Abstract
The adenylate uridylate-rich elements (AREs) mediate the rapid turnover of mRNAs encoding proteins that regulate cellular growth and body response to exogenous agents such as microbes, inflammatory and environmental stimuli. However, the full repertoire of ARE-containing mRNAs is unknown. Here, we explore the distribution of AREs in human mRNA sequences. Computational derivation of a 13-bp ARE pattern was performed using multiple expectation maximization for motif elicitations (MEME) and consensus analyses. This pattern was statistically validated for the specificity towards the 3′-untranslated region and not coding region. The computationally derived ARE pattern is the basis of a database which contains non-redundant full-length ARE-mRNAs. The ARE-mRNA database (ARED; http://rc.kfshrc.edu.sa/ared) reveals that ARE-mRNAs encode a wide repertoire of functionally diverse proteins that belong to different biological processes and are important in several disease states. Cluster analysis was performed using the ARE sequences to demonstrate potential relationships between the type and number of ARE motifs, and the functional characteristics of the proteins.
INTRODUCTION
Adenylate uridylate-rich element (ARE)-containing genes include a number of the early response genes that regulate cell proliferation and responses to exogenous agents. AREs have been functionally attributed to a restricted number of mRNAs such as certain hematopoietic cell growth factors (e.g. granulocyte–monocyte colony stimulating factor, GM-CSF), interleukins, interferons, TNF-α and some proto-oncogenes (e.g. c-fos, k-ras and pim-1); Table 1 shows a list of previously known ARE-containing mRNAs. These gene products have been shown to play an important role in different cancers and inflammation states (1–3). However, the full repertoire of ARE-containing mRNAs, referred to hereafter as ARE-mRNAs, is not known, and hence the range of possible consequences of dysregulation of the ARE-mRNA decay pathways remains undetermined. In this paper, we explore the repertoire of human ARE-mRNAs using a bioinformatics approach with the compilation of ARE-mRNA sequences in a database.
Table 1. A list of previously known ARE-mRNAs.
| Gene name/function |
Ref. |
| Early lymphocyte activation antigen CD69 | (34) |
| 6-phosphofructo-2-kinase (PFK-2)/fructose-2,6-bisphosphatase | (35) |
| B-cell leukemia/lymphoma2 oncogene (Bcl-2) | (27) |
| c-fos proto-oncogene (transcription) | (36) |
| CHOP/Growth arrest and DNA-damage inducible factor | (37) |
| c-myb proto-oncogene (transcription) | (38) |
| c-myc proto-oncogene (transcription) | (39) |
| Cyclin D1 (cell cycle) | (40) |
| Cyclooxygenase (inflammation) | (15) |
| Endothelin-2 (vascular toning) | (41) |
| Epidermal growth factor receptor | (42) |
| Estrogen receptor α | (43) |
| Fibroblast growth factor 2 | (44) |
| Granulocyte colony stimulating factor | (38,45) |
| Glucose transporter 1 | (46) |
| Granulocyte monocyte colony stimulating factor | (8,14) |
| Gro-α, growth-regulated gene | (47) |
| Inducible nitric oxide synthase | (48) |
| Interferon-α (immune response, innate) | (38,49) |
| Intereferon-αAAa | (49) |
| Interferon-α1 | (38,49) |
| Interferon-α1B | (49) |
| Interferon-αF | (38,49) |
| Interferon-αG | (38,49) |
| Interferon-αH | (38,49) |
| Interleukin-1α (inflammation) | (50) |
| Interferon-β (immune response, innate) | (51,52) |
| Interferon-γ (immune response, adaptive) | (53) |
| Interleukin-1β (inflammation) | (54) |
