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LncRNA loc285194 is a p53-regulated tumor suppressor

Protein-coding genes account for only a small part of the human genome, whereas the vast majority of transcripts make up the non-coding RNAs including long non-coding RNAs (lncRNAs). Accumulating evidence indicates that lncRNAs could play a critical role in regulation of cellular processes such as cell growth and apoptosis as well as cancer progression and metastasis. LncRNA loc285194 was previously shown to be within a tumor suppressor unit in osteosarcoma and to suppress tumor cell growth. However, it is unknown regarding the regulation of loc285194. Moreover, the underlying mechanism by which loc285194 functions as a potential tumor suppressor is elusive. In this study, we show that loc285194 is a p53 transcription target; ectopic expression of loc285194 inhibits tumor cell growth both in vitro and in vivo. Through deletion analysis, we identify an active region responsible for tumor cell growth inhibition within exon 4, which harbors two miR-211 binding sites. Importantly, this loc285194-mediated growth inhibition is in part due to specific suppression of miR-211. We further demonstrate a reciprocal repression between loc285194 and miR-211; in contrast to loc285194, miR-211 promotes cell growth. Finally, we detect downregulation of loc285194 in colon cancer specimens by quantitative PCR arrays and in situ hybridization of tissue microarrays. Together, these results suggest that loc285194 is a p53-regulated tumor suppressor, which acts in part through repression of miR-211.

INTRODUCTION
Recent advances in functional genomics have led to the discovery of a new type of regulatory genes, i.e. long non-coding RNAs (lncRNAs), which are >200 bases in length. Although they are less well characterized compared with small non-coding microRNAs (1)(2)(3)(4)(5), increasing evidence suggests that lncRNAs could play a critical role in regulation of diverse cellular processes such as stem cell pluripotency, development, cell growth and apoptosis and cancer metastasis (6)(7)(8)(9)(10)(11)(12)(13). In this regard, lncRNAs may function (i) as signals for transcription; (ii) as decoys to titrate transcription factors; (iii) as guides so that chromatin-modifying enzymes can be recruited to target genes; and (iv) as scaffolds to bring together multiple proteins to form ribonucleoprotein complexes (14,15). An additional function may include serving as a 'sponge' to titrate microRNAs (16).
Accordingly, lncRNAs may function as oncogenes and tumor suppressors in cancer just like protein-coding genes and microRNAs. For example, HOTAIR is one of the first identified lncRNAs and plays a critical role in cancer through epigenetic regulation mechanisms. HOTAIR is a 2.2 kb gene in the HOXC locus, which, however, can repress transcription in trans of HOXD genes. This repressive action is mediated by the interaction of HOTAIR with the Polycomb Repressive Complex 2 (17). Furthermore, HOTAIR is remarkably overexpressed in breast tumors, and the expression of HOTAIR in primary breast tumors is a strong prognosis marker of patient outcomes such as metastasis and patient survival (6).
Loc285194, also called LSAMP antisense RNA 3, is an lncRNA consisting of 4 exons with >2 kbs in length (Gene ID: 285194) and is located at osteo3q13.31 (18). As the osteo3q13.31 locus harbors frequent focal copy number alterations (CNAs) and loss of heterozygosity in primary osteosarcoma samples, it implies that loc285194 may function as a potential tumor suppressor. Furthermore, the tumor suppression function of loc285194 was also suggested by knockdown experiments, which showed an increased cell proliferation (18). However, little is known as to how loc285194 is regulated in cancer cells; moreover, the underlying mechanism of loc285194 as a tumor suppressor is elusive.
In the present study, we show that loc285194 is a direct transcription target of p53 through interaction with the putative p53 response element in the upstream region of loc285194. Moreover, loc285194 suppresses cell growth both in vitro and in vivo; consistently, loc285194 is downregulated in colon tumor specimens. Finally, we demonstrate that loc285194 negatively regulates miR-211, which may in part account for loc285194-mediated cell growth inhibition. Of considerable interest, miR-211 promotes cell growth, and, at the same time, it represses loc285194 expression, thus forming a reciprocal repression feedback loop, which may function as a part of 'competitive endogenous RNA' network (19).

MATERIALS AND METHODS
Reagents, cell culture, western blot and transfection were described in Supplementary Materials.

LncRNA profiling
To focus on those clinically relevant lncRNAs, we used Human Disease-Related LncRNA Profiler (CAT# RA920D, System Biosciences (SBI)) consisting of 83 lncRNAs, which were selected from the lncRNA database (www.lncRNAdb.org) (20) or RNA database (http://research.imb.uq.edu.au/rnadb/Default.aspx). Total RNA was isolated from HCT-116 WT cells treated with doxorubicin (doxo) at 1 mg/ml for 24 h. Reverse transcription was carried out by using RevertAid TM Reverse Transcriptase (Fermentas) and random primer mix (New England BioLabs). The values for the cells without doxo treatment after normalization by the internal controls served as a basal level of expression of loc285194; delta-delta Ct values (no doxo versus doxo) were used to determine their relative expression as fold changes, as previously described (21).

Plasmid construction
All PCR primers for cloning were listed in Supplementary  Table S1 and PCR reactions for cloning purpose used Phusion enzyme (New England BioLabs). Cloning of loc285194 was described in Supplementary Figure S3 legend. The same strategy was used to generate other constructs in this study, otherwise stated. To clone miR-211, we first amplified $500 bp sequence containing the miR-211 precursor from human genomic DNA by PCR using primers hsa-miR-211-5.1 and hsa-miR-211-3.1 and then cloned it into pCDH-CMV-EF1-copGFP vector (CAT#CD511B-1, SBI). The cloning of loc285194 putative promoter used human genomic DNA as a template and PCR primers Loc285194p-Xho1-5.1 and Loc285194p-Xho1-3.1, and the product was then cloned into pGL3-basic (Promega) at Xho1 site. To generate loc285194 promoter constructs carrying deletions or mutations, two-step PCR procedure was used as described previously (22). All PCR products were verified by DNA sequencing.

Luciferase assay
Luciferase assays were performed using a luciferase assay kit (Promega) according to the manufacturer's protocol, as previously described (21). Briefly, cells were first transfected with appropriate plasmids in 12-well plates. Then, the cells were harvested and lysed for luciferase assay 24 h after transfection. b-galactosidase or renilla luciferase was used for normalization.

Quantitative RT-PCR
To detect loc285194 expression, we used the SYBR Green method with primers listed in Supplementary  Table S1. b-actin was used as an internal control. To detect mature miR-211 expression, and to perform microRNA profiling to determine which microRNAs interact with loc285194, we used the poly A polymerasebased SYBR Green method (QuantMiR kit from SBI). U6 RNA was used as an internal control. In both cases, delta-delta Ct values were used to determine their relative expression as fold changes, as previously described (21). To detect pri-and pre-miR-211 expression, we used a standard RT-PCR procedure with primers pri-miR-211-RT-5.1 and pri-miR-211-RT-3.1, and pre-miR-211-RT-5.1 and pre-miR-211-RT-3.1, respectively. Sequences of these primers were listed in Supplementary Table S1.

Cell proliferation assay
Cells were first transfected with vector or loc285194; negative siRNA or loc285194 siRNA overnight and then split into 12-well plates. Cell growth assays were performed using the trypan blue method in Vi-Cell (Beckman Coulter) over 4 days of incubation.

Chromatin immunoprecipitation
Chromatin immunoprecipitation (ChIP) assays were performed using a commercial kit from Cell Signaling. Briefly, cells were first fixed with 1% formaldehyde, and chromatin DNA was isolated and bound protein was digested with proteinase K. PCR was performed using primers Loc285194-ChIP-p53-5.2 and Loc285194-ChIP-p53-3.1. Immunoglobulin G and non-specific antibody (anti-SUMO) were used as negative controls. The p53 binding site at the p21 promoter was used as a positive control.

RNA precipitation
To determine whether loc285194 is associated with the RNA-induced silencing complex (RISC) complex, we performed RNA precipitation assay using synthesized biotin-labeled loc285194 as a probe and then detected Ago 2 from the pellet by western or detected miR-211 by quantitative RT-PCR (qRT-PCR). In brief, the DNA fragment covering loc285194 exon 4 was PCR-amplified using a T7 containing primer and then cloned into pCR8 (Invitrogen). In addition, another lncRNA, GAS5 (23), was also cloned and used in precipitation experiments for comparison. The resultant plasmid DNA was linearized with restriction enzyme Not I, which was introduced from the reverse PCR primer, and then used to synthesize RNA by T7 polymerase. A 20 ml reaction contained 400 ng linearized plasmid DNA, 20 U ribonuclease inhibitor, 2.5 mM NTP mixture supplemented with 10% biotin labeled UTP (Perkin Elmer) and 20 U T7 RNA polymerase (New England BioLabs); and then it was incubated at 37 C for 60 min, followed by 25 U RNase-free DNase I (New England BioLabs) at 37 C for 30 min. The labeled RNA was purified by a column-based kit (Zymo Research). Cellular extract was prepared from a 10 cm dish culture ($80% confluence) with cell lysis buffer (24). For precipitation assays, the reaction (RNA probe and cellular extract) was incubated at 4 C for 60 min, followed by five washes with phosphate buffered saline. The pellets were used either for extraction of RNA for RT-PCR or for western blot according to standard procedures.

In situ hybridization
In situ hybridization (ISH) was used to detect loc285194 in clinical specimen and cancer cell lines after doxo treatment based on a previously described method (25) with some modifications. In brief, a biotin labeled antisense LNA probe was derived from exon 4 of loc285194 (Supplementary Table S1); a sense probe was used as a negative control. Prehybridization and hybridization were carried out at 67 C for 30 min and 4 h, respectively. The relative signal was assessed based on the intensity as 0 (negative), like no probe control or negative control; +(weak positive) and + + (strong positive). To detect loc285194 induction by p53 in cell culture, we seeded HCT-116 WT cells on coverslips overnight and then treated HCT-116 WT cells with 1 mg/ml of doxo for 24 h before fixing the cells with 2% formaldehyde for 15 min at room temperature.

Xenograft model
Animal work to determine the role of loc285194 on tumor growth was performed according to the procedures as previously described (22). All animal studies were conducted in accordance with NIH animal use guidelines and a protocol approved by SIU Animal Care Committee. In brief, HCT-116 WT cells were first transfected with vector alone or loc285194 expression vector overnight. The cells were then split into new dishes and were harvested at the exponential growing stage when they reached $70% confluence. About 1.5 million cells in 50% matrigel were injected subcutaneously into the flanks, one injection per mouse. Tumor growth was monitored, and tumor size was measured every other day. Tumor volume was calculated using the formula, volume = 1/2(length Â width 2 ).

Statistical analysis
Statistical analysis of data was performed using the Student's t test. Differences with P-values <0.05 are considered significant.

