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Introduction

The nuclear receptor (NR) superfamily is comprised of 59 member classes, all of which are ligand-dependent transcription factors that are involved in several important biological processes, such as the control of embryonic development, organ physiology, cell differentiation and homeostasis (1,2). In humans, only 48 members of the superfamily have been found and genetic mutations in these NRs have been proven to cause rare diseases, such as cancer, diabetes, rheumatoid arthritis, asthma and hormone resistance syndromes (3). Bearing that in mind and the fact that nuclear hormone receptors possess internal pockets, that bind to hydrophobic, drug-like molecules, they are considered ideal drug targets for the treatment of several diseases. The main limitation, though, in using NRs for drug discovery, is the fact that they control numerous genes and are involved in a number of complex pathways. The major goal of analyzing this protein family is to discover ligands that control the desired, limited subset of genes for therapeutic purposes (4).

As proteins, NRs consist of four functional domains (A/B, C, D and E), each with a separate crucial function (Fig. 1). The N-terminal, NTD domain, also referred to as the activation-function-1 (AF-1) domain, varies in length between NRs and is implicated in transactivation in a cell- and promoter-specific manner. The DNA-binding domain (DBD) is the most highly conserved region among the receptors. Although the precise mechanism of DNA interaction varies from receptor to receptor, a common theme is that direct contact of the NR to DNA is made through two consecutive zinc fingers. Zinc fingers are DNA-binding motifs that contain the key amino acids necessary for the association of the NR to the major groove of an appropriate DNA response element that is located within the promoter segment of a target gene. The domain at the C-terminus is by far the most important to the drug discovery efforts of the pharmaceutical industry. This region, known as the ligand-binding domain (LBD), contains all the binding determinants of the small molecule hormone or synthetic ligand (5). Apart from binding to ligands, this domain also interacts directly with coregulator proteins through a region in the C-terminal part, termed AF2-AD which has a ligand-dependent autonomous activating function. Upon ligand binding, the LBD domain undergoes major structural rearrangements which relieve the binding of co-repressors, unmask AF2-AD, allow the binding of co-activators and render the receptor able to regulate transcription. There is also another domain known as the hinge region or D domain, which is a short, flexible linker between the DBD and the LBD. This region has the least sequence and size conservation between NRs and in numerous cases, it harbors nuclear localization signals (6,7). Last but not least, some but not all NRs have a C-terminal domain or CTD domain, which seems to play an important role in the transcriptional activation and dimerization of the receptor (6,7).

Figure 1 Structural domains of the NR superfamily. Each domain is represented in a different color. NTD in green, DBD in blue, HR in yellow, LBD in purple and CTD in red. NTD, N-terminal domain; DBD, DNA-binding domain; HR, hinge region; LBD, ligand-binding domain; CTD, C-terminal domain.

NR members are involved in a number of biological pathways of human physiology and pathology, serving as sensors of stimuli, dominant regulators of controlling molecular events and hubs governing complex gene regulatory networks. The importance of the NR superfamily in control development, differentiation and homeostasis has led to substantial efforts being made to target this family therapeutically (6,7). In the present study, the sequence and structural information of both LBD and DBD were analyzed with an aim of drawing conclusions regarding NR conserved motifs and novel pharmacological targets.

Data and methods

Dataset collection and filtering

The amino acid sequences that are related to the NR proteins were selected from the NCBI database (ncbi.nlm.nih.gov), using the representative keywords as described in Table I. Protein sequences that responded to the query, but did not include NR members were eliminated from the primary dataset, by using related keywords and regular expression techniques in the header information, and local alignments with reference protein sequences. Furthermore, a final dataset for each member class was produced by using internal protein alignments and protein identity score. Duplicated protein sequences in each phylum that were found to share <90% protein identity within the dataset were removed. In total, 109,613 NR protein sequences were identified from several members and species, and a dataset containing 333 representative, unique, non-duplicate protein sequences of phylum and kingdom was created.

Table I Nuclear receptor members analyzed in the present study.

Table I

Nuclear receptor members analyzed in the present study.

