases and cancers, suggesting that NRs’ physiological expression and functions may block the pathogenesis and progression of those diseases.
The fourth supporting evidence for classifying most of nuclear receptors as HAMPs is demonstrated in Tables  16 and 17 (Part 4 (3) in Fig.  1). Inflammation signaling genes regulated nuclear receptor expression levels as judged by the following results: (1) Most NRs were downregulated when stimulated with pro-inflammatory agents, suggesting that the pro-inflammatory signals suppress the NRs expression, and (2) some NRs were downregulated when anti-inflammatory signaling genes were deficient. In contrast, those NRs were upregulated when anti-inflammatory signals were activated.
The fifth supporting evidence for classifying most of nuclear receptors as HAMP receptors is demonstrated in Tables  20, 21, and 22 (Part 4 (4) in Fig.  1). Most NRs were more upregulated than downregulated when innate immune sensor inflammasome/caspase-1 genes were deficient. In contrast, caspase-1-degrading gene histone deacetylase Sirt1 [ 8] transgene may have anti-inflammatory functions by increasing the expression of certain NRs.
Table 22
Signal pathways that are upregulated by genes listed in Table 21
https://static-content.springer.com/image/art%3A10.1186%2Fs13045-017-0526-8/MediaObjects/13045_2017_526_Tab22_HTML.gif

Discussion

NRs are a class of 48 lipophilic ligand-activated transcription factors identified as key players of metabolic and developmental processes. Upon activation by the ligand messenger, NRs typically function as transcription factors where they bind to recognition elements on the genomic DNA and regulate the expression of target genes via type I, II, and III signaling formats [ 66]. Regardless of the significant progress that has been made in characterizing NR functions and expression, the global profiling of NR expression in human immune and cardiovascular tissues and potential mechanisms underlying the physiological expression of NRs remained poorly defined. In addition, the important issue of how innate immune sensor inflammasome/caspase-1 and other inflammatory signaling globally regulate NR expression in tissues and cells also remained unknown. To examine these issues, we took panoramic profiling database analysis approaches and made the following important findings: (1) NRs are differentially expressed in human and mouse tissues and NR expression may be under regulation by oxygen sensors, angiogenesis pathway, stem cell master genes, PRRs, and tissue hypomethylation/hypermethylation indices; (2) NR sequence changes and mutations are associated with increased risks for development of metabolic diseases, cardiovascular diseases, hormone insensitivity/resistance, cancers, and autoimmune diseases; (3) NRs have less tendency to be upregulated than downregulated in human autoimmune diseases, metabolic diseases, and cancers, which may be regulated by numerous inflammation-modulating pathways and mitochondrial energy metabolic enzymes; (4) The innate immune sensor inflammasome/caspase-1 pathway plays a critical role in regulating the expression of most NRs (Table 23); and (5) We propose a new paradigm that most NRs are anti-inflammatory HAMPs for regulating the balance of inflammation, inhibition of inflammation, and resolution of inflammation.
Table 23
Nuclear receptor expression was regulated by ApoE and LDL-R, pro/anti-inflammatory cytokines, and inflammasomes in pathology
Human metabolic disease
NRNC symbol
ApoE KO
LDL-R KO
IFN-γ stimulation
IL-1β stimulation
TNF-α stimulation
NFκB inhibitor
TGF-β KO
Tg-PPARα
CAS1 KO
ASC KO
Tg-Sirt1
NLRP3 mutant
Upregulation (from Fig  6c)
NR0B1
                       
NR1B1
         
       
NR1B3
     
         
NR1F3
             
   
 
NR1I1
               
NR2F6
   
     
     
NR3B2
             
       
NR4A1
 
 
         
NR5A1
                     
NR4A2
 
             
 
NR6A1
               
   
NR4A3
         
         
Downregulation (from Fig 6c)
NR1A1
             
 
NR1B2
 
         
NR1C1
             
       
NR1D1
         
         
NR1H3
           
       
NR3C3
             
     
NR1F1
     
 
 
   
NR1C3
 
     
   
 
NR3A1
 
       
   
NR2C2
           
 
   
NR3C4
 
       
 
 
NR1F2
     
     
     
NR3B3
     
           
NR3C1
 
     
   
NR2A2
         
         
NR2B3
 
         
   
NR2C1
         
     
NR2E3
                     
NR2F1
 
   
         
NR3A2
     
 
         