| Interleukin-10 (immune response) | (55) |
| Interleukin-2 (immune response, adaptive) | (56,57) |
| Interleukin-3 (hematopoiesis) | (12) |
| Interleukin-4 (immune response) | (38) |
| Interleukin-6 (immune response) | (14) |
| Interleukin-8 (inflammation) | (14) |
| Interleukin-11 (adipogenesis inhibitory factor) | (58) |
| Lymphotoxin (immune response, inflammation) | (38) |
| K-ras proto-oncogene (signal transduction) | (59) |
| Leukemia inhibitory factor (LIF) | (60) |
| Macrophage colony stimulating factor | (61) |
| Macrophage chemotaxis protein-1 | (62) |
| Macrophage inflammatory protein-α | (63) |
| Macrophage inflammatory protein-β | (55) |
| Macrophage inhibitory protein-2α | (64) |
| Mda-7 (melanoma differentiation-associated gene) | (65) |
| Monocyte Chemotactic Protein-3 | (66) |
| MYCN (transcription) | (67) |
| Nerve growth factor | (49,68) |
| Platelet-derived growth factor/c-sis protooncogene | (69) |
| Pim-1 proto-oncogene (singal transduction) | (70) |
| Plasminogen activator inhibitor type 2 | (71) |
| Thioredexin reductase (metabolism, redox) | (72) |
| Tissue factor (thromboplastin, coagulation) | (73) |
| Tumor necrosis factor (inflammation, others) | (2,6,74) |
| Urokinase-type plasminogen (uPA) receptor | (75) |
| Urokinase-type plasminogen activator | (24) |
| Vascular endothelial growth factor | (16) |
The list has been compiled using PubMed literature search according to criteria described in the text. Gene name/function were given according to the most characterized function in conjunction with AREs. If the gene name does not correspond to a biological function, a function category in brackets is given. This list was used as a training set for computational derivation of ARE motif.
aThere are more than 20 IFN-α subtypes.
BACKGROUND: BIOLOGY OF AU-RICH ELEMENTS
A common trait of the ARE-mRNAs is that they are short-lived and thus rapidly disappear once their critical role in gene regulation ceases. ARE-mediated changes in mRNA stability are important in processes that require transient responses such as cellular growth, immune response, cardiovascular toning and external stress-mediated pathways. Thus, stabilization of the ARE-mRNAs can cause a prolonged response that may subsequently lead to a diseased state. Identifiable AREs in the 3′UTR of the mRNA were noted such as the pentameric and nonameric sequences of AUUUA and UUAUUUAUU. The minimal ARE sequence has been shown to be the nonamer rather than the pentamer (4–7). Shaw and Kamen (8) showed that the stable β-globin mRNA could be rendered unstable when its 3′UTR was replaced with the GM-CSF multiple ARE-containing 3′UTR. Several groups have described ARE-binding proteins that influence the ARE-mRNA stability. Among the well-characterized proteins are the mammalian homologs of ELAV (embryonic lethal abnormal vision) proteins including AUF1, HuR and He1-N2 (9–11). The zinc-finger protein tristetraprolin has been identified as another ARE-binding protein with destabilizing activity on TNF-α, IL-3 and GM-CSF mRNAs (12,13). Other non-ARE regions of certain mRNAs, including c-myc, histone and the transferrin receptor, have also been implicated in the stability of mRNA. The recent emphasis in literature is on signaling mechanisms, notably the role of stress activated protein kinases, p38 MAP kinase, in the ARE-mediated stabilization of certain ARE-mRNAs, e.g. IL-8, cyclooxygenase-2 and vascular endothelial growth factor (14–16).