Induction of loc285194 by p53
Although there are overwhelming numbers of non-coding RNAs that can be transcribed from the human and mouse genome (26,27), a relative small number of lncRNAs have been characterized and shown to be associated with human diseases. Therefore, we attempted to focus on those clinically relevant lncRNAs by using a real-time RT-PCR based array (Human Disease Related LncRNA Profiler) consisting of 83 lncRNAs (Supplementary Figure  S1A). We validated the lncRNA Profiler in various cell lines; only single product was seen for each set of primers (Supplementary Figure S1B).
As p53 is the well-known tumor suppressor that regulates a variety of cellular functions and disease processes, we simply asked whether any of these lncRNAs is induced by p53. We first treated HCT-116 cells expressing wildtype p53 (HCT-116 WT) with various concentrations of doxorubicin (doxo), a known DNA damaging agent, for 24 h and detected p53 induction in a dose-dependent manner, as expected ( Figure 1A). Therefore, we then chose doxo at 1 mg/ml for 24 h for profiling experiments and identified 7 lncRNAs with over a 10-fold of induction by p53 from the preliminary profiling (Supplementary Figure S2). We were particularly interested in loc285194 because a previous report suggests its potential tumor suppressive role in osteosarcoma (18). To further confirm the specific effect of p53 on loc285194 expression, we treated HCT-116 p53 null (HCT-116 null) cells at the same concentration of doxo. Although doxo also induced loc285294 in HCT-116 null cells, the level of induction was much lower than that in HCT-116-WT cells ( Figure 1B). Next, we examined loc285294 expression using a different set of primers derived from a different region of loc285194 ( Figure 1C), further confirming this p53-mediated induction. In addition, such induction was also detected in other cell lines expressing wild-type p53, i.e. MCF-7, HCT-8 and A549 ( Figure 1C) with $8-, 5and 7-fold, respectively. Moreover, we performed in situ hybridization to detect the level of loc285194 in the cells after doxo treatment. We observed a substantial increase of loc285194 in the doxo-treated cells compared with no doxo control ( Figure 1D). Of note, we found that a majority signal came from the cytoplasm. As doxo may also induce other cellular responses independent of p53, leading to gene expression, we ectopically expressed p53 to determine whether this loc285194 induction is specific to p53. As shown in Figure 1E, we detected over a 3-fold increase in the loc285194 level, similar to the induction of a known p53-regulated gene, miR-145 (22) ( Figure 1E, right). Of considerable interest, p53 with a point mutation (R175H) at the DNA-binding domain, a frequent mutant in cancer (28), had no effect on loc285194 expression, suggesting that loc285194 is specifically induced by wild-type p53. Therefore, loc285194 was selected for further characterization of the p53-mediated induction and its role in tumorigenesis.
p53 directly interacts with the p53 response element in the upstream region of loc285194 p53 is also a well-known transcription factor. To determine whether p53 transcriptionally regulates loc285194, we used the p53 scan program (29) to analyze the upstream region of loc285194 and identified a putative p53 response element (p53RE) at $1500 upstream of loc285194 ( Figure 2A). Thus, we cloned this 2-kb fragment into pGL3 basic (Promega). Luciferase assays indicated that p53 induced the promoter activity by over a 2.5-fold ( Figure 2B), which was comparable with the induction of the miR-145 promoter (22). Consistent with the induction of loc285194 at the RNA level ( Figure 1E), we found that mutant p53 had no effect on the promoter activity ( Figure 2B). To further determine the function of this p53RE, we made two deletions involving p53RE ( Figure 2C) and demonstrated that these deletions caused a significant reduction of the activity compared with the full-length (2 kb) promoter construct ( Figure 2D). For example, the luciferase activity for deletions involving p53RE was <40% of the full length promoter. Furthermore, site-directed mutagenesis involving the conserved C and G of p53RE (Figure 2A) also yielded <40% of luciferase activity ( Figure 2D). Finally, we showed that p53 directly interacted with p53RE by ChIP assays ( Figure 2E). Together, these results suggest that p53 interacts with the p53RE in the loc285194 promoter to induce its transcription.

Loc285194 inhibits tumor cell growth
It is known that p53 can induce a large number of genes including tumor suppressors such as p21. To evaluate the biological consequence of p53 induction of loc285194 and the role of loc285194 in tumorigenesis, we ectopically expressed loc285194 in HCT-116 WT and MCF-7 cells (Supplementary Figure S3 and S4A) for cell proliferation assays. As expected, loc285194 significantly inhibited tumor cell growth ( Figure 3A). This loc285194-mediated cell growth inhibition was also seen in MCF-7 cells ( Figure 3B). Loc285194 consists of 4 exons with a total length of $2.1 kb. Thus, we made various deletion constructs to define which region of loc285194 is responsible for cell growth inhibition ( Figure 3C). This deletion analysis identified the active region within exon 4 and the C-terminal 600 bp fragment of exon 4 (E4-d) was still able to suppress cell growth ( Figure 3D). To further determine the role of loc285194 in cell growth inhibition, we suppressed loc285194 by RNAi (Supplementary Figure   Figure 2. p53 induces loc285194 by direct interacting with loc285194 promoter. (A) Schematic description of loc285194 promoter with a potential p53 response element (p53RE). Also shown are sequences for mutant p53RE and p53RE consensus. Relative position of primers used for ChIP assay (E) was indicated by arrows. (B) Induction of loc285194 promoter activity by wild-type p53, but not mutant p53. HCT-116 WT cells were transfected with loc285194 luciferase reporter (loc-p-FL) along with vector, wild-type p53 or mutant p53 (R175H). The cells were harvested for luciferase assays 24 h after transfection. The miR-145 promoter reporter served as a positive control. Error bars represent SEM, n = 3. **P < 0.01. (C) Deletion analysis of the promoter activity to determine the role of the potential p53RE in p53 regulation of loc285194. Loc-p-FL is a full-length promoter; Loc-p-d1 and Loc-p-d2 carry a deletion involving p53RE; Loc-p-m is a mutant at p53RE, as indicated in Figure 2A. (D) Relative luciferase activity for corresponding constructs in Figure 2C. Luciferase assay was conducted as in Figure 2B. Error bars represent SEM, n = 3. **P < 0.01; *P < 0.05. (E) Confirmation of p53 binding to p53RE in loc285194 promoter as detected by ChIP assay. Control primers Loc285194-ChIP-Ctrl-5.1 and Loc285194-ChIP-Ctrl-5.1 were derived from outside of p53RE. Immunoglobulin G and SUMO antibody were used as negative controls; p21 served as a positive control.
S4B), and both loc285194 siRNA-1 (18) and siRNA-2 (Supplementary Table S1) significantly suppressed the endogenous loc285194. At the same time, loc-siRNA increased cell growth in HCT-116-WT cells ( Figure 3E) as well as in HCT-116 null cells ( Figure 3F), suggesting that loc285194 may target genes involved in cell growth and proliferation.
To further determine the tumor suppressive role of loc285194, we injected HCT-116 WT cells transfected with vector or loc285194 into nude mice. Compared with vector control, loc285194 significantly suppressed tumor growth based on tumor growth rate ( Figure 4A) and final tumor weight ( Figure 4B and C). For example, loc285294 caused >50% reduction of tumor weight compared with vector control. Therefore, loc285194 suppresses tumor cell growth not only in vitro but also in vivo, further suggesting that loc285194 is a p53 downstream effector, functioning as a tumor suppressor.

Reciprocal repression of miR-211 and loc285194
The importance of lncRNAs in human diseases may have to do with their ability to regulate gene expression. Moreover, a major feature for lncRNAs is their ability to interact with either protein, DNA or RNA to exert their functions. For example, lncRNAs may exert their functions through interaction with regulatory proteins such as those chromatin remodeling proteins (30). In this regard, several lncRNAs have been shown to interact with Polycomb Repressive Complex 2 components such as Ezh2 (12). However, our RNA immunoprecipitation with specific Ezh2 antibody found no evidence of such interactions. Therefore, we searched for potential interactions with microRNAs because emerging evidence suggests that non-coding RNAs may participate in 'competitive endogenous RNAs' regulatory network (19). For example, there is a negative correlation between miR-155 and the ncRNA UC34A (31). Moreover, a muscle-specific lncRNA, linc-MD1, may act as a 'sponges' for miR-133 (16). Using microRNA.org-target program (www. microRNA.org), we found that 32 microRNAs formed complementary base paring with loc285194, and their relative positions were listed in Supplementary Figure  S5. To determine whether any of them is truly regulated by loc285194, we profiled expression of these 32 microRNAs in HCT-116 WT transfected with vector or loc285194. The initial profiling identified 3 microRNAs (miR-24, miR-376c and miR-211) were reduced to <25% of the control by loc285194 (Supplementary Figure S6). As there were two miR-211 binding sites in exon 4 of loc285194 ( Figure 5A), we deleted these two sites (Loc-d), and this construct was no longer able to suppress miR-211 ( Figure 5B). To determine whether miR-211 is able to negatively regulate loc285194, we also cloned miR-211 and then introduced it into HCT-116 WT cells. As shown in Figure 5C, ectopic expression of miR-211 reduced the loc285194 level by almost 60%.
To dissect the underlying mechanism of this negative regulation of miR-211 by loc285194, we examined the effect of ectopic expression of loc285194 on the level of mature miR-211, pri-miR-211 and pre-miR-211. As shown in Figure 5D, although loc285194 caused a reduction of mature miR-211, it had no effect on pri-miR-211 or pre-miR-211, suggesting a possible post-transcriptional regulation involved. It is well known that microRNAs exert their silencing functions through the RISC, and potential microRNA targets can be isolated from this complex after Ago2 co-immunoprecipitation (32) because Ago2 is a key component of RISC complex required for siRNA or microRNA-mediated gene silencing. However, confirmation of miR-211 and loc285194 in the RISC complex does not exclude the possibility that miR-211 and loc285194 may be in separate RISC complexes. Therefore, to determine whether both loc285194 and miR-211 are in the same RISC complex, we performed RNA precipitation experiments using loc285194 RNA probe and then detected Ago2 and miR-211 simultaneously. We first synthesized a biotin-labeled loc285194 RNA probe by T7 polymerase and then mixed with cellular extract. After pulldown with streptavidin beads, RNA or protein that interacts with the probe is expected to be co-precipitated with the biotin-labeled probe. Indeed, we detected Ago2 ( Figure 5E), a key component of RISC complex, which is known to be required for siRNA or microRNA-mediated gene silencing. Importantly, we detected miR-211 in the same pellet ( Figure 5F). We used this approach instead of immunoprecipitation with Ago2 antibody to pulldown the RISC complex so that we would be able to reduce the possibility that loc285104 and miR-211 are in separate RISC complex.
To determine the specificity of this interaction, we also made a probe from another lncRNA, GAS5, which was investigated in our laboratory for a different project, as a control. Although lncRNA GAS5 probe also precipitated Ago2, there was no significant amount of miR-211 in this pellet ( Figure 5F). Together, these results suggest that both loc285194 and miR-211 are associated with the RISC complex through which loc285194 is able to reduce the miR-211 level and vice versa. It also implies that loc285194 and miR-211 may regulate each other in a way similar to the microRNA-mediated silencing of protein-coding genes.
The role of miR-211 in cancer appears to be cell-type specific. In melanoma, miR-211 was reported to function as a tumor suppressor (33). However, in other types of cancers such as colon and oral cancers, miR-211 may function as an oncogene (34,35) because ectopic expression of miR-211 promotes cell growth. Therefore, we asked whether ectopic expression of miR-211 (Supplementary Figure S7) promotes cell growth in colon cancer HCT-116 cells. As expected, miR-211 significantly increased cell growth in HCT-116 WT cells ( Figure 6A). This miR-211-promoted cell growth was also seen in breast cancer MCF-7 cells (Figure 6B), suggesting that loc285194 inhibits tumor cell growth in part through negative regulation of miR-211.

