Open Access

Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis

  • Authors:
    • Bing-Gang Guan
    • Xiao-Xi Cai
  • View Affiliations

  • Published online on: February 27, 2019     https://doi.org/10.3892/etm.2019.7326
  • Pages: 2894-2900
  • Copyright: © Guan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Abnormal sub-pathways competitively regulated by long non-coding RNAs (lncRNAs) for postmenopausal osteoporosis (PO) based on integration of lncRNA-mRNA expression data and pathway network topologies were inve-stigated. Interesting lncRNA-mRNA pairs were selected by Pearson's correlation coefficient (PCC) algorithm on the basis of lncRNA-miRNA and miRNA-mRNA interactions and gene expression profiles. Then, lncRNAs in interesting pairs were embedded into pathway graphs as signature nodes by linking to their regulated-mRNAs, and lncRNA competitively regulated pathways (LCRPs) were gained for PO patients. Moreover, sub-pathways were detected dependent on the shortest distance similarity and the pathway topology. The abnormal sub-pathways were determined utilizing the Wallenius approximation methods through evaluating the statistical significance of sub-pathways. In total 75 interesting lncRNA-mRNA pairs (representing 17 lncRNAs and 74 mRNAs) were identified. Subsequently, 42 LCRPs were extracted from pathway graphs by signature lncRNA regulated mRNAs. Moreover, 14 abnormal sub-pathways with P<0.05 were obtained between PO patients and controls, such as sub-pathways of PI3K-Akt signaling pathway and long-term potentiation. This finding may facilitate understanding the molecular mechanism of PO, and point a new direction to identify potential biomarkers for treatment and prevention of the disease.

Introduction

Postmenopausal osteoporosis (PO), primary type of osteoporosis, is regarded as a systemic skeletal disease characterized by compromised bone strength predisposing to an increased risk of fracture (1). Clinically, major manifestations of PO patients are low bone mass density (BMD), a fracture or even vertebral compression fracture occurring during routine daily activities without a specific fall or injury (2). In consequence, PO imposes a great burden on life of both individuals and their families. Currently, usual treatments for postmenopausal women with osteoporosis mainly include vitamin D, estrogens, bisphosphonates, and calcitonin (3,4). Thus, an effective approach to overcome these limitations is an urgent need and a huge challenge for PO related research.

Simultaneously, previous studies have demonstrated that low BMD is the optimal predictor of fracture risk in asymptomatic PO (5,6). Hence, a comprehensive understanding and explanation for molecular mechanism underlying the progression of BMD reduction is critical for PO patients. Increasing proofs showed that long non-coding RNAs (lncRNAs) are able to competitively adjust messenger RNAs (mRNAs) expression levels via sharing common microRNA (miRNA) binding sites with mRNAs (7,8). In addition, identifying lncRNA competitively regulated pathways (LCRPs) give potential for uncovering molecular mechanism and exploring functions of lncRNAs in complicated human disease (8).

Pathway structure information helps to understand the physiology of diseases and the functions of the biological system (9). Moreover, Li et al suggested that key local sub-regions, rather than complete pathways, could subtly explain the etiology of diseases (10,11). This finding revealed that more attention should be focused on sub-pathways rather than entire pathways (12). Therefore, in this study, we aimed to identify abnormal sub-pathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profiles and pathway topologies for PO patients. The results might provide powerful evidence and give insights for uncovering the potential molecular mechanism underlying PO.

Materials and methods

Using lncRNA-mRNA expression data, the inference of abnormal sub-pathways between PO patients and normal samples consisted of four steps. The first step was to construct interesting lncRNA-mRNA pairs by Pearsons correlation coefficient (PCC) algorithm. Secondly, LCRPs were gained for PO patients. Thirdly, sub-pathways were detected dependent on the shortest distance similarity and the LCRP network topology. Ultimately, abnormal sub-pathways were determined by the Wallenius approximation methods via evaluating the statistical significance of sub-pathways.

Recruitment of lncRNA-mRNA expression data

The interactions between lncRNAs and mRNAs were built by media of miRNAs. Thus the first part was to download the known lncRNA-miRNA and miRNA-mRNA interactions from the experimentally validated small non-coding RNAs target Base (starBase) and miRecords, respectively. Here, the straBase (http://starbase.sysu.edu.cn/) offers a comprehensive exploration for lncRNA-miRNA interactions curated from published studies (13). While the miRecords (http://mirecords.umn.edu/miRecords) are considered as an integrated resource not only for experimental miRNA-target interactions but also for target prediction programs (14). Furthermore, the lncRNA-mRNA competitively regulated relationships were defined by meeting to the two conditions concurrently: i) hypergeometric test (15) of shared miRNAs satisfying the criterion P<0.05; and ii) Jaccard coefficient (16) of shared miRNAs ranking in top 20%. Consequently, 7,693 interactions (covering 835 lncRNAs and 1,749 mRNAs) were retained, termed with background lncRNA-mRNA interactions for PO patients.