COMPUTATIONAL DERIVATION OF THE ARE MOTIF AND A NON-REDUNDANT HUMAN ARE-mRNA DATABASE
Sequence retrieval and analysis utilized the GCG-Wisconsin Package (Genetics Computer Group/Oxford Molecular Company, Madison, WI) and additional programs written in the Practical Extraction and Report Language (PERL). A total of 36 951 human mRNA/cDNA sequences were extracted from GenBank Release 113 (National Center for Biotechnology Information, NCBI) using Lookup program (GCG codes) that was used to find mRNA or cDNA in the Definition Field along with Homo sapiens in the Organism Field (Source) in GenBank entries. Subsequently, a PERL code was written to extract the sequences that contain the field CDS in the Features Table to exclude those that do not have CDS; this resulted in 27 403 CDS-containing mRNA/cDNA sequences. This file was used as the input to another PERL program that extracted sequences as with complete CDS, i.e. without ambiguous CDS such as <, >, complement or join. The output was 15 148 sequences as full-length CDS-containing mRNA/cDNA file. The 3′UTRs were constructed using Assemble program (GCG codes) which extracted the sequences downstream of CDS (i.e. >CDS). This step was necessary because most of the GenBank records lack the 3′UTR as an annotated Feature key—this limitation of annotation was previously noted (17)—despite the fact that this information can be extracted computationally from CDS Feature as executed here. The UNIX command, Stream Editor (Sed), was used to remove sequences that have no 3′UTR. A resultant list of 13 057 human full-length CDS/3′UTR-containing mRNA sequences was finally compiled.
The minimum functionally relevant ARE motif has been previously reported to be UUAUUUA (A/U)(A/U). However, this consensus was based only on a limited number of AU-rich mRNAs such as GM-CSF, c-fos, TNF-α etc. (5–7,9). In the present analysis, a list of GenBank entries (Table 1) that belong to 57 previously recognized ARE-containing mRNAs was used as a training set for the computational derivation of the ARE motif. The selection of the ARE-mRNAs in this training list was based on either of two criteria: (i) mRNAs in which their AREs in the 3′UTR have been experimentally determined to affect their mRNA turnover, e.g. GM-CSF, c-fos, TNF-α and IFN-β; or (ii) mRNAs without available experimental evidence of an ARE-mediated mRNA decay but that contain AREs in their 3′UTR sequence and the mRNAs are transiently induced such as the case of IFN-α subtypes and k-ras. The 3′UTR sequences of these entries were extracted computationally as described above, and cleaned from the poly(A) tails using a PERL code to reduce its recognition as a pattern. The 57 3′UTRs were then plugged into the MEME (multiple expectation maximization for motif elicitations) program which finds conserved ungapped short motifs within a group of related, unaligned sequences (18). MEME yielded the motif pattern UAUUUAWW, which is very similar to the previously reported nonamer UUAUUUAWW. Next, we performed a consensus analysis around this motif, which resulted in the pattern WWWUAUUUAUWWW with a certainty level of 75% at each position. Subsequently, this 13-bp pattern was used by the FindPattern analysis (GCG codes). The stringency was decreased by allowing one mismatch in each direction of the nucleotides flanking the core pattern (UAUUUAU), in order to allow maximum recovery from the search. This step was performed on the 3′UTRs of the full-length CDS/3′UTR-containing mRNA list (13 057 sequences). The resulting subset of sequences was made minimally redundant using the CLEANUP program (19) with the parameters of 90% similarity and 90% overlap, which produced an output file that contained the longest available sequences. Approximately 17% redundancy in the ARE-mRNA list was computationally removed. A total of 897 minimally redundant sequences (∼8% of the human mRNA sequences analyzed) were finally obtained and subsequently termed the ‘ARE-mRNA database’ (ARED). This database was stored as flat GenBank files and imported for further analysis into the commercial Vector NTI software version 5.5 (InforMax, Bethesda, MD).
In order to validate the specificity/sensitivity of the computationally-derived 13-bp ARE pattern in the 3′UTR (e.g. in comparison to CDS), we searched for the pattern in the CDS (Assemble and FindPattern GCG codes) in ARED (Table 2). The data show that the 13-bp ARE pattern with two mismatches (which was used for ARED build-up) was highly selective (89% specificity) towards 3′UTR when compared to CDS (P < 0.0001). The selectivity can also be increased to 96%, though at the expense of ARED coverage (82%; Table 2). Another distinguishable feature of the 13-bp pattern in typical ARE-mRNAs is that a great number of ARE-mRNAs (~40% of total ARE-mRNAs) have continuous patterns of AUUUA (n > 1) with the predominant pattern of WWWUAUUUAUUUAWW.