Nuclear receptor	Abbreviation	Sample
Thyroid hormone receptor	TRα, TRβ	7,800
Retinoic acid receptor	RARα, RARβ, RARγ	13,200
Peroxisome proliferator activated receptor	PPARα, PPARβ, PPARγ	7,500
V-ErbA-related protein	Rev-ErbΑ, Rev-ErbΒ	1,160
Ecdysone-induced protein 78C	Eip78C	400
RAR related orphan receptor	RORα, RORβ, RORγ	4,500
Steroid hormone receptor cnr14	Cnr14	8
Ecdysone receptor	EcR	3,370
Liver X receptor	LXRα, LXRβ	2,080
Farnesoid X receptor	FXRα, FXRβ	3,450
Vitamin D receptor	VDR	2,160
Pregnane X receptor	PXR	590
Constitutive androstane receptor	CAR	880
Nuclear receptor HR96, HR8 and HR48	HR96, HR8, HR48	390
Nuclear receptor HR1	HR1	26
Hepatocyte nuclear factor 4	HNF4α, HNF4γ	3,800
Retinoid X receptor	RXRα, RXRβ, RXRγ	5,040
Ultraspiracle	USP	3,790
Testicular receptor	TR2, TR4	3,080
Tailes-related receptor	TLX	1,400
Photoreceptor specific nuclear receptor	PNR	1,660
COUP transcription factor	COUP-TF1, COUP-TF2	80
V-erbA-related protein 2	EAR-2	35
Estrogen receptor	ERα, ERβ	13,740
Estrogen related receptor	ERRα, ERRβ, ERRγ	5,244
Glucocorticoid receptor	GR	6,240
Mineralocorticoid receptor	MR	1,640
Progesterone receptor	PR	4,150
Androgen receptor	AR	4,670
Nerve growth factor IB	NGFIB	45
Nuclear receptor related 1	NURR1	1,590
Neuron-derived orphan receptor 1	NOR-1	22
Steroidogenic factor 1	SF-1	1,120
Liver receptor homolog 1	LRH-1	2,360
Nuclear hormone receptor FTZ-F1β	FTZ-F1β	420
Germ cell nuclear factor	GCNF	1,100
Zygotic gap protein knirps	kni	105
Dosage-sensitive sex reversal	DSS	18
Small heterodimer partner	SHP	750
Total		109,613

Structural identification of NR protein domains

A more specialized analysis was performed in order to identify the protein domains in each NR member. The analysis was performed using the InterPro Database and a representative protein sequence from each NR member class (8,9). In the majority of cases, the Homo sapiens species protein sequence was used as a representative, except in cases where no human sequences existed and thus, another species was selected.

Multiple sequence alignment (MSA)

MSA was performed using the MATLAB Bioinformatics Toolbox (https://www.mathworks.com/products/bioinfo.html), utilizing a guide tree and the progressive MSA method as previously described (9,10). Pairwise distances among sequences were estimated based on the pairwise alignment and followed by calculating the differences between each pair of sequences. The Neighbor-Joining method was used towards to estimating the guide tree by assuming equal variance and independence of evolutionary distance estimates (11). A more specific analysis was performed using the MATLAB Bioinformatics toolbox towards comparing the MSA results and the protein domains structural features from the previous step.

Exploration of conserved motifs

Consensus sequence was calculated and visualized through the Jalview platform (12) using the MSA result and parameters including amino acid conservation (9,10,12). The commentary section of Jalview, which presents the amino acid conservation using logos and histograms, was further observed to uncover innovative motifs within the protein domains.

Phylogenetic analysis of steroid hormones (SRs)

The representative dataset of the steroid hormone members was used for the construction of the phylogenetic analysis. The primary dataset was filtered, and duplicated protein sequences in each species that were found to share <95% protein identity within the dataset were removed. MSA was performed using the MATLAB Bioinformatics Toolbox (13) alignment methods. Only unambiguous homologous regions were retained for phylogenetic analysis; manual masking, trimming and consensus multiple alignments were performed in MATLAB (13). The phylogenetic analysis was performed using the MATLAB bioinformatics toolbox. The distances between the sequences of the SR dataset were measured with the Jukes-Cantor pairwise distance method (14,15) and phylogenetic analysis was performed utilizing the average distance statistical method (16,17) with 100 bootstrap replicates and visualized using MEGA software radiation option (18).

Structural and functional characterization of conserved motifs

The structural and functional characterization of the LBD domain conserved motifs was analyzed using representative 3D structures of NRs within the steroid hormone subfamily. In the present study, the 3D structures of the glucocorticoid receptor (GR) LBD [4P6X (19) and 5NFT (20)] and the androgen receptor (AR) LBD [6NWL (21)] from the Protein Data Bank (https://www.rcsb.org) were used. The structural and functional analysis at SR structures was gained using MOE (20,22-24), where all the interactions sites with the corresponding proteins and ligands were identified and studied. Specifically, each PDB entry was examined for ligand interaction using the ligand interaction function. MOE showcased the LBD amino acids that interacted with said ligands or co-activators.