Loc285194 is downregulated in colon tumors
In light of these findings, we then determined whether loc285194 is dysregulated in clinical specimens. We used Colon Cancer cDNA Array IV from OriGene, which contained cDNAs derived from 8 normal and 40 colon tumor specimens. The quantitative PCR analysis revealed that loc285194 was undetectable in 2 of the 40 tumor samples, which could be due to deletion of the locus. For the rest 38 tumor samples, each of them expressed loc285194 at a level lower than the average level of normal specimens, with the average expression level of 0.18 compared with normal tissue as 1 ( Figure 7A).
We also determined the expression of loc285194 in colon cancer tissue microarrays by in situ hybridization. Consistent with qRT-PCR results, loc285194 was also downregulated in tumor specimens compared with normal specimens (Figure 7B and C). For example, all of normal tissue expressed a certain level of loc285194. However, no signal was detected in 31 of 79 tumor specimens ( Figure 7C), similar to negative controls (Supplementary Figure S8). These results may imply that loc285194 may serve as a diagnostic marker for colon cancer.

DISCUSSION
As a newly discovered class of non-coding genes, lncRNAs have been recently found to be pervasively transcribed in the genome. Alterations in the primary structure, secondary structure and expression levels of lncRNAs as well as their cognate RNA-binding proteins are often associated with human diseases, in particular cancer (36). We present evidence that loc285194 is a direct target for p53 and functions as a tumor suppressor in part through negative regulation of miR-211. These results provide further supporting evidence that there exists a competitive endogenous RNA regulatory network (19) where loc285194 and miR-211 negatively regulate each other.
We show that loc285194 inhibits tumor cell growth both in vitro and in vivo. In contrast, suppression of loc285194 by RNAi promotes tumor cell growth. Furthermore, loc285194 is downregulated in colon tumor specimens (Figure 7). This is consistent with the finding that focal osteo3q13.31 CNAs and loss of heterozygosity are also common in cell lines from other cancers (18). Although CNAs may account in part for the downregulation of loc285194, our study indicates that other factors such as p53 status may also contribute to this downregulation because p53 mutation or deletion could account for up to 50% cancer cases.
Although lncRNAs impact cellular functions through various mechanisms, such as interactions with chromatin remodeling proteins (17,30), they are able to regulate other non-coding RNAs, in particular microRNAs. In support of this notion, we show that this loc285194-mediated suppression of cell growth is at least in part through suppression of miR-211. Ectopic expression of loc285194 reduces the miR-211 level and the miR-211-binding sites are critical for this loc285194-mediated repression. In contrast, ectopic expression of miR-211 can also suppress the loc285194 level, thus, forming a reciprocal repression feedback loop. In this regard, loc285194 may function as the endogenous sponge, similar to what has been reported for ncRNA UC34A (31), HULC (37) and linc-MD1 (16). For example, HULC is highly upregulated in liver cancer (38) and plays an important role in tumorigenesis. In particular, HULC can downregulate miR-372 through its interaction with miR-372 (37). In this scenario, microRNA response elements may serve as letters of a new language (19) through which microRNAs may regulate not only protein-coding genes but also non-coding genes. For example, three recent articles provide further evidence of competitive endogenous RNAs involving the tumor suppressor PTEN (39)(40)(41). In another case, a viral non-coding RNA is able to repress a host microRNA (42). Our study further suggests that this reciprocal repression of loc285194 and miR-211 is likely through the pathway involving RISC complex.
The function of miR-211 seems to be dependent on the cellular content. For example, miR-211 is downregulated in melanoma cells and melanoblasts compared with melanocytes (43,44). This may be related to the fact that miR-211 is a pigment-cell-enriched miRNA because miR-211 and TRPM1 (melastatin) are under control of the microphthalmia-associated transcription factor, which is frequently downregulated in melanoma. Ectopic expression of miR-211 suppresses migration and invasion of malignant and highly invasive human melanomas (33), thus functioning as a tumor suppressor. On the other hand, miR-211 was shown to promote tumorigenesis in colon and oral cancer (34,35). For example, miR-211 enhances the proliferation, migration and anchorage-independent colony formation of oral carcinoma cells (35). In colon cancer, a recent report indicated that miR-211 promotes cell proliferation, tumor growth and cell migration of HCT-116 cells. In particular, chromodomain-helicase-DNA-binding protein 5 (CHD5) was shown to be a direct target for miR-211 (34). Given that CHD5 is often downregulated and miR-211 is upregulated in colon cancer, this negative correlation highlights the importance of miR-211 in colon tumorigenesis. Our finding provides another possible mechanism by which miR-211 promotes tumor cell growth. As a master regulator for gene expression, p53 is able to directly or indirectly regulate numerous protein-coding genes and non-coding genes, especially microRNAs. For example, several groups have reported that the miR-34 family, including miR-34a, miR-34b and miR-34c, are induced by DNA damage and oncogenic stress in a p53-dependent manner (45)(46)(47). Similarly, we have shown that miR-145 is an important microRNA that is induced by p53 (22). The present study suggests that lncRNAs can also be subject to p53 regulation. For example, our initial profiling identified that a number of lncRNAs, in addition to loc285194, might be induced by p53. Thus, further characterization of those potential lncRNAs would strengthen the notion that, like protein-coding genes, lncRNAs are also a component of the p53-regulatory network. Apparently, at least two important lncRNAs, mouse lincRNA-p21 and human PANDA, have been shown to be p53 transcription targets (8,48), involved in the p53-mediated cellular processes. Therefore, the identification of loc285194 as a p53 inducible lncRNA expands the repertoire of p53-regulated genes.

SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online: Supplementary Table 1 and Supplementary Figures 1-8.


extfieldsofstudy: Biology Medicine

text:
Mediator Subunit18 Controls Flowering Time and Floral Organ Identity in Arabidopsis

Mediator is a conserved multi-protein complex that plays an important role in regulating transcription by mediating interactions between transcriptional activator proteins and RNA polymerase II. Much evidence exists that Mediator plays a constitutive role in the transcription of all genes transcribed by RNA polymerase II. However, evidence is mounting that specific Mediator subunits may control the developmental regulation of specific subsets of RNA polymerase II-dependent genes. Although the Mediator complex has been extensively studied in yeast and mammals, only a few reports on Mediator function in flowering time control of plants, little is known about Mediator function in floral organ identity. Here we show that in Arabidopsis thaliana, MEDIATOR SUBUNIT 18 (MED18) affects flowering time and floral organ formation through FLOWERING LOCUS C (FLC) and AGAMOUS (AG). A MED18 loss-of-function mutant showed a remarkable syndrome of later flowering and altered floral organ number. We show that FLC and AG mRNA levels and AG expression patterns are altered in the mutant. Our results support parallels between the regulation of FLC and AG and demonstrate a developmental role for Mediator in plants.

Introduction
Mediator plays an important role in regulating RNA polymerase II (Pol II) transcription. The Mediator complex contains 22-28 subunits, and mediates interactions between transcriptional coactivators and Pol II [1,2]. Mediator is evolutionarily conserved and has an ancient eukaryotic origin [3]; it is found in organisms from fungi to mammals and plants, although the evolutionary conservation of individual subunits is moderate [4][5][6]. More than 30 different subunits have been described that are part of the Mediator complex in different organisms, but only about 20 subunits are found in all eukaryotes [6][7][8]. The others consist of either species-specific subunits or other ancillary subunits associated with activation of specific genes. Med18 is one subunit of the Mediator complex and a component of the head module that is involved in stimulating basal RNA Pol II transcription in yeast [4,9].
Arabidopsis Mediator was first found to contain 27 subunits, and most of them are conserved in eukaryotes [5]. Until now, all the known yeast/metazoan Mediator components have been identified in plants [10]. PHYTOCHROME and FLOWERING TIME1 (PFT1), now known as MED25, was identified as a factor of a Phytochrome B (phyB) signaling pathway that promotes flowering and controls final organ size [5,[11][12][13][14]. STRUWWELP-ETER (SWP)/MED14 was reported to be a nuclear protein that plays a role in defining the duration of cell proliferation [15]. Some Mediator subunits like MED25, MED8, MED16, and MED21 act as integrators in response to environmental cues in Arabidopsis [16][17][18][19][20][21]. MED18 is a subunit of the head submodule of the plant Mediator complex [10]. Recently, Kim et al. (2011) reported that several Mediator subunits including Mediator 18 (MED18) are required for microRNA biogenesis [22]. Little is known about Mediator function during floral organ formation or its role in the regulation of flowering time.
The transition from vegetative growth to reproductive development in Arabidopsis is regulated by multiple floral induction pathways. Genetic studies of the timing of flowering in Arabidopsis have revealed 5 major pathways [23]. The photoperiod and vernalization pathways integrate external signals into the floral decision, the autonomous and gibberellin (GA) pathways act independently of environmental cues, whereas the endogenous pathway adds plant age to the control of flowering time. The flowering pathways are interconnected and converge on a few floral integrators, such as FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1 or AGL20) [24][25][26]. One important regulator of floral initiation is the MADS-box transcription factor FLOWERING LOCUS C (FLC), which acts as a negative regulator of flowering in response to both endogenous and environmental signals; it is also an integrator of the autonomous and vernalization pathways [23,27,28]. The autonomous and vernalization pathways both suppress the expression of FLC [29], resulting in a decreased expression of FLC and consequently increased expression of FT and/or SOC1 in a later developmental stage or after a prolonged exposure of plants to low temperature [23,30]. The florigen, FT, directly regulates floral meristem identity genes such as APETALA1 (AP1) and initiates floral morphogenesis [31,32].
Following the vegetative to floral transition, Arabidopsis flowers develop four different organ types that are arranged in concentric whorls: the first whorl contains four sepals, the second whorl contains four petals, six stamens develop in the third whorl, and two fused carpels form in the fourth whorl. The control of floral organ identity has been intensively studied in the past 25 years. Analysis of floral homeotic mutants led to the proposal of a simple genetic model, explaining how three groups of regulatory genes (class A, B and C genes) alone or by interactions, control the organ identity of the four floral whorls [33,34]. The termination of stem cells in the floral meristem requires AGAMOUS (AG), a MADSdomain transcription factor [35]; As a class C floral homeotic gene, AG specifies stamen identity together with the B class and SEPALLATA (SEP) genes and carpel identity together with the SEP genes [34,36]. AG activates SPOROCYTELESS (SPL), which controls sporogenesis in both stamens and carpels [37]. Clearly, AG is one of the most important regulators for the floral transition, floral organ identity, and spore formation.
In this study, we describe the function of Arabidopsis Mediator subunit 18 (MED18), and show that it controls flowering time and floral organ identity by transcriptional regulation of FLC and AG.