Table 2. Statistical characteristics of the computationally derived ARE pattern in 3′UTR and CDS.
| 3′UTR | CDS | P valuef | |||||||
| |
No.a |
Findsb |
Meanc ± S.D. |
%d |
No. |
Finds |
Mean ± S.D. |
% spe |
|
| Mismatch = 0 | 276 | 349 | 1.3 ± 0.7 | 31 | 2 | 3 | N/A | >99 | N/A |
| Mismatch = 1 | 736 | 3670 | 4.9 ± 6 | 82 | 27 | 50 | 1.85 ± 3 | 96 | 0.0001 |
| Mismatch = –2 | 897 | 9781 | 10.9 ± 12 | 100 | 98 | 233 | 2.37 ± 3.7 | 89 | 0.0001 |
The ARE-mRNA list of 897 was verified against 3′UTR and CDS for the specificity and database coverage of the 13-bp pattern under different search stringency (e.g. with one mismatch and two mismatches in nucleotides flanking the conserved core) used for computational compilation of ARED.
aNumber of mRNA sequences retrieved by the search.
bThe number of ARE patterns found in each subset.
cMean of finds of the 13-bp ARE pattern per 3′UTR or CDS.
dPercentage coverage = % (number of 3′UTR with ARE pattern /total 897 mRNA sequences).
ePercentage specificity (% sp) = 1 – (CDS containing the pattern/total 897 mRNA sequences). .
fP values indicate statistical significance between the mean of 13-bp ARE pattern per ARE-mRNA using an unpaired t-test with Welch correction (used because of the significantly different variances as verified by F test, P < 0.0001).
N/A = not applicable due to the small number of finds
ARE CLUSTERING OF ARE-mRNAs
The ARE-mRNA database was further clustered into five groups depending on the number of motifs in the ARE stretch (Table 3). Clustering was performed in such a way that, for example, Group I included not only exact five or more continuous ARE pentamers but also those with 10% ambiguity, so that a stretch of NUUUAUUUAUUUAUUUAUUUN would fall in this category. This process was verified by a phylogenic tree relationship using Clustal-W alignment of ARE stretches and their variations. As could be expected, this analysis showed that the lower the number of ARE motifs in a group, the higher the number of sequences that were included, and apparently the more functionally diverse the corresponding ARE genes (Table 3). This may indicate evolutionary conservation within gene families containing long ARE stretches. It has been shown that the number of ARE motifs correlates with the turnover of ARE-mRNAs such as GM-CSF (20). Furthermore, the database also shows that not all cytokines or oncogene mRNAs belong to the ARE family as widely believed. Approximately 90% of the ARE-mRNAs compiled in the new database have not previously been recognized as such; hence only 10% represent previously known ARE-mRNAs. Based on this analysis we estimate the proportion of human mRNAs that contain functional AREs to be in the vicinity of 8%. This suggests, given an estimated number of genes in the human genome of ARE-mRNAs of 35 000–120 000 (21), that the number of ARE-containing genes is between 2800–9600.
Table 3. Diversity of ARE mRNA sequences.