Abbreviations: KO knockout, ApoE apolipoprotein E, LDL-R−/− low-density lipoprotein receptor, IFN-γ interferon gamma, IL-1β interleukin 1 beta, TNF-α tumor necrosis factor-α-like; TGF-β transforming growth factor-β, Casp1−/− caspase-1-deficient mice; ASC−/− PYD and CARD domain-containing deficient mice, Tg-Sirt1 transgenic sirtuin 1 mice, NLRP3 NLR family pyrin domain containing 3 deficient mice, PBMC peripheral blood mononuclear cells
We utilized an experimental database mining approach that was pioneered and developed in our laboratory throughout the years [ 2, 6769]. By analyzing DNA sequencing data from tissue cDNA libraries, we were able to study expression profiles of NRs in various tissues. Since the gene expression sequencing tag (EST) data deposited in the NIH-NCBI-UniGene database have been established based on DNA sequencing data, the data extracted from EST database mining are more precise in providing the tissue expression profiles of genes than traditional hybridization- and primer annealing-based approaches like Northern blots and RT-PCRs [ 2]. Of note, since the UniGene database does not have many non-tumor cell line-related gene expression data in the presence of various gene deficiencies and stimulation conditions, we analyzed microarray-based gene expression data deposited in NIH-GEO datasets to determine NR expression changes under pathological conditions. Also, as all the data we provided in this manuscript were collected from cDNA cloning, DNA sequencing experiments, and microarray datasets rather than theoretical data derived from computer modeling, we believe that our findings are relevant for many biological and pathological scenarios. Nevertheless, herein we acknowledge that further well-designed experiments are needed to consolidate our findings.
As we pointed out in Table 7, a previous paper reported a mouse NR tissue expression profile using nucleic acid binding based RT-PCR technique [ 26, 27]. However, NR superfamily expressions using a more accurate DNA sequencing-based technique have not been profiled for human tissues. Other reports have confirmed the tissue distribution of few NRs. For example, as previously mentioned, the rat tissue distribution and/or the relative level of NR3A1 and NR3A2 expression seems to be quite different, i.e., moderate to high expression in the uterus, testis, pituitary, ovary, kidney, epididymis, and adrenal for NR3A1 and the prostate, ovary, lung, bladder, brain, uterus, and testis for NR3A2 [ 70]. Another study showed that NR2E3 mRNA was detected in the adrenal gland, thyroid gland, prostate, testis, uterus, trachea, and salivary gland [ 71]. A study assessed the expression patterns of NRs in peripheral blood mononuclear cells and found that 33/48 NRs were expressed in peripheral blood mononuclear cells [ 72]. In order to clearly summarize our findings, study the expression profile of NRs, and offer a simple, powerful way to obtain highly relational information about their physiologic functions as individual proteins and as a superfamily, we proposed a novel pyramid model to highlight several categories of NR activities in many important tissues. This pyramid model is significant as it improves our understanding of the tissue differences of NR machinery. This model is also significant for understanding the potential pharmacological side effects of new drugs targeting NRs in those tissues. Based on the different distributions and relative levels of the NRs in different target tissues, ligands could be used to elicit beneficial hormone-like activities and reduce adverse side effects of NR-targeted drugs.
The current DAMP receptor model emphasizes only the danger signals generated from endogenous metabolic processes. It fails to recognize the roles of potential endogenous metabolites in anti-inflammatory responses, inflammation resolution, and maintenance of homeostasis. As we pointed out in our previous report [ 5], it is significant for us to address these limitations and shift the paradigm to form a new model [ 73] to recognize novel anti-inflammatory and homeostatic signals derived from endogenous metabolites. Recent advances in immunology have clearly demonstrated the well-published “two arms model.” This model states that in addition to the pro-inflammatory immunoeffector and T cell co-stimulatory mechanisms, there are several immunotolerance and anti-inflammatory mechanisms mediated by the immune system. These anti-inflammatory mechanisms include T cell co-inhibition/co-suppression pathways, T cell anergy, regulatory T cells [ 74], and secretion of anti-inflammatory/immunosuppressive cytokines such as transforming growth factor-β (TGF-β), interleukin-10 (IL-10), IL-35 [ 69, 75], and IL-37 as we and others reported, etc. We have reported two types of lysophospholipids such as lysophosphatidylserine (LysoPS) and lysophosphatidylethanolamine (LPE) [ 5] and a few uremic toxins as anti-inflammatory homeostasis-associated molecular patterns [ 76]. In addition, along the same line, endogenous specialized pro-resolving mediators have been identified as regulators of infection and inflammation [ 77].
Our new classification of most NRs as homeostasis-associated molecular pattern (HAMP) receptors was that some NRs have been experimentally proved to bind promiscuously to certain types of “patterns” but not exclusively stick to highly specific ligands (Table 3). For example, activation of NRs by a variety of endo- and exogenous chemicals are elemental to induction and repression of drug-metabolism pathways. The master xenobiotic-sensing NRs, the promiscuous pregnane X receptor (PXR), and less-promiscuous constitutive androstane receptor (CAR) are crucial to initial ligand recognition, jump-starting the metabolic process [ 78]. In addition, phytoestrogens are natural endocrine disruptors that interfere with estrogenic pathways. They insert directly within the hormone-binding domain of estrogen receptor-α (ER-α) and β, with a preference for the β isoform of which the concentration predominates in the normal mammary epithelium [ 79]. Moreover, bisphenol A (BPA) is widely used as a component in polycarbonate plastics for food and beverage packaging, epoxy linings for canned foods, and dental sealants, among other applications. Experimental literature demonstrates BPA’s affinity for estrogen receptors and downstream effects on estrogen-responsive genes [ 80]. Those examples have clearly demonstrated that some NRs can promiscuously bind to certain types of “patterns” but not exclusively stick to highly specific ligands.
However, little is known how and why some receptors such as PXR and CAR develop promiscuity. The most widely accepted speculation is that both narrow and broad specificity seen for receptors or proteins are a result of natural selection process [ 81]. Less specificity of receptors provides evolutionary advantage to organisms that had to conduct a broad set of biological activities with limited protein repertoire and also allowed the organisms to evolve new responses to many endogenous and external ligands [ 8284]. Promiscuity of such receptors complicate identification of the physiological ligands that activate them in vivo [ 85]. One way to identify candidate ligands for orphan NRs is to identify their three dimensional structure [ 86, 87]. However, receptor affinity for the ligand and the physiological concentrations of the ligand in the tissues have to be taken into account when determining the potential relevance of the specific ligand for the receptor function [ 85, 88]. Additionally, if it is known that the promiscuous NRs require intracellular lipid binding proteins to shuttle the ligand toward it (like PPAR utilizing certain FABPs—fatty acid binding proteins), the nuclear translocation of the particular protein in response to a compound can be used to determine potential ligands for the NR [ 85]. Nevertheless, identifying the potential endogenous ligands bound to the NR of interest in vivo by using mass spectroscopy, high-performance liquid chromatography (HPLC) or gas chromatography are the most relevant methods than the ones mentioned above [ 85, 89, 90].
We acknowledge that some of the fold changes shown in our tables are less than twofold. However, complex diseases such as diabetes, obesity, cancer, and autoimmune disorders are regulated by myriad of genes similar to quantitative traits [ 91, 92]. Previously, for most of the continuous traits, the strongest genetic association could explain only a small fraction of the genetic variance [ 93, 94]. However, later analyses revealed that casual loci with small effect size are also important in determining continuous traits and complex diseases such as schizophrenia [ 94]. Moreover, recent publications demonstrated that complex and chronic diseases are driven by accumulation of weak effects on the key genes and regulatory pathways [ 95, 96]. It is evident that polygenic effects are important across a wide variety of traits and diseases such as diabetes [ 97]. Therefore, it is our understanding that even a low fold change in potent transcription factors such as NRs can significantly impact progression of complex diseases.