Results

NR protein domains

The NR proteins are composed of a number of domains and sub-domains which are differentially conserved between the NR members. The NRs protein domains play diverse roles, including the variable N-terminal region, the conserved DBD, the variable hinge region, the conserved LBD and the variable C-terminal region. The representative protein sequences were selected for the analysis of NR domains belonging to the species Homo sapiens, except in a few members from which this species was absent, including FXRβ, Eip78C, Cnr14, EcR, HR96, USP, FTZ-F1β and kni. The FXRβ NR representative was selected from the species Mus musculus, and Eip78C, Cnr14, EcR, HR96, USP, FTZ-F1β and kni from the Drosophila melanogaster species. Sequence analysis of the NR protein domains revealed a clear separation between them (Fig. 2). The characteristic five-protein domains of the NR family were identified in the majority of the members. NR protein domains vary in length among members, and in a few cases, some of them are missing (Fig. 2). Based on these results, some members appear without a C-terminal region, while others do not even have a DBD (SHP, DSS) or LBD (kni) (Fig. 2).

Figure 2 Protein domains in nuclear receptors. The N-terminal region is shown in green, the DBD is shown in blue, the hinge region is shown in yellow, the LBD is shown in purple, and the C-terminal region is shown in red. DBD, DNA-binding domain; LBD, ligand-binding domain.

MSA and conserved motifs

MSA of proteins sequences from the NR superfamily, as well as the subfamily of SRs, was performed to identify highly conservative regions within all organisms of the animal kingdom. The visualization and analysis of the MSA results in both cases were performed using Jalview. As is known, the majority of NRs consist of five structural domains, including the N-terminal, DBD, LBD, C-terminal domains and the hinge region. Sequence analysis of the SR protein sequences dataset has revealed clear conservation in all species. The DBD is the most highly conserved region in the NR superfamily, as observed in the MSA of both NRs and SRs. Two zinc finger motifs can be identified, each of which consisted of a zinc ion linked to four cysteine residues. In the first zinc finger, a conserved pattern, termed P-box, can be identified, containing amino acid residues necessary for the specialized identification and binding of the receptor to the DNA of the target-gene (22). In the second zinc finger, another pattern of amino acids, can be identified, which is involved in the dimerization of the receptors and is termed D-box (23). The amino acids of the D-box appear to be quite diverse in the NR superfamily (Fig. 3), whilst in the SRs, they are highly conserved (Fig. 4) (23).

Figure 3 Highly conserved motifs of the nuclear receptor superfamily in the DBD, based on the MSA of 333 protein sequences. The two zinc fingers with the preserved cysteine residues are colored green, whilst the P-box and D-box are highlighted with a dark red color. DBD, DNA-binding domain; MSA, multiple sequence alignment.

Figure 4 Highly conserved motifs of the steroid receptor family in the DBD. The two zinc fingers with the preserved cysteine residues are colored green, whilst the P-box and D-box are highlighted with a dark red color. DBD, DNA-binding domain.

The LBD appears to be less conserved; nevertheless, some important conserved motifs can be observed, particularly within the SRs. Motif A can be found in positions 944-949 of the steroid receptor MSA (Fig. 5). This is an LLxxL motif (where L represents a leucine residue and x denotes any amino acid), which is an inverse NR box (LxxLL) (24). The NR box (NR interacting box) plays an important role in the interaction of NRs with co-activators and therefore, in the regulation of transcription. Motif B, occupying positions 960-970 of the SR alignment (Fig. 5) and motif C, occupying positions 972-982 (Fig. 5), as reported in the literature, also appear to be regions necessary for co-activator function (25). Motif D, which occupies positions 1051-1063 of the steroid receptor multiple alignment (Fig. 5), appears to be highly conserved and is an area of utmost significance for ligand binding, as mutations in this area have been proven to lead to the complete inability of the receptor to bind (22,24,25). Finally, in alignment positions 1115-1122, a LxxLL motif is observed (motif E), whereas in alignment positions 1144-1153, an inverse NR box (LLxxL) can be found, named motif F (Fig. 5) (24).

Figure 5 Highly conserved LBD motifs, based on the steroid receptors MSA. Motifs A, B, C, D, E and F are highlighted (colored red). (A) Motifs A-C; (B) motifs D-F. LBD, ligand-binding domain; MSA, multiple sequence alignment.

As for the NR superfamily (Fig. 6), only motifs B, C, D and E seem to be conserved, whilst in the alignment positions that motifs A and F would be located, a great amino acid variability is observed.