Plant Materials and Growth Conditions
All the transgenic and mutant Arabidopsis lines used in this study were of ecotype Columbia (Col) except for the ag-1 and pi-1 mutants, which were in the Landsberg erecta (Ler) background. Plants were grown in a temperature controlled greenhouse at 22-24uC, with a relative humidity of 60%-70%, and a16 hours light and 8 hours dark photoperiod.
For short day experiments, Plants were grown in a growth chamber equipped with 40 W fluorescent light tubes with a 9 hours light and 15 hours dark photoperiod.

Analysis of Transcripts Levels
Semi-quantitative RT-PCR was used to measure the transcript levels of MED18 and the floral homeotic genes using ACTIN4 (At5g59370) and GAPC (AT3G04120) as loading controls. Total RNA samples were treated extensively with RNase-free DNase I to remove any contaminating genomic DNA. First-strand cDNA was synthesized using 1 mg of total RNA in a 20 ml reaction volume using High Capacity cDNA Archive Kit from Applied Biosystems (Foster City, CA) according to the manufacturer's instructions, followed by phenol/chloroform purification, and ethanol precipitation. The cDNA were dissolved in 30 ml TE buffer and 1 ml was subjected to PCR in a 20 ml reaction volume. The RT-PCR runs were 20 to 30 cycles, depending on the linear range of PCR amplification for each gene, with cycle parameters of 94uC for 0.5 min, 58uC for 0.5 min, and 72uC for 1 min for each cycle, with a final incubation of 72uC for 10 min. AP1, AP2, AP3, PI, and AG, primers were designed according to published sequences [38]. All other primers designed in this study were in Table S3.
Quantitative RT-PCR was modified from a previously published method [39]. Mutant and wildtype seedling or flowers were dissected and pooled. Total RNA was extracted using RNeasy plus micro kit (Qiagen) and RNA quantity (.100 ng/ml) and purity (260/280.2.0, 260/230.1.65) were determined using a Nanodrop. RNA integrity (RIN.8.5) and 28S/18S ratio (.1.5) was assessed using a Bioanalyzer 2100 (Agilent Technologies). A quantity of 500 ng of high-quality RNA for each pooled sample was converted into cDNA using the iScript cDNA Synthesis Kit (Bio-Rad). Gene expression was determined using the CFX96TM Real Time system (Bio-Rad) with Act2/8 [40] as a control. FT, SOC1 and FLC Primers were designed according to Zhou and Ni [41]. The results were determined using DDCt method [42], 4 replicates of pooled samples were used for both wildtype and mutant seedlings and flowers.

In situ Hybridization
We used previously established methods for in situ hybridization [43] with the following modifications. To generate templates for in situ probe synthesis, a cDNA was PCR amplified using primers that contained the phage T7 RNA polymerase initiation sequence. The PCR product was used for in vitro transcription of digoxigenin-labeled probes using a DIG-RNA labeling kit (Roche Applied Science). DIG-labeled RNA probes were not hydrolyzed, and used at a final concentration of 400 ng/ml in the hybridization solution. Slides were photographed under bright field illumination.

Statistical Analysis
All group differences in our dependent variables were revealed using two-tailed Student's T-tests, and a-Levels were set at 0.05.

MED18 Controls Flowering Time and Floral Organ Identity
The MED18 (NP_565534; At2g22370) gene was originally identified through a phylogenomic comparison of single-copy genes conserved in angiosperms [44]. Additional database searches revealed that MED18 was a plant homolog of Mediator subunit 18 (Fig. S1, ref [3,5]). To gain further insight into the function of this gene in Arabidopsis, we examined the phenotypes of plants homozygous for T-DNA insertions in the MED18 coding sequence. We identified T-DNA insertion alleles (SAIL_889_C08, med18-1; SALK_ 027178, med18-2) from the SALK T-DNA insertion database (http://signal.salk.edu/cgi-bin/tdnaexpress) [45,46], and confirmed the location of the T-DNA inserts by PCR using MED18 and T-DNA specific primers (Fig. 1A). We also examined MED18 mRNA levels in plants homozygous for each of the alleles using RT-PCR. MED18 mRNA was undetectable in med18-1 plants compared to wild type. This result suggests that med18-1 is a null allele. Kim et al. [22] reported weak expression of MED18 mRNA in med18-2 plants, but we failed to detect any MED18 mRNA using primers located on either side of the T-DNA insertion (Fig. S2).
When either med18-1 or med18-2 was crossed with wildtype plants, the F1 showed a wildtype phenotype, which demonstrates that both med18-1 and med18-2 are recessive. A complementation test was also performed using the med18-1 and med18-2 alleles. The F1 plants from a cross of med18-1 and med18-2 plants showed a med18 phenotype (Table S1), which is described below. This result demonstrates that med18-1 and med18-2 are allelic.
In addition to the previously described phenotype [22], med18 mutations cause a syndrome of related phenotypes affecting flowering time, inflorescence structure, and flower morphology. Under long day conditions (16 hour light, 8 hour dark), both med18-1 and med18-2 mutants did not flower even after 35 days after germination (DAG) (Fig. 1C). In contrast, wildtype plants flowered approximately 14 DAG (12 leaves) (Fig. 1B). The med18-1 mutation also affects flower morphology. In wildtype flowers, the sepals fully enclose the developing flowers until shortly after the beginning of anthesis (Fig. 1D). The sepals of med18-1 flowers did not fully enclose the developing flowers such that the buds appeared prematurely open (Fig. 1E).
Most striking of all was that med18-1 mutants showed dramatically altered floral organ numbers as compared to wildtype. All floral organs were affected (Table S1, Fig. 1F-H), more than 40 and 80 percent of the flowers on med18-1 plants had abnormal sepals and petals number respectively, while .80% of the flowers had fewer than 6 stamens. Approximately 25% of med18-1 mutant flowers had more than two carpels (Table S1, Fig. 1I, J). In addition to the altered floral organ number, med18-1 mutants also showed delayed stamen development and later maturation of pollen (Fig. 1K, L), which led to reduce seed set.

MED18 Expression during Arabidopsis Development
The flowering time and floral organ patterning defects in the med18 mutants suggests that MED18 plays a role in regulating genes important for flowering and floral organ development. To determine if MED18 expression coincides with the phenotype, we examined the RNA expression pattern of MED18 in wildtype plants using in situ hybridization. MED18 transcripts could be detected in developing seeds ( Fig. 2A), the inflorescence meristem, the floral meristem, and floral organ primordia (Fig. 2 B and C). Transcript abundance appeared highest in the developing stamens and pistils (Fig. 2D). In later stages, MED18 expression was abundant in developing ovules and pollen (Fig. 2E) and in addition, MED18 mRNA was weakly expressed in petals, sepals, and the walls of carpels up to stage 12 (Fig. 2E). The strong expression of MED18 in the inflorescence meristem and floral organs is consistent with the floral defects observed in med18-1 mutants.

med18 Mutants Affect Flowering Time in Both Long and Short Days
In long day condition (16 h light, 8 h dark), the mutant plants did not flower until more than 40 leaves; and in short day (9 h light, 15 h dark), after 95 days (more than 60 leaves), med18 mutants still did not flower, and many rosette and cauline leaves showed senescence (Fig. 3A). Compared to wildtype plants (12 leaves under long days and about 40 leaves under short days), med18 is late flowering under both long and short days (Fig. 3A).

med18 Mutants Affect Flowering Time through upregulation of FLC Expression
To understand how MED18 regulates flowering time, we compared the expression levels of key genes of different pathways in mutant and wildtype seedlings using RT-PCR. FT and FLC show expression differences in the med18 mutants as compared to wildtype plants (Fig. S4). We performed QPCR and showed that FT mRNA was down regulated approximately 4.7 fold (Fig. 3B, p#0.004), while the FLC transcript level was much higher (9.5 fold) in med18-1 than in the wildtype plants (Fig. 3B, med18 Mutants are Responsive to Vernalization but not GA Treatment To examine the responsiveness of med18 to vernalization, the med18-1 plants were planted and kept at 4uC for 4 weeks in the dark and then transferred to normal growth temperature (23uC). The vernalization-treated med18 plants flowered much earlier than the untreated plants, after producing about14 leaves, which is comparable to that of wildtype plants (Fig. 3C. The untreated plants flowered after producing close to 40 leaves, which suggests that the flowering of the mutant plants was decreased by vernalization (Fig. 3C). To examine the effects of GA treatment, a GA solution of 20 mM was sprayed twice a week after germination until flowering. The results show that GA has no obvious effect on the flowering of med18 mutants as compared to wildtype plants. The GA-treated med18-1 plants initiated flowering after producing more than 35 leaves, which is not significantly different than untreated mutant plants (p = 0.115, Fig. 3D). Our results are consistent with previous results that show that vernalization promotes flowering by repressing FLC and releasing FT from repression [23,47]. The responsiveness of med18-1 to vernalization suggests that MED18 regulates flowering time through the vernalization pathway.

MED18 Affects Floral Organ Formation in all Four Whorls
Both mutation and overexpression of MED18 altered the number of floral organs (Fig. 1G-I, Table S1, Fig. S3). The increased number of petals and fewer than normal stamens seen in the med18 mutants was reminiscent of the floral phenotype of agamous (ag) mutants [48,49]. To further explore the floral developmental pathway in which MED18 is involved, we constructed double mutants with the well-studied floral developmental regulators, ag, pistillata (pi) and apetala2 (ap2). Flowers on the med18-1 ag-1 double mutant showed the striking reiteration of sepals and petals characteristic of ag mutants (Fig. 4A-C, Table  S2). The med18-1 pi-1 double mutant flowers had abnormal carpels, but fewer sepals than the pi-1 single mutant (Fig. 4D, E, Table S2). The flowers on the med18-1 ap2-5 double mutant displayed a much more complex phenotype. Carpels in both the first and fourth whorls were present as seen in the ap2-5 single mutant. In addition, double mutant flowers exhibited petaloid stamens and other petaloid structures in whorls 2 and 3 (Fig. 4F, G, Table S2).