| Group I Cluster (AUUUAUUUAUUUAUUUAUUUA) | ||
| MMSET type I (MMSET) | AF071594 | Cell growth/differentiation |
| GDP-L-fucose:β-d-galactoside 2-α-l-fucosyltransferase | M35531 | Metabolism, carbohydrate |
| Cellular growth-regulating protein | L10844 | Cell growth |
| Gro-β (melanoma stimulating growth factor) | M57731 | Cell growth |
| Pim-1 | M16750 | Signal transduction, oncogenes |
| Neuoron-specific γ-2 enolase | M22349 | Development/differentiation |
| Nuclear matrix protein NRP/B (NRPB) | AF059611 | DNA transcription/differentiation |
| Natural killer cell stimulatory factor (IL-12) | M65290 | Hematopoiesis, immune response |
| Granulocyte–macrophage colony stimulating factor (GM-CSF) | M11220 | Hematopoiesis |
| Adipogenesis inhibitory factor (IL-11) | X58377 | Hematopoiesis, metabolism |
| Natural resistance-associated macrophage protein | D50402 | Immune response |
| Interleukin 1-β | M15330 | Inflammation |
| Tumor necrosis factor | X01394 | Inflammation |
| Group II Cluster (AUUUAUUUAUUUAUUUA) stretch | ||
| Apoptosis-inducing (TRAIL) receptor 2 | AF016849 | Apoptosis |
| Death receptor 5 (DR5) | AF012535 | Apoptosis |
| K-ras oncogene protein | M54968 | Signal transduction |
| Thioredoxin reductase | S79851 | Metabolism, redox |
| Interleukin-10 receptor | U00672 | Immune regulation, receptors |
| Tyrosine kinase (ELK1) oncogene | M25269 | Transcriptional regulation |
| 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase (PFKFB) | AF056320 | Metabolism, carbohydrate |
| Inducible 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase (IPFK-2) | AF109735 | Metabolism, carbohydrate |
| MOP1, basic helix–loop–helix PAS | U29165 | Transcriptional regulation |
| Cytokine-inducible SH2-containing protein (G18) | AF132297 | Signal transduction, inhibitors |
| K-Cl cotransporter KCC4 | AF10536 | Nutrient transport |
| Dishevelled 1 (DVL1) | AF006011 | Signal transduction |
| Guanine nucleotide regulatory factor (LFP40) | U72206 | Cellular motility/biogenesis |
| Zinc finger containing protein ZNF157 (ZNF157) | U28687 | DNA transcription |
| Tubulin-folding cofactor C | U61234 | Cellular motility/biogenesis |
| Interferon(LyIFN-α-1) | E00102 | Immune response, innate |
| α-interferon Gx-1 | E00124 | Immune response, innate |
| Inteferon-α | V00542 | Immune response, innate |
| Leukocyte interferon-α, clone pIFN105 | M28585 | Immune response, innate |
| Interferon α J | E00052 | Immune response, innate |
| Messenger RNA for human leukocyte interferon | V00542 | Immune response, innate |
| Angiotensin/vasopressin receptor AII/AVP | AF054176 | Signal transduction, receptors |
| c-fos proto-oncogene | V01512 | Transcriptional regulation |
| Group III Cluster (WAUUUAUUUAUUUAW) stretch | ||
| Interferon (IFN-α-M1) | M27318 | Immune response, innate |
| B-cell leukemia/lymphoma 2 (bcl-2) proto-oncogene | M13994 | Apoptosis |
| Condoroitin 6-sulfotransferase | AB017915 | Metabolism, sulphate |
| Sodium bicarbonate cotransporter 3 (SLC4A7) | AF047033 | Metabolism, transport |
| Cyclooxygenase-2 (Cox-2) | M90100 | Inflammation |
| Neuralized mRNA | AF029729 | Development, neural |
| Gro (growth regulated) gene | J03561 | Cellular growth |
| Epiregulin | D30783 | Cellular growth |
| A-kinase anchor protein | U17195 | Signal transduction |
| CREB-binding protein | U47741 | Transcriptional regulation |
| L-type amino acid transporter subunit LAT1 | AF104032 | Nutrient transport |
| Zinc finger DNA-binding motifs (IA-1) | M93119 | Transcriptional regulation |
| Interleukin 3 | M20137 | Hematopoiesis |
| E16 | M80244 | Signal transduction, receptors |
| 2-oxoglutarate dehydrogenase | D10523 | Metabolism, energy |
| SIRP-β1 | Y10376 | Signal transduction, inhibitors |
| Putative sulphate transporter (PDS) | AF030880 | Nutrient transport |
| Interleukin 2 (IL2) | U25676 | Immune response, adaptive |
| Post-synaptic density protein 95 (PSD95) | U83192 | Neural regulation |
| Dihydrolipoamide dehydrogenase-binding protein | AF001437 | Metabolism, energy |
| Type 2 iodothyronine deiodinase | AF093774 | Metabolism, hormones |
| RGP G-protein 3 | U27655 | Signal transduction |
| α-Endosulfine | AF067170 | Nutrient transport, regulation |
| Tumor necrosis factor α inducible A20 zinc finger protein | M59465 | Transcriptional regulation |
| Vascular endothelial growth factor | AF022375 | Cellular growth, angiogenesis |