Figure 6 Highly conserved LBD motifs of the nuclear receptor superfamily, based on the MSA of 333 protein sequences. Motifs B, C, D and E are highlighted with red. (A) Motifs B and C; (B) motifs D and E. LBD, ligand-binding domain; MSA, multiple sequence alignment.

Phylogenetic analysis

The phylogenetic tree of the steroid hormones comprises of five major monophyletic sub-clusters including the GR, mineralocorticoid receptor (MR), AR, progesterone receptor (PR) and estrogen receptor (ER) (Fig. 7). Based on the tree topology, steroid receptors have been clustered in distinct evolutionary clades. In the phylogenetic analyses, representative members from several kingdoms and phyla were identified, including mammals, Aves (birds), Pisces (fish), insectum (arthropods) and Reptilia.

Figure 7 Phylogenetic analysis of the steroid hormone receptors.

Conserved motif exploration

In the present study, eight candidate conserved motifs that were identified in both DBD and LBD are summarized. Although no structural information is provided for the DBD of the NR, some ‘key’ amino acid residues have been identified and provided within the P-box and B-box motifs, which are mainly termed ‘inc-coordinating motif’ (Figs. 3 and 4) (24,26). The zinc-coordinating motif is a potential pharmacological target of the DBD of the NRs. It is characterized by two anti-parallel helices connected by loops at their amino-terminal ends, from which each helix-loop combine coordinates a single zinc ion using four ‘key’ cysteines (26). Through this specific mechanism, NR members are able to change conformation and activate the selection mechanism towards binding DNA molecules (Fig. 8).

Figure 8 Rippon representation of the RXR (colored purple) and the THR (colored orange) DBD heterodimer, which is bound to thyroid response element DNA (colored green). Zinc-coordinating motif cysteines residues are colored blue. Crystal structure from the PDB (ID: 2NLL). RXR, retinoid X receptor; DBD, DNA-binding domain; PDB, Protein Data Bank.

On the other hand, conserved motifs that are formed the interaction site of the LBD of NR are of a major interest as potent pharmacological targets. The majority of these directly interact with ligands, and some others are contained critical patterns, including the ‘LLxxL’ pattern in back and forward direction (Fig. 9). The ‘LLxxL’ pattern is repeated >3 times in the LBD of the majority of the NR members and constitutes a critical conserved region that is directly related to their biological function (24,27). Mutations in the ‘LxxLL’ pattern, have been shown to be strongly associated with incorrect signaling in NR members, particularly in motif D (28). Mitsis et al (24) provided a structural and chemical analysis of the LBD of the NRs, which explains the possible role of this pattern and its significant as a potent pharmacological target.

Figure 9 (A) Multiple sequence alignment of the GR (4PGX, 2Q7L) and AR (5NFT) protein sequences. In color green are highlighted the ‘key’ amino acids between each NR and their ligand. The five conserve motifs A-F are presented in different colors. (B and C) Structural superposition of the of the LBD of the GR (colored red and orange) and the AR (colored blue) in ribbon representation. (D and E) Conserved motifs A-F and interaction site of the LBD of the GR and the AR. GR, glucocorticoid receptor; AR, androgen receptor; NR, nuclear receptor; LBD, ligand-binding domain.

Discussion

The NR superfamily consists of transcription factors that influence gene expression in a positive or negative manner (6). Although >300 transcription factors have been recognized as members of this superfamily, the human genome encodes only 48 NRs, whereas mice and rats have 49 and 47 NR superfamily members, respectively (29-31). These DNA-binding proteins play a crucial role in several physiological functions, such as proliferation, differentiation, apoptosis, immune and stress response (32,33). The first founding members of the NR superfamily, which were sequenced, were the human GR and ERα (34,35). The analysis of their full-length sequence demonstrated similar structural domains, indicating the presence of a receptor family (36). Several other receptors, including the MR, AR, PR, and receptors for vitamin A and D, have been shown to have high amino acid sequence similarity (37). Indeed, NRs consists of a less conserved N-terminal or immunogenic domain, a highly conserved DBD, and a LBD (36).

The NR superfamily consists of six families of transcription factors (38). A significant number of NRs have not yet been shown to bind to a known ligand (39,40). On the other hand, SRs form the third subfamily of steroid-activated NRs consisting of the ER and AR, which both control reproduction and sexual development; the PR, which plays a key role in female reproduction; the MR that maintains electrolyte balance; and the GR, which plays fundamental role in maintaining basal and stress-related homeostasis (Fig. 7) (41). Since this class of transcription factors substantially influences numerous aspects of human physiology, they have been early identified as potential pharmacological targets (42). Currently, a number of steroid receptor agonists and antagonists are widely used in the therapeutic management of numerous benign and malignant diseases (43). Importantly, synthetic glucocorticoids are frequently prescribed for several inflammatory diseases due to their potent anti-inflammatory and immunosuppressive actions (44).