MED18 Affects Floral Organ Formation through Regulation of Floral Homeotic Gene Expression
The clear epistasis observed in the med18-1 ag-1 double mutants suggested that MED18 and AG function in the same pathway to control floral development. Therefore, we examined AG mRNA levels as well as the levels of mRNA for other key floral regulators in med18 mutants using semi-quantitative RT-PCR. Our results showed that the mRNA expression levels of AG, AP1 and PI were down regulated in med18-1 mutants while AP2 and AP3 mRNA showed no obvious change (Fig. S5). QPCR results revealed AG mRNA was down regulated up to 4.8 fold (Fig. 5A, p#0.007), AP1 and PI mRNA expression levels were also reduced in the med18-1 mutant 1.9 (p#0.014) and 2.4 fold (p#0.011) respectively, while AP2 and AP3 transcript levels showed no significant differences between med18-1 and the wildtype (Fig. 5A, p = 0.068 and 0.082). The decreased AG expression is likely to cause a stamen to petal transition as well as abnormal carpel development [49], as observed in med18-1 and MED18 overexpression flowers ( Fig. 1F-H, Fig. S3). These results support the hypothesis that MED18 controls floral organ identity through its regulation of floral homeotic gene expression.

Altered AG Expression Patterns are Observed in med18 Mutants
To further examine the expression of AG in med18-1 mutants, in situ hybridization using an AG antisense RNA probe was performed on tissue sections from wildtype (Fig. 5B, D, F, H) and med18-1 plants (Fig. 5C, E, G, I). As described previously [50,51], AG is not expressed in the inflorescence meristem, nor in stage 1 or stage 2 floral meristems in wildtype plants. Strong AG expression is first found in the center of stage 3 and stage 4 wildtype flowers, but not in the emerging sepal primordia (Fig. 5B). During later stages of wildtype flower development, AG expression is present in stamens and carpels (Fig. 5D, F, H). In med18-1 mutants, a pattern of AG expression similar to that seen in wildtype was observed in both the inflorescence meristem and in stage 1 and stage 2 floral meristems (Fig. 5C). In flowers from stage 4 to stage 7, weak AG expression was detected in the center of the carpel primordia, but strong AG expression was observed in stamen primordia, although no expression was observed in petal and sepal primordia (Fig. 5C, E, G). In later development stages (stage 11, Fig. 5H, I), AG expression was observed in developing pollen similar to that observed in wildtype flowers (Fig. 5H), but it was difficult to detect any signal in vascular bundles of stamens and carpels (Fig. 5I). This result, together with the results of the QPCR analysis, strongly suggests that MED18 is required to maintain the normal AG expression level and pattern during early stamen and gynoecium development.

Discussion
Under many conditions Mediator appears to function as a general transcription factor [52]. Nonetheless, expression profiling of yeast Mediator subunit mutants has revealed the direct regulation of specific sets of genes by Mediator [53], and analysis of Mediator in Arabidopsis has shown that the Mediator subunits are important in regulating specific developmental processes like early embryo patterning [54,55], cell number and organ size [12,15], flowering time control [5,14,56] environmental regulation and defense gene regulation [16,[18][19][20][21]57].
MED18 was first characterized as a general transcription factor that promotes Pol II transcription through promotion of the transcription of miRNA, and knocking down MED18 expression caused abnormal cotyledon and silique development as well as a later flowering phenotype [22]. However, the mechanisms by which MED18 regulates flowering are poorly understood, and nothing is known about how MED18 regulates floral organ identity. In this study, we identified MED18 as a regulator of both flowering time and floral organ identity. Our findings show that MED18 controls flowering time by up-regulating FLC expression, which also affects the expression of the downstream gene, FT. After the flowering transition, MED18 plays a role in floral organ identity by regulating the AG expression level and pattern as well as AP1 expression levels (Fig. 5, 6). These results suggest that MED18 is important in the integration of key signaling pathways in plants by controlling target genes transcription (Fig. 6).
The med18 mutants display various developmental defects, suggesting that MED18 regulates multiple pathways. This is in agreement with the function of animal and yeast Mediator proteins, which have been suggested to regulate both basic and specific transcription [8,58,59]. We have focused on a study of MED18 function during flowering and floral organ development. We found that MED18 mRNA is localized mainly to the precursor cells of inflorescence meristem, the four floral whorls and strongly in the pollen and ovule primordia. This suggests a role for MED18 in the control of floral and reproductive organ initiation and development. Floral primordia arise from the inflorescence meristem, and floral organs are then formed in the floral meristem. The ABC model of flower development explains how three classes of genes control sepal, petal, stamen, and carpel identity [33]. Furthermore, the model indicates that class A and C genes are mutually antagonistic [60]. The previously isolated floral homeotic genes all seem to code for potential transcription factors. AP1, AP3, PI, AG, and the SEP proteins contain the MADS domain known to bind to DNA [36]. AP2 contains another DNA binding domain, the AP2 domain [61]. General regulators like Mediator are likely to regulate these genes. Our studies have shown that mutations in MED18 cause down regulation of several homeotic genes, such as AP1, PI and AG, but not all of them; this finding suggests that the effect of MED18 on homeotic gene expression is gene specific.
Because MED18 affects the expression of multiple genes, the flowers of med18 mutants show variable phenotypes, such as 4-14 petals, 2-6 stamens, 0-6 sepals and 1-3 carpels. In one of the flowers that had 14 petals, four stamens are clearly visible (Fig. S6). Therefore, the effect of the med18 mutation cannot be a simple homeotic transformation of the floral organs into petals. It is interesting to note that the med18 mutation causes a down  regulation of AG expression, yet some flowers have three carpels. According to the ABC model, down regulation of an A class gene (such as AP1) will cause ectopic carpel formation.
The later flowering phenotype observed in med18 mutants is caused by up-regulation of FLC mRNA expression. FLC also contains a MADS domain, and interestingly, both FLC and AG expression levels are altered in med18 mutants, but in opposite directions. There are similarities between the DNA structure of FLC and AG. Most of the genes in the Arabidopsis thaliana genome have short (,1 kb) introns [62], but intron 1 in FLC and intron 2 in AG are both greater than 3.0 kb in length, and both of these introns have cis-elements that are important for transcriptional control [27,63,64]. HUA1, HUA2, HUA ENHANCER2 [HEN2] and HEN4 were shown to positively regulate AG expression, either by inhibiting premature polyadenylation within AG intron 2 or by promoting the splicing of this intron, and hua1 hua2 double mutants have reduced levels of FLC mRNA [65]. All these results and our data that MED18 regulates FLC and AG mRNA expression levels, strongly support the idea of parallel regulation of FLC and AG [66].
The result of up-regulation of FLC and down-regulation of AG suggest that MED18 is not a general transcription repressor, but rather it plays different roles depending on the identity of its target, and/or the developmental stage. The mechanism by which MED18 affects FLC and AG expression is currently unknown. Highly specific gene regulation is thought to be determined by activators and combinatorial use of cofactors. In yeast, Med18 acts downstream of CDK8, and may act as a direct processor of signaling pathways for determining specific gene expression [53]. Med18 was also reported to be required for proper termination of transcription of a subset of genes during yeast budding [67]. In Arabidopsis, the CDK8 homolog is HUA ENHANCER3 [HEN3], which also controls organ identity and show similar loss-of-Cfunction phenotypes (Wang and Chen, 2004), suggesting that HEN3 may regulate organ identity through MED18 in Arabidopsis (Fig. 6). Our results suggest a conserved mechanism may exist in yeast and plants. Figure S1 Phylogenetic tree of eukaryotic MEDIATOR SUBUNIT 18 (MED18). The Bayesian inference analysis was derived from 406 amino acid positions of MED18 in different species of Eukaryotes. The best model amino acid replacement for MED18 sequences was JTT and gamma model for substitution rate heterogeneity between sites. Bayesian phylogenetic inference was performed with MrBayes Version 3.0 using four chains and 2,000,000 generations. Numbers at node indicate posterior probabilities; scale bar shows 0.2 amino acid substitutions per site. (TIF) Figure S2 RT-PCR result of MED18 showing that MED18 mRNA was not detected in med18-1, med18-2, and med18-1/med18-2 T1 plants using primers designed for the PCR product including both insertion sites. The Table S1 Number of different organ types in med18 flowers. a a 200 flowers were counted. b mean of 200 lowers. c Organ number is 6 or more. d Organ number is 4 or less. e Organ number is 1 or 1.5 (some carpels only have half or one third of normal size located at the tip of pistil ). f Organ number is between 2 and 3 (same as e). Mutant plants were grown in the greenhouse (16 h light, 2362uC), wildtype plants were grown under the same conditions. The floral organs on 200 wildtype plants were also counted, and all flowers showed 4 sepals, 4 petals, 6 stamens and 2 fused carpels, except 2 flowers showed 5 petals. The med18-1 allele is a strong allele and all four floral organs show significant differences from wildtype (**p,0.01), med18-2 is a weaker allele, and only petals and stamens show significant differences from wildtype. The F1 plants from a cross of med18-1 with med18-2 (med18-16med18-2) also show obvious floral organ number changes. (DOCX) Table S2 Number of different organ types in floral homeotic mutants and double mutants with med18-1 flowers. a a, at least 15 flowers were counted for each mutant. b, the first whorl of each organ showed the normal organ number for that whorl. c, many flowers showed 1 connected carpel-like sepal. (DOCX)


s2fieldsofstudy: Biology Medicine

text:
Hypothalamus-Related Resting Brain Network Underlying Short-Term Acupuncture Treatment in Primary Hypertension

The present study attempted to explore modulated hypothalamus-seeded resting brain network underlying the cardiovascular system in primary hypertensive patients after short-term acupuncture treatment. Thirty right-handed patients (14 male) were divided randomly into acupuncture and control groups. The acupuncture group received a continuous five-day acupuncture treatment and undertook three resting-state fMRI scans and 24-hour ambulatory blood pressure monitoring (ABPM) as well as SF-36 questionnaires before, after, and one month after acupuncture treatment. The control group undertook fMRI scans and 24-hour ABPM. For verum acupuncture, average blood pressure (BP) and heart rate (HR) decreased after treatment but showed no statistical differences. There were no significant differences in BP and HR between the acupuncture and control groups. Notably, SF-36 indicated that bodily pain (P = 0.005) decreased and vitality (P = 0.036) increased after acupuncture compared to the baseline. The hypothalamus-related brain network showed increased functional connectivity with the medulla, brainstem, cerebellum, limbic system, thalamus, and frontal lobes. In conclusion, short-term acupuncture did not decrease BP significantly but appeared to improve body pain and vitality. Acupuncture may regulate the cardiovascular system through a complicated brain network from the cortical level, the hypothalamus, and the brainstem.