| Inhibitor of apoptosis protein-1 (MIHC) | AF070674 | Apoptosis, regulation |
| A28-RGS14p | U70426 | Signal transduction |
| TNFR2-TRAF signaling complex protein | L49432 | Transcriptional regulation |
| Fibrillin-2 | U03272 | Cellular motility/biogenesis |
| Farnesylated-proteins converting enzyme 1 | Y13834 | Protein modification |
| D-1 dopamine receptor | X58987 | Signal transduction, neural |
| HEB helix–loop–helix protein (HEB) | M80627 | Transcriptional regulation |
| IFN-ω 1 | A12140 | Immune response, innate |
| Interferon β | X04430 | Immune response, innate |
| Lymphotoxin | E01275 | Immune response |
| Musashi/Nrp-1 | AB012851 | Development, neural |
| Thiamine carrier 1 (TC1) | AF153330 | Metabolism, nutrient transport |
| Transcription factor (HTF4A) | M83233 | Transcription |
| Phospholipase C-β-2 | M95678 | Signal transduction |
| Onconeural ventral antigen-1 (Nova-1) | U04840 | Development/differentiaion, neural |
| Protein tyrosine kinase mRNA | M59371 | Signal transduction |
| Tyrosine kinase receptor p145TRK-B (TRK-B) | U12140 | Signal transduction, receptors |
| Protein tyrosine phosphatase | U27193 | Signal transduction, inhibitors |
| Transcriptional regulatory protein p54 | AF045451 | Transcriptional regulation |
| cAMP phosphodiesterase PDE7 (PDE7A1 | L12052 | Signal transduction |
| Retinoic acid receptor γ 1 | M38258 | Transcriptional regulation, receptors |
| Tissue factor (thromboplastin) | A19048 | Inflammation |
| c-sis/platelet derived growth factor 2 (PDGF2) | AF022375 | Cellular growth, oncogenes |
| Endothelial leukocyte adhesion molecule-1 (ELAM-1) | M30640 | Inflammation |
| Cluster IV Group (WWAUUUAUUUAWW) stretch | ||
| 175 sequences available with other group at http://rc.kfshrc.edu.sa/ared | ||
| Cluster V Group (WWWWAUUUAWWWW) stretch | ||
| 582 sequences available with other group at http://rc.kfshrc.edu.sa/ared |
The ARE-mRNAs were clustered into five groups containing five, four, three and two pentameric repeats, while the last group contains only one pentamer within the 13-bp ARE pattern. Functional categories were assigned whenever possible according to NCBI-COG functional annotation (76)—in addition to the categories: inflammation, immune response, Development/Differentiation—using an extensive PubMed literature search. Those ARE-mRNAs, which do not have known biological function in Groups I–III are not shown in the table. Members of the training list (Table 1) are indicated in bold.
BIOLOGICAL DIVERSITY AND INFERENCES IN DISEASE: EXAMPLES
The large number of ARE genes discovered by computational means show that they encode a large and wide variety of gene products in addition to the regulators of cell proliferation or the inflammatory/immune response. Group I (Table 3) contains many secreted proteins including GM-CSF, IL-1, IL-11, IL-12 and Gro-β that affect the growth of hematopoietic and immune cells (22). Although TNF-α is both a pro-inflammatory and anti-tumor protein, there is experimental evidence that it can act as a growth factor in certain leukemias and lymphomas (23). Unlike Group I, Groups II–V contain functionally diverse gene families comprising immune response, cell cycle and proliferation, inflammation and coagulation, angiogenesis, metabolism, energy, DNA binding and transcription, nutrient transportation and ionic homeostasis, protein synthesis, cellular biogenesis, signal transduction, and apoptosis. Only a few cases of ARE mRNAs coding for receptors were previously recognized: notably, urokinase-type plasminogen receptor and epidermal growth factor receptor (Table 1), but a significant number of receptors (69 mRNAs) mediating several biological responses including several hormone receptors were found in ARED. Interestingly, among the known apoptosis-inducing receptors, only TRAIL 2 and its closely-related death receptor 5, fall into the family of ARE mRNAs (Group II). Intriguingly, in some cases both the receptor and its ligand belong to ARED, for example, IL-10/IL-10R and urokinase-type plasminogen/u-PA receptor; indicating the tightly regulated processes of inflammation and cell adhesiveness, respectively (24). Among adhesion molecules, the endothelial leukocyte adhesion molecule-1 (ELAM-1, Group III) appears to be unique among other adhesion molecules in sense that it belongs to ARED.