Conclusions

To improve our understanding on most NRs as anti-inflammatory sensors and regulators, we propose a new working model and classified most NRs as homeostasis-associated molecular pattern receptors (HAMPRs) as shown in Fig.  7. First, NRs can sense lipophilic metabolites, hormones, and xenobiotics in a ligand-receptor-specific manner and “pattern” recognition manner; second, most NRs inhibit inflammation; third, a list of tissue homeostasis regulator pathways, tissue regeneration, and angiogenesis pathways including hypoxia sensors, VEGFR pathways, stem cell master regulators, and hypomethylation/hypermethylation may regulate the tissue expression of NRs (as shown in Fig. 7a); fourth, metabolic diseases [ 98, 99] and autoimmune arthritis have less tendency to upregulate than downregulate NR expression; fifth, comparing to conventional receptor-mediated pathways that signal via multiple steps for checking and relaying, NRs’ signals are much faster and require much less signal relay (as shown in Fig. 7b); and sixth, innate immune sensor inflammasome/caspase-1 pathways suppress the majority of NR expressions, suggesting that most NRs play critical roles in counteracting the role of DAMPs during sterile inflammatory pathologies and maintaining the homeostasis of tissues and cells in addition to their functions in metabolic, developmental [ 100], and growth processes [ 101107]. Our new findings have significantly improved our understanding on NRs in the regulation of inflammation and tissue homeostasis (as shown in Fig. 7c).

Acknowledgements

Not applicable.

Funding

This work was supported by an NIH grant to Drs. XF Yang, H. Wang, and ET Choi (Grant No. HL131460-01); National Natural Science Foundation of China (Grant No. 81560051) and the National Key R&D Program in the Twelfth Five-year Plan (Grant No. 2014ZX09303305) to XS Cheng; and Natural Science Foundation of China (Grant Nos. 81370371, 81570394) and Ministry of Education of the People’s Republic of China (Grant No. B13037) to HM Tan.

Availability of data and materials

Experimentally verified mRNA expressions of NRs in various tissues were obtained from databases of the National Institutes of Health (NIH)/National Center of Biotechnology Information (NCBI) UniGene ( http://​www.​ncbi.​nlm.​nih.​gov/​sites/​entrez?​db=​unigene). The microarray datasets that were used in this study were retrieved from NIH-GEO database ( https://​www.​ncbi.​nlm.​nih.​gov/​gds), and the numbers of datasets that were used are as follows: GSE55235, GSE6088, GSE6054, GSE23561, GSE13670, GSE55100, GSE48964, GSE15773, GSE9490, GSE43292, GSE13139, GSE18443, GSE24342, GSE21324, GSE3920, GSE19240, GSE40838, GSE10939, GSE46150, GSE42267, GSE33101, GSE2489, GSE41802, GSE57002, GSE56670, and GSE49694. MouseMine database ( http://​www.​mousemine.​org/​mousemine/​begin.​do) was used to analyze how the deficiency of NRs lead to development of metabolic and cardiovascular pathologies.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to disclose.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.

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