Introduction
Hypertension is a common chronic disease affecting onethird of all adults worldwide and causing 51% of stroke deaths and 45% of coronary heart disease deaths in 2012 [1]. Investigating hypertension relief has therefore gained increasing attention and interest.
Acupuncture has emerged as a common alternative or complementary therapeutic intervention in western medicine. Reports have stated that acupuncture has certain curative effects for high BP and cardiac pain, with few side effects observed [2]. Despite its public acceptance, unequivocal scientific explanations regarding the mechanism underlying acupuncture in high BP treatment has not been attained and awaits further investigation.
In the recent years, the therapeutic effect of acupuncture in lowering BP has been investigated and discussed in many case reports and small-scale clinical trials [3][4][5][6][7], as well as several large-scale clinical trials on hypertension with acupuncture treatments [8][9][10]. However, these studies have only focused on the efficacy of acupuncture, and the mechanism of acupuncture in hypertension relief remains unknown.
In the past two decades, converging evidence from fMRI studies has demonstrated that acupuncture stimulation can modulate neural activities in a wide cortical-subcortical network, particularly the limbic system [11][12][13][14][15][16][17][18][19]. In practice, the well-identified physical effects of acupuncture and its purported clinical efficacy also suggest that it helps maintain a homeostatic balance of the internal state within and across multiple brain systems. Evidence from animal studies has demonstrated that acupuncture stimulation can facilitate the release of certain neuropeptides in the central nervous system (CNS), eliciting profound physiological effects and even activating self-healing mechanisms [20,21]. Electroacupuncture studies in rats revealed that both low-frequency and highfrequency stimulation induced analgesia, but differential effects existed in low-and high-frequency acupuncture on the types of endorphins released [20]. Peripheral acupuncture stimulation in deeper areas also activated various brain structures, such as the limbic, hypothalamic, and brainstem neural nuclei [21].
The present study attempted to investigate short-term acupuncture treatment in hypertension relief using resting state fMRI combined with a 24-hour ABPM. The aim was to (i) observe if short-term acupuncture decreased BP of hypertensive patients and improved quality of life and (ii) determine the possible mechanism of short-term acupuncture for treating hypertension in the hypothalamusrelated brain network.

Participants.
We recruited hypertensive participants from the Beijing Dongcheng District Tiantan community health service center and Fengtai District 201 community hospital. The eligibility criteria included (1) clinical diagnosis in line with WHO diagnostic criteria of essential hypertension: systolic blood pressure (SBP) ≥140 mmHg and diastolic blood pressure (DBP) ≥90 mmHg without antihypertensive drugs, two or more repeated measurements, normal BP now with the use of antihypertensive drugs but with a clear history of hypertension, and species and dose of antihypertensive medicine not changed or suspended or not taking antihypertensive drugs during the study period [8,9]; (2) 30-75 years old; (3) without cerebral infarction history and without cerebral infarction by MRI preliminary screening; (4) without nervous system disease, mental disease, diabetes, or other serious illness; (5) no drug addiction history; (6) no MRI contraindication and acupuncture contraindication; (7) no acupuncture experience over the past year; (8) right-handed by Edinburgh handedness questionnaire [19]; (9) provision of informed consent. The exclusion criteria included (1) could not receive acupuncture; (2) any unsuitable situation in the study. The drop-off criteria are as follows: (1) hypertensive crisis or other emergency; (2) could not adhere to acupuncture treatment. The present study was according to the principles of the Declaration of Helsinki (Version Edinburgh 2000).
Of the 71 participants recruited, only 30 (14 male, 35-74 years, mean age of 56.73 ± 9.29 years) met the eligibility criteria and finished the experiment ( Figure 1). All subjects were randomly divided into the acupuncture group and the control group, with 15 participants in each.

Acupuncture Interventions.
Participants underwent five acupuncture treatments over five consecutive days, with each session lasting about 30 minutes. During each treatment, seven acupoints used to treat hypertension in clinical settings were selected and needled bilaterally (except single points like GV20 (baihui), GV23 (shangxing), and four points EX-HN1 (sishencong)), including ST9 (renying), LI11 (quchi), PC6 (neiguan), LI4 (hegu), ST36 (zusanli), SP6 (sanyinjiao), and LR3 (taichong) [10,22] (Figures 2 and 3). After local skin disinfection, sterile acupuncture needles (0.2 mm in diameter, 40 mm long, Hua tuo acupuncture needles, Suzhou, China) were inserted into the skin to a depth of 15-50 mm according to different acupoints and were gently twisted in a mild reinforcing-reducing method 4-6 times till a deqi response was obtained. Control group participants did not receive acupuncture treatment and took medicine according to their original treatment programs. The acupuncture procedure was performed by the same experienced and licensed acupuncturist (15 years of experience) on all subjects. Figures 2 and 3 show needling acupoints for hypertension treatment. GV20 (baihui) is located in the center of the head; EX-HN1 (sishencong) includes four acupoints located around GV20, each about 3 cm to GV20; ST9 (renying) is located in the neck, beside the Adam's apple, in the midpoint of the leading edge of the sternocleidomastoid; LI11 (quchi) is located in the outer end of the elbow stripes; LI4 (hegu) is located in the back of the hand, between the first and second metacarpal, radial side of the midpoint of the second metacarpal; PC6 (neiguan) is located in the volar forearm, about 6 cm above the wrist stripes between tendon and radial measured wrist flexor tendon; ST36 (zusanli) is located in the anterolateral leg, between the tibialis anterior muscle and extensor digitorum longus, about 10 cm below the knee, about 1.5 cm from the tibia leading edge; SP6 (sanyinjiao) is located EX-HN1 GV20 Figure 3 in the medial leg, about 10 cm above the medial condyle tip; LR3 (taichong) is located in the dorsal foot and the first metatarsal gap rear depression.

Data Acquisition.
The participants were scanned in a 3.0 Tesla Siemens Trio MR whole body scanner. A foam pillow and a band across the forehead were used to fix the head. Resting state functional images were acquired with a singleshot gradient recalled echo planar imaging sequence. The sequence covered the whole brain, axial view, parallel to the AC-PC line, TR = 2000 ms, TE = 30 ms, measurement = 240, resolution = 64 × 64, field of view (FOV) = 240 mm × 240 mm, flip angle = 90 ∘ , slice thickness = 5 mm without gap, 32 slices, and scan time = 8,06 minutes. A set of T1weighted high-resolution structural images were collected using a 3D MPRAGE sequence for anatomical localization. TR = 1900 ms, TE = 2.39 ms, field of view (FOV) = 256 mm × 256 mm, flip angle = 7 ∘ , in-plane resolution = 1 mm × 1 mm, slice thickness: 1 mm without gap, 32 slices, and scan time = 8,26 minutes.

Experiment Workflow.
The experiment workflow is shown in Figure 3. The first MRI scan adopted the T2WI sequence to exclude participants with cerebral infarction. Participants who met the inclusion criteria underwent resting state fMRI and 3D T1WI structure sequences. The second and third MRI scans were only performed in the resting state fMRI sequence. The BP and heart rate (HR) of the participants were measured three times before and after every MRI scan, with the average taken for the three measurements. Participants sat quietly for 5 minutes before each measurement. We used a MOBIL GRAPH sphygmomanometer (Germany) to monitor 24-hour ambulatory blood pressure (ABMP). The BP and HR of the participants were also tested three times before and after every acupuncture treatment, with the average taken for the three recordings.
The acupuncture group received 24-hour ABMP and an MRI scan at multiple time points (before acupuncture, after acupuncture, and one month later). The control group only received 24-hour ABMP and one MRI scan. During the study period, participants maintained their original treatments, and drug type and dosage were not modified except for  At the end of the first acupuncture treatment, the participants completed a questionnaire using a 10-point visual analog scale (VAS) to rate the experience of deqi during intervention. The questionnaire included aching, pressure, heaviness, coolness, soreness, fullness, numbness, warmth, tingling, dull pain, and sharp pain. The VAS was defined as 0 = no sensation, 1-3 = mild, 4-6 = moderate, 7-8 = strong, 9 = severe, and 10 = unbeatable sensation.
Considering that BP and HR can be easily affected by psychological factors, such as affective states and anxiety, participants filled out questionnaires for assessments of anxiety (State Trait Anxiety Inventory (STAI)) [23] and affective state (BFS mood survey) [24]. To evaluate quality of life (QoL), subjects also completed questionnaires on MOS item short from health survey (SF-36) [25] before and after acupuncture treatment in the acupuncture group. The control group participants also completed the SF-36 (see Figure 4).

Statistical Analysis
2.5.1. Physiological Data. Since 24-hour ABMP is affected by movement and other factors, the data collected from each participate was not exactly the same. Therefore, we selected independent sample t-tests to compare the physiological data and paired t-tests to analyze the SF-36 survey. We used SPSS 13.0 for statistical analyses, with < 0.05 indicating statistical difference and < 0.01 indicating significant statistical difference.

Image Preprocessing.
All preprocessing steps were carried out using statistical parametric mapping (SPM5, http://www.fil.ion.ucl.ac.uk/spm/). Functional images were preprocessed using sinc interpolation for slice scan time correction, trilinear sinc interpolation for alignment (motion correction) of functional volumes, and high-pass temporal filtering to 1 Hz to remove slow drifts in the data. The image data were further processed with spatial normalization based on the MNI space and resampled at 2 mm × 2 mm × 2 mm. Poststimuli resting data were also filtered using a band pass filter (0.01∼0.08 Hz) to reduce low-frequency drift and high-frequency noise. Finally, the functional images were spatially smoothed with a 6 mm full width at half maximum (FWHM) Gaussian kernel. All resting state functional images were preprocessed using Statistical Parametric Mapping 5 (SPM5) and included motion correction, normalization, and smoothing.

Functional Connectivity Analysis.
For each subject, the "seeding" time courses of the hypothalamus were, respectively, cross-correlated with all low-pass filtered voxels to generate functional connectivity maps within each of the three conditions. This approach was termed withincondition interregional covariance analysis (WICA). The resulting correlation coefficient -maps were normalized and corrected to roughly standard normal distributions using methods previously described. The normality of the distribution was then tested using Kurtosis tests ( < 0.001). The three -maps of each individual were entered into one-sample t-tests, respectively, to determine whether group data was significantly different from zero. For visualization, all connectivity results were transformed into the Talairach stereotactic space and overlaid on MRIcro (http://www.mccauslandcenter.sc.edu/CRNL/) for presentation purposes. All resulting -maps were then cluster-filtered to remove correlations involving less than three contiguous voxels and then superimposed on high-resolution anatomical images using a < 0.001 cutoff threshold (uncorrected).
Evidence-Based Complementary and Alternative Medicine 5 The above image processing programs were coded in MAT-LAB7 (MathWorks, Inc.).

Physiological Data.
Average BP at different time points for the acupuncture and control groups are shown in Table 1. Two independent sample t-tests were used to compare the BP and HR between the two groups, = −1.529, = 0.127, with no statistical differences observed in physiological data, indicating comparable balance in the two groups.
Comparison between acupuncture treatment before and after showed that the average SBP, DBP, and HR tended to decrease after acupuncture, although no statistical differences were observed (Table 2). Comparison between the acupuncture and control group showed that average SBP, DBP, and HR demonstrated no statistical differences between the two groups (Table 3).