Evolution is a complex biological process, which is characterized by the elaboration and optimization of the inherited traits from one generation to the next, leading to the emergence of new divergent functions (45,46). During the evolutionary process, genes are duplicated, rapidly mutated and naturally selected for each function (47,48). NRs are ancient in origin and diversified prior to the arthropod/vertebrate split (49). Escriva et al (50) demonstrated that NRs are specific to metazoans (cnidarians and acoelomates). Of note, they demonstrated that the NR superfamily was evolved through two serial waves of gene duplications; the first wave occurred before the cnidarians diverged from bilaterians, whereas the second wave took place in vertebrates (50). Thornton (45) further elucidated the mechanisms underlying the evolution of steroid hormone receptors. Using an extensively parallel PCR screen, Thornton (45) found specific sequences of steroid receptors in the sea lamprey Petromyzon marinus, a jawless fish that diverged from gnathostomes ~450 million years ago. According to his study, Thornton (45) identified the lamprey PR, ER and CR, and reconstructed the two first ancestral proteins (AncSR1 and AncSR2) according to sequencing analyses of lamprey receptors and subsequent alignment with 70 other available steroid and related receptors (9). The AncSR1 was 71% identical to the human ERα, while the AncSR2 was 77% similar to the human PR (35). Further phylogenetic analysis revealed that steroid receptors evolved through two serial genomic expansions, which are likely to have occurred one before the lamprey-gnathostome divergence and one after (30). The first created an ER and a 3-ketosteroid receptor, whereas the second produced a receptor for 3-ketogonadal steroids (progestins, androgens or both) and a corticoid receptor that finally gave rise to MR and GR (Fig. 7) (6).

Considering that a number of NRs participate in diverse physiological functions, it would be of great interest to further examine the evolutionary story of these proteins. In the present study, eight different conserved motifs were identified and are subject to further investigation. These significant key regions, in combination with the structural features of the protein domains they are contained will provide beneficial knowledge towards understanding the evolution of NRs. In addition, the delineation of the evolutionary pathway of NRs may further explain the interactions between environment and the expressed genome, since each NR has a different group of target genes.

In conclusion, the present study provides a comprehensive sequence analysis study of the NR superfamily. Using several filtering techniques, the present study was able to select representative protein sequences from different phyla and kingdoms for each NR member. Therefore, the present study dealt with the ‘big data’ of the NR family. NRs and in particular, SRs, are involved in numerous biological processes, constituting the basic regulators of the human body. The main aim of the present study was to identify conserved motifs or patterns that play major roles in biological processes in which NRs are involved. Based on these results, eight ‘key’ motifs in the two main protein domains of the NRs were recognized, namely the LBD and the DBD. Moreover, all the identified conserved motifs were associated with the NR function. In addition, evolutionary differences between steroid hormones receptors and NRs were identified in these conserved regions, leading to an updated phylogenetic tree of the steroid hormones receptors that indicates the ideal candidate groups for the development of new pharmacological targets.

Acknowledgements

Not applicable.

Funding

The present study received funding from the project ‘INspIRED-The National Research Infrastructures on Integrated structural Biology, Drug screening Efforts and Drug Target Functional characterization’ (grant no. MIs 5002550) and by the project: ‘OpENscREENgR An Open-Access Research Infrastructure of chemical Biology and Target-Based screening Technologies for Human and Animal Health, Agriculture and the Environment’ (grant no. MIs 5002691), which are implemented under the Action ‘Reinforcement of the Research and Innovation Infrastructure’, funded by the Operational program ‘competitiveness, Entrepreneurship and Innovation’ (NsRF 2014-2020) and co‑financed by Greece and the European Union (European Regional Development Fund). The present study also received funding from the ‘competitiveness, Entrepreneurship and Innovation, EpAnEK 2nd cycle’ Operational program (grant no. Τ2ΕΔΚ‑02222, MIS 5074548), which was co‑funded by greece and the European Union.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

GPC, EE and DV conceived the present study. LP, LS, AE, FB, GPC, EE and DV wrote, drafted, revised, edited and reviewed the manuscript, and were involved in data collection. GPC, EE and DV confirm the authenticity of all the raw data. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

GPC is the Editor in Chief of the journal, and DV and EE are Editors of the journal. However, they had no personal involvement in the reviewing process, or any influence in terms of adjudicating on the final decision, for this article.

References