Several metabolic processes including carbohydrate, amino acid and nucleic acid metabolism are represented in ARED (Table 3). For example, Group III contains several transporters and enzymes necessary for nutrient and nucleotide transport. Enzymes that belong to different functional categories are also present throughout ARED. A large number of transcription factors and DNA binding proteins including zinc finger proteins (at least 30 mRNAs) were present in ARED. Since many early response genes are triggered by signal transduction, many mRNAs in ARED belong to this category (more than 50 entries). In addition, ARED contains a significant number of large (>4 kb) ARE-mRNAs that have no known gene function (∼100 sequence entries; 25). It is apparent from ARED that many ARE-mRNAs mediate transient processes including hormone response, apoptosis, immune response and inflammation, cellular growth, etc. Thus, this compilation of ARE-containing mRNAs may offer further insights into the biology of ARE and relationships to disease processes; examples are given below.
Several human cancer and inflammatory diseases, including certain B-cell lymphomas, neuroblastoma, and chronic inflammatory conditions have been linked to ARE defects (reviewed by Vassalli and co-workers, 3). Removal of the ARE stretch correlates with the increased oncogenicity of proto-oncogene c-fos (Group II, Table 3) (26). Bcl-2 mRNA (Group III, Table 3) is an ARE-containing mRNA in which its increased stability may lead to overproduction of the antiapoptotic BCL-2 protein likely responsible for neoplastic transformation of follicular B-cell lymphoma (27). The development of two specific pathologies of chronic inflammatory arthritis and Crohn’s-like inflammatory bowel disease were seen in mice rendered mutant in their TNF gene (Group I) by deleting the AT-rich region (2).
Increased production of hematopoietic growth factors such GM-CSF acting as autocrine growth factors, due to defects in ARE-mediated stability, may contribute to the pathogenesis of leukemia (28,29). TRAIL 2 is known to be involved in apoptosis of tumor cells but not normal cells (30), thus, prolongation of TRAIL2 mRNA stability may cause unwanted apoptotic events instead of transient apoptotic events in normal cells. Other notable examples of the role of receptors in disease are the angiotensin/vasopressin receptor (Group II) and D1 dopamine receptor (Group III), which may be involved in hypertension (31,32). The vascular endothelial growth factor/vascular permeability factor (Group III) which affects angiogenesis is thought to promote the cancer survival process (33).
The present identification and clustering of new ARE-mRNAs together with previously known ARE-mRNAs may facilitate our understanding of how these genes are regulated and how they may possibly be involved in disease processes. Thus, the investigation of regulatory pathways and pathological processes that involve ARE-mediated action may benefit greatly from the bioinformatics approach described here, and the availability of the ARE-mRNA database.
ACCESS AND FUTURE DIRECTION
We are planning to expand the list of ARE-mRNAs to include joined CDS from GenBank entries and to refine redundancy. It is our intent to provide at least one updated release of ARED per year. The web site http://rc.kfshr c.e du . sa/ared contains flat files of ARED database and their cluster groups. Also, database description and tables are available at the same site. The ARED files are in GenBank flatfile view (i.e. nucleotide sequence with annotations) that can be either downloaded or directly viewed when opened with Internet browsers.
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