Connectivity Mapping.
At the baseline, the hypothalamus showed prominently spontaneous activations associated with limbic, cortical, and subcortical regions ( < 0.001), including the bilateral cerebellum, middle brain, bilateral insula, thalamus, and most of the frontal lobes. These results demonstrated a hypothalamus-anchored resting brain network under baseline conditions. Conversely, spontaneous deactivation was mainly located in the left cerebella vermis, as well as the left superior and right inferior frontal gyrus.
After acupuncture treatment, these spontaneous activation and deactivation networks anchored by the hypothalamus remained relatively stable ( Figure 5, < 0.001). In comparison with baseline conditions, however, we also identified significant changes (both in spatial distributions and response magnitudes) after acupuncture treatment.
There were prominently increased spontaneous activations in the bilateral cerebellum, brainstem, limbic system (bilateral insula, hippocampus, amygdala, and cingulate cortex), bilateral thalamus, and bilateral frontal lobes. Enhanced deactivations were located in the bilateral cerebellum, bilateral frontal lobes, and right parietal gyrus.
Results from after and before acupuncture showed a wide range activation of brain regions ( Figure 5). Notably, the most modulated changes were exhibited in the cerebellum, brainstem, insula, and frontal lobe. Increased positive correlations were primarily located in the cerebellum, limbic system (insula, parahippocampal gyrus, and cingulate cortex), bilateral thalamus, and frontal lobes. It is also worth noting that enhanced deactivation was only identified in the temporal lobe, left posterior cingulum, and right parietal lobe. Results from the acupuncture and control groups also showed significant differences in the cerebellum, amygdala, brain stem, and insula, and frontal lobes (see Figure 6).

Discussion
The present short-term acupuncture intervention study used common acupoints to clinically treat hypertension once a day over five consecutive days. After acupuncture, no statistical differences were found in SBP, DBP, and HR between the before acupuncture and control groups. However, average SBP, DBP, and HR did decrease after acupuncture treatment. The SF-36 survey from the acupuncture group showed statistical differences in body pain and vitality after acupuncture compared to the baseline. Our results did not indicate any differential benefit of short-term acupuncture for controlling BP and HR, but it may improve quality of life for patients in relation to body pain and vitality. This is the first study to investigate the hypothalamus-anchored brain network involving acupuncture treatments for hypertension.
Several large scale hypertension studies have investigated and discussed the therapeutic effects of acupuncture treatment. Flachskampf et al. [8] randomized 160 hypertensive participants in a single-blind 6-week trial using active or sham acupuncture and found significant ( < 0.001) differences in posttreatment BP between the two groups. Yin et al. [9] recruited 41 hypertensive or prehypertensive patients and designed a randomized, double-blind, placebocontrolled trial. All subjects were randomly assigned into real or sham acupuncture groups, and after 8 weeks of intervention, the mean BP of the acupunctured group was 6 Evidence-Based Complementary and Alternative Medicine      [10] showed there were no significant differences in BP decrease in the individualized traditional Chinese acupuncture group (IND), standardized acupuncture group (STD), and invasive sham acupuncture group (CNTL). Despite there being no statistically significant differences, mean decreases in BP after 10 weeks of treatment were observed between active (IND and STD) and sham acupuncture in comparison to the baseline in the SHARP study. The trials of Flachskampf et al. [8] and Yin et al. [9] showed a decrease in BP after acupuncture, but SHARP [10] only showed a downtrend without statistical differences. In previous studies, we found that the Flachskampf 's and Yin's studies were long term with intensive frequency of acupuncture treatment, while the SHARP study was long term with infrequent acupuncture treatment. Acupuncture in our study was short term and intensive, which resulted in no significant differences between acupuncture and control group. Considering that acupuncture has sustained and cumulative effects, short term use may lead to inconsistent results. We therefore speculated that the efficacy of frequent, long-term acupuncture would be better than long interval use or short-term treatment in hypertensive patients.
The cardiovascular center is mainly located in the medulla oblongata and hypothalamus, and these areas are not in the default brain network [26]. Many electrophysiological studies [27][28][29][30][31] have shown that the hypothalamus may be a key hub in the CNS in relation to cardiovascular regulation. Thus, we selected the hypothalamus as the seed point region to explore modulated brain network changes underlying acupuncture hypertension treatment. Our results suggested that increased functional connectivity existed between the hypothalamus and other regions after acupuncture. The functional connection with the hypothalamus was enhanced in some areas, mainly in the bilateral frontal lobe (vmPFC, dlPFC), limbic system (bilateral insula, amygdala, hippocampus, anterior cingulate cortex, and posterior cingulate cortex), bilateral cerebellum, and medulla oblongata. The central regulatory region of the cardiovascular system is very wide from brainstem to cortex, and includes the medulla oblongata, pons (locus coeruleus), midbrain (periaqueductal gray, substantia nigra), and limbic system (hypothalamus, the amygdaloid, subfornical organ, and insula), which together make up a complete network [27]. The medulla oblongata is the basic center, which includes the rostral ventrolateral medulla (vasoconstriction), ventrolateral part of the medulla oblongata (vasodilation), ambiguous nucleus and dorsal nucleus of the vagus nerve (restrains heart function), and tractus solitarius medulla oblongata (afferent nerve terminal) [28]. Past research has indicated that the medulla oblongata regulates the cardiovascular system through neurotransmitters such as glutamate, P substance and -aminobutyric acid [29][30][31].
Previous studies on electrophysiology have shown that the periaqueductal gray (PAG), hypothalamus, and other parts of the limbic system were important parts of the cardiovascular center [32][33][34][35][36][37][38][39][40] through different neurotransmitters, adjusting the sympathovagal balance to regulate the cardiovascular system. Many projection fibers are found between the ventrolateral medulla, PAG, and hypothalamus arcuate nucleus for their closely interconnected, and adjusted the cardiovascular system in common [32][33][34]. The limbic system also plays an important part in BP regulation in the CNS. Some parts are involved in step-up regulation, such as the paraventricular nucleus, lateral hypothalamus/perifornical area, dorsomedial nucleus, ventromedial nucleus, posterior hypothalamic nucleus, lateral septum ventrolateral part/habenular body, subfornical organ, and the central amygdala and insula, while the anteroventral third ventricle area (AV3V area) and the arcuate nucleus participate in decompression regulator, with interactions occurring between these regions [28,29,[35][36][37][38][39][40]. Although the role of the cerebellum in cardiovascular regulation is unclear, animal experiments suggest that the neuronal excitability of the fastigial nucleus has the effect of decreasing BP [41][42][43]. Electrical stimulation of the fastigial nucleus can increase arterial BP and HR, which relieve neuron injury through chronic cerebral ischemia, and was helpful for damaged arterial baroreflex function recovery [41][42][43]. Normal adults can also present transient changes in physiological indicators after fastigial nucleus electrical stimulation, which suggests a decrease in peripheral vascular flow and an increase in peripheral resistance and a slight increase in heart rate [44]. In addition, the vermiform process of the cerebellum may be important in cerebellum cycle control [45]. Idiaquez et al. [46] reported on vermis and right cerebellar hemisphere resection from cerebellar hemorrhage, where the patient appeared transient orthostatic hypotensive, suggesting that the cerebellum was involved in cardiovascular regulation. We found a negative correlation to the hypothalamus in the fastigial nucleus, which may play a part in the therapeutic action of acupuncture.
Beissner et al. [47] combined cardiac-gated brainstemsensitive fMRI to investigate the mechanism of acupuncture. They found that, from the cortical level, the fMRI group showed significant increases activation in dorsolateral prefrontal gyrus (dlPFC), anterior mid cingulated cortex (ACC), insula, and frontal lobe cortex. Deactivations were found in the ventromedial prefrontal and orbitofrontal cortices (vmPFC/pgACC). They supposed that these areas were involved in acupuncture-induced heart rate changes. Further, as these areas are typical cortical and brainstem centers of pain and autonomic processing, they hypothesized that acupuncture may be a low-intensity deep pain stimulus that can activate autonomic concomitants and exert nonanalgesic effects with therapeutic potential. Our results also indicated a positive correlation with the hypothalamus in vmPFC, ACC, insula, and dlPFC, and these areas may participate in cardiovascular regulation in acupuncture treatment of hypertension and relieve patient pain.
The present study was limited by the small sample size. During our research, patients were found to have different reactions to acupuncture, with some being sensitive to acupuncture and some not. A larger sample size would be necessary to draw more definitive conclusions. Secondly, clinical experience and comparison with large trials indicated that long-term, frequent acupuncture may have good efficacy. The present study indicated that short-term acupuncture 8 Evidence-Based Complementary and Alternative Medicine did not improve the BP of primary hypertensive patients. Finally, nonbrainstem sensitive fMRI sequences were used here. Because the main cardiovascular center is located in the brainstem, a more brainstem sensitive sequence is needed to investigate the mechanism of acupuncture for treating hypertension.

Conclusions
According to our results, short-term acupuncture did not decrease BP significantly but may have improved body pain and vitality. Acupuncture may also regulate the cardiovascular system through a complicated brain network from the cortical level, the hypothalamus, and the brainstem. However, the mechanism remains unclear and needs further investigation.


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Genome-Wide Association Study Identifies a Novel Locus Contributing to Type 2 Diabetes Susceptibility in Sikhs of Punjabi Origin From India

We performed a genome-wide association study (GWAS) and a multistage meta-analysis of type 2 diabetes (T2D) in Punjabi Sikhs from India. Our discovery GWAS in 1,616 individuals (842 case subjects) was followed by in silico replication of the top 513 independent single nucleotide polymorphisms (SNPs) (P < 10−3) in Punjabi Sikhs (n = 2,819; 801 case subjects). We further replicated 66 SNPs (P < 10−4) through genotyping in a Punjabi Sikh sample (n = 2,894; 1,711 case subjects). On combined meta-analysis in Sikh populations (n = 7,329; 3,354 case subjects), we identified a novel locus in association with T2D at 13q12 represented by a directly genotyped intronic SNP (rs9552911, P = 1.82 × 10−8) in the SGCG gene. Next, we undertook in silico replication (stage 2b) of the top 513 signals (P < 10−3) in 29,157 non-Sikh South Asians (10,971 case subjects) and de novo genotyping of up to 31 top signals (P < 10−4) in 10,817 South Asians (5,157 case subjects) (stage 3b). In combined South Asian meta-analysis, we observed six suggestive associations (P < 10−5 to < 10−7), including SNPs at HMG1L1/CTCFL, PLXNA4, SCAP, and chr5p11. Further evaluation of 31 top SNPs in 33,707 East Asians (16,746 case subjects) (stage 3c) and 47,117 Europeans (8,130 case subjects) (stage 3d), and joint meta-analysis of 128,127 individuals (44,358 case subjects) from 27 multiethnic studies, did not reveal any additional loci nor was there any evidence of replication for the new variant. Our findings provide new evidence on the presence of a population-specific signal in relation to T2D, which may provide additional insights into T2D pathogenesis.

and 40 and were ≤45 years when they first joined the study. On the basis of clinical history (independence from insulin for at least 6 months after diagnosis), biochemistry and immunological testing (absence of anti-GAD antibodies) and exclusion for mitochondrial diabetes (mt3243 A>G) [11], 879 were considered to have clinical T2D and available for inclusion in this study. The mean (SD) age at diagnosis for this group was 37.3 (5.2) years and the mean BMI was 24.9 (3.9) kgm -2 . Genotyping was performed using Applied Biosystems TaqMan® SNP genotyping assays on an Applied Biosystems 7900HT system.

East Asian Replication Studies Bio Bank Japan
The BioBank Japan Project aims to gather basic information for personalized medicine. GWAS data are available on 4,878 individuals with T2D and 3,345 controls using Illumina HumanHap610-Quad and 550K BeadChip, respectively. We then selected 7,541 subjects belonging to the Hondo cluster (4,470 cases and 3,071 controls) for an association study with T2D. We performed genome-wide imputation by using IMPUTE (https://mathgen.stats.ox.ac.uk/impute/impute.html) with data from 89 HapMap samples (44 JPT and 45 CHB in HapMap phase 2) [12]. T2D cases were selected from individuals registered as having T2D. Control groups were individuals registered as not having T2D but affected by other illnesses, or healthy volunteers.

RIKEN T2D study
We selected another 5,339 T2D cases from the BioBank Japan or from subjects with T2D who visited outpatient clinics at one of 4 different institutions: St. Marianna University School of Medicine, Kawasaki Medical School, Toyama University Hospital; or the Shiga University of Medical Science. We also examined 2,141 controls enrolled during an annual health check-up at Keio University, St. Marianna University School of Medicine; or Toyama University Hospital. Diabetes was diagnosed according to World Health Organization (WHO) criteria. We excluded individuals who were positive for antibody to glutamic acid decarboxylase (GAD) or those with diabetes due to (1) liver dysfunction, (2) steroids and other drugs that might raise glucose levels, (3) malignancy, or (4) a monogenic disorder known to cause diabetes [13].

AGEN Consortium Korea Association Resource Study (KARE)
The KARE study was initiated in 2007 to undertake a large scale GWA analysis for T2D and numerous complex quantitative traits amongst the 10,038 participants (aged between 40 and 69) of the Ansung (N=5,018) and Ansan (N=5,020) population-based cohorts. About 10,000 subjects from KARE study cohorts were genotyped with Affymetrix Genome-Wide Human SNP array 5.0. Two KARE study cohorts were established as part of the Korean Genome Epidemiology Study (KoGES) in 2001. The sampling base for both cohorts is in KyungGi-Do province, close to Seoul, the capital of the Republic of Korea. Both cohorts were designed to allow longitudinal prospective study and adopted the same investigational strategy. Participants have been examined every two years since baseline (2001). More than 260 traits have been extensively examined through epidemiological surveys, physical examinations, and laboratory tests applied to Ansung and Ansan cohort members. Among a total of 10,038 KARE study participants, 1,042 subjects were included as T2D cases according to the following criteria: (1) treatment of T2D, (2) fasting plasma glucose ≥7 mmol/L or plasma glucose 2-h after ingestion of 75g oral glucose load ≥11.1 mmol/L and (3) age of disease onset ≥40 years. About 10,000 (1,042 T2D cases and 8,958 controls) participants from KARE study cohorts were genotyped with Affymetrix Genome-Wide Human SNP array 5.0.

Singapore Diabetes Cohort Study (SDCS)/Singapore Prospective Study Program (SP2)
T2D cases were selected from The Singapore Diabetes Cohort Study (SDCS). SDCS is a research initiative to identify genetic and environmental risk factors for diabetic complications. Since 2004, all T2D patients seen at participating polyclinics and hospitals were invited to be part of the cohort. Questionnaire data as well as clinical data of consenting patients were obtained together with bio-specimens such as blood and urine archived at -80°C. For the purpose of this study, 2,202 Chinese subjects were available for genome-wide analysis [14]. T2D controls were selected from The Singapore Prospective Study Program (SP2). From 2003-2007, participants were invited to participate by linking their unique national identification numbers with national registries, where 7,742 attended the interview and of these 7,742 participants, 5,163 attended the clinical examination. A total of 5,499 Chinese, 1,405 Malays and 1,138 Asian-Indians were available at the time of the study and only the Chinese were used for this study. Detailed population selection and methodology have been previously reported [14,15]. Controls were individuals with no prior history of diabetes and had a fasting glucose level of not more than 6.0 mmol/L.

Singapore Malay Eye Study (SiMES)
The Singapore Malay Eye Study (SiMES) is a population-based, cross-sectional study of Malay adults, aged 40 -80 years living in Singapore (N=3,280). Of the 4,168 eligible participants invited, 3,280 participated in the study with a 78.7% response rate. Briefly, age-stratified random sampling of all Malay adults aged from 40-80 years residing in 15 residential districts in the southwestern part of Singapore was performed. Details of the study participants and methods have been published previously [14,16]. For the SiMES study, 3,072 samples from the population based study were genotyped on the Illumina HumanHap 610Quad. Diabetic cases were defined as having either a history of diabetes or had HbA1c level ≥6.5%. Controls had no history of diabetes and HbA1c level <6%. This yielded 794 Malay diabetic cases with 1,240 controls.

Cardiometabolic Genome Epidemiology (CAGE) Network
In the case-control panel of the CAGE Network, T2D cases (N=1,110) were enrolled according to the 1999 WHO criteria, while unaffected controls (N=1,014) were enrolled according to the following criteria: (1) no past history of urinary glucose or glucose intolerance; (2) HbA1c, <5.6% or a normal glucose (75g) tolerance test; and (3) age at examination, ≥55 years. In addition, population-based subjects (740 cases and 4,889 controls from the CAGE-Fukuoka Study) and the Biobank Japan (http://biobankjp.org/) cases (N=3,403, BBJ samples independent of those used for the stage-2 analysis) were included in this case-control study. In the CAGE-Fukuoka Study (12,569 participants in total), diabetes was defined as HbA1c ≥7.0% or under treatment of diabetes; the controls were chosen as nondiabetic subjects who met the following conditions: age, ≥55 years; HbA1c, ≤5.0%; no previous and/or current treatment for diabetes; and absence of renal failure (serum creatinine, <3.0 mg/dL), as previously described [17].

Shanghai Diabetes Genetic Study (SDGS)
The SDGS includes genome wide scan data of 1,019 diabetes case and 1,710 controls. Details of the study design have been described elsewhere [18]. Briefly, diabetes cases in the SDGS included 886 incident T2D cases identified in the Shanghai Women's Health Study (SWHS) [19], a population-based cohort study of 75,000 women, and 133 prevalent T2D cases identified from female controls of the Shanghai Breast Cancer Study (SBCS), a population-based case control study [20]. The 886 diabetes cases identified from the SWHS all met the following criteria: (1) age ≤65 with a self-reported diabetes diagnosed after study enrollment; (2) used diabetes medication; (3) had fasting glucose level >7 mmol/L at least twice, and (4) donated a blood sample. The 133 diabetes cases identified from the controls of SBCS were women who were diagnosed with T2D and were on diabetes medication or had a blood glucose level >7 mmol/L (measured by study). The 1,710 controls used in this GWAS were shared with a GWAS of breast cancer that was recently completed and was based primarily on the Shanghai Breast Cancer Study (SBCS). Excluded from the control group are women who (1) had a self-reported history of diabetes; or (2) had a blood glucose level between 5.5 and 7mmol/L and had HbA1c >6.1% or had no HbA1C data.

Taiwan T2D study (TDS)
A total of 2,798 unrelated individuals with T2D, age >20 years, were recruited from China Medical University Hospital (CMUH), Taichung, Taiwan; Chia-Yi Christian Hospital (CYCH), Chia-Yi, Taiwan; and National Taiwan University Hospital (NTUH), Taipei, Taiwan. All of the T2D cases were diagnosed according to medical records and fasting plasma glucose levels using American Diabetic Association criteria. Subjects with type 1 diabetes, gestational diabetes, and maturity-onset diabetes of the young (MODY) were excluded from this study. The controls were randomly selected from the Taiwan Han Chinese Cell and Genome Bank. The criteria for controls in the association study were (1) no past diagnostic history of T2D, (2) HbA1c ranging from 3.4% to 6%, and (3) BMI ≤32 kg/m 2 . The two control groups were comparable with respect to BMI, gender, age at study, and level of HbA1c. All of the participating T2D cases and controls were of Han Chinese origin, which is the origin of 98% of the Taiwan population [21].

Cebu Longitudinal Health and Nutrition Survey (CLHNS)
The CLHNS is a community-based birth cohort study that originally enrolled 3,327 pregnant women from the Metropolitan Cebu, Philippines area in 1983-4 (3,080 singleton live births), and has since followed them and their offspring. For this study of CLHNS mothers, glucose levels were collected in the year 2005, and the study sample consists of 159 T2D cases and 1624 controls. T2D was defined as overnight fasting plasma glucose level ≥7.0 mmol/L or current use of anti-diabetic medication. Controls were defined as overnight fasting plasma glucose level <7.0 mmol/L and treatment naive. Whole blood glucose levels were converted to plasma glucose level by subtracting 0.97 mmol/L [22].

European Replication Studies DIAbetes Genetics Replication And Meta-analysis (DIAGRAM)
The DIAGRAM consortium is focused on performing large-scale studies to characterize the genetic basis of T2D, with a principal focus on samples of European descent. An incremental meta-analysis (DIAGRAM)+ is comprised of a total of 8,130 cases and 38,987 controls available with GWAS genotyping. T2D case-control status was defined using study-specific criteria. SNP associations were tested using an additive genetic model, and combined across studies by inverse variance meta-analysis using a fixed effects model [23].

Diabetes Genetics in Pakistan study
We would like to thank all the diabetic patients and control subjects for agreeing to participate in this study. We are grateful to Dr. Asher Fawwad, Dr. Waheed Iqbal and Dr. Abdul Razzaq for recruiting the study subjects and coordinating data collection. We are particularly grateful to Dr. Muhammad Zafar Iqbal Hydrie for overseeing the DGP collection and for the assistance of Prof. A. Samad Shera in setting up collaborative links between the UK and Pakistan. Funding for the collection of DNA and clinical data was provided by Diabetes UK (project number 07/0003512).

RIKEN/ Bio Bank Japan
For RIKEN T2D study, we thank Dr. Minoru Iwata at First Department of Internal Medicine, University of Toyama for providing DNA samples and clinical information. We also thank the technical staff of the Laboratory for Endocrinology and Metabolism at the RIKEN Center for Genomic Medicine for providing technical assistance. Japanese studies were supported by a grant from Ministry of Education, Culture, Sports, Science, and Technology, Japan.