Gene expression profiles with accessing number GSE56815 for PO patients were acquired from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo). The data were deposited on Affymetrix Human Genome U133A Array [HG-U133A], and comprised of 40 PO samples and 40 control samples. In order to optimize the data regardless of the terms of quality and quantity, standard pretreatments and normalizations (17,18) were conducted on GSE56815. Next, the preprocessed probes were converted into gene structures immediately. As a result, 12,437 genes were obtained from the dataset for PO patients. With an attempt to increase the correlations between background lncRNA-mRNA interactions and PO patients, 12,437 genes were mapped to these interactions, and only the intersected lncRNAs and mRNAs were reserved as lncRNA and mRNA data for PO in the subsequent analyses.

Construction of interesting lncRNA-mRNA pairs

Using the lncRNA and mRNA data, the co-expression probability for any pair of lncRNA-mRNA was assessed by the PCC algorithm. The PCC is a measure of the linear correlation between two variables, and its values (r) range from +1 to −1, where +1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation (19). Subsequently, the Fisher's Z-transformation was applied to evaluate the statistical significance for r, which converts r to the normally distributed variable Z (20,21). The Fisher's Z-transformation of r was computed by the following formula:

Z = 12ln1+r1-r

When Z goes from negative infinity to positive infinity, P distributes from 0 to 1, and any value of P will uniquely be matched with a value of Z and vice versa (21). If a pair of lncRNA-mRNA had a P<0.05, it would be regarded as interesting lncRNA-mRNA pair of PO patients for further exploitation.

Identification of LCRPs

The KEGG pathway enrichment analysis was carried out on gene lists in pretreated GSE54400 (DAVID, http://david.ncifcrf.gov). Particularly, Fisher's exact test was employed to determine the statistical significance of pathways (22). The P-values were corrected by the false discovery rate (FDR) in Benjamini & Hochberg method (23). Pathways with P<0.001 were considered to be significant between PO patients and control samples. These significant pathways were converted into undirected graphs and kept the original pathway structural information using R packages (10). Then, lncRNAs in the interesting lncRNA-mRNA pairs were embedded into pathway graphs as nodes through linking to their regulated-mRNAs. As a consequence, LCRP network for PO was gained, including lncRNA-mRNA competitively regulated edges and lncRNA nodes. In addition, these lncRNA nodes were named as signature nodes in LCRP network.

Location of sub-pathways

During this step, sub-pathways for PO were located according to the shortest distance similarity combined with LCRP network topology. We chose any two of signature nodes randomly and computed the shortest path between them. If the amount of molecules between each signature pair was less than n then they were integrated into one node. Simultaneously, if the node number in the molecule sets within pathway was more than k, they would be thought as sub-pathways competing and regulated via signature lncRNAs. Specifically, the factor n controlled the intensity of regulated pathways, whereas k parameter measured the size of this sub-pathway. In this study, we applied k = 8 and n = 1 as default parameters.

Investigation of abnormal sub-pathways

For purpose of evaluating whether the sub-pathways were competing and regulated by signature lncRNAs comparing random Wallenius approximation methods were implemented to test the statistical significance. Here, the Wallenius approximation methods is a generalization of the hypergeometric distribution where items are sampled with bias (24). Particularly, it was executed by BiasedUrn model in R package (25). In detail, a weight (W) was computed for every sub-pathway, which indicated the intensity of competing regulation via signature lncRNAs associated with the sub-pathway.

W = 1-log2(GLPG)

PG was the number of mRNAs in this sub-pathway, and GL stood for the number of mRNAs competitively regulated by signature lncRNAs within this sub-pathway. Subsequently, supposing that x was the number of interesting mRNAs in interesting lncRNA-mRNA pairs; N referred to the amount of mRNAs in the gene expression data; m1 was on behalf of the number of mRNAs associated with this sub-pathway; and m2 represented the number of interesting mRNAs annotated into this sub-pathway, a corrected P-value was produced for the candidate sub-pathway as the following formula:

P = F(x,m1,m2N,W)

Moreover, the cut-off for abnormal sub-pathways between PO and controls was P<0.01.

Results

Interesting lncRNA-mRNA pairs

In this study, 7,693 background lncRNA-mRNA interactions involved in 835 lncRNAs and 1,749 mRNAs were recruited based on the starBase and miRecords when setting the thresholdings as P<0.05 and top 20% Jaccard distribution. To make these interactions more correlated to PO, we mapped 12,437 genes in pre-processed GSE56815 to them, and only took the intersections. As a result, a total of 44 lncRNAs and 1,498 mRNAs which had intersections with all genes were gained to construct random lncRNA-mRNA pairs. An r value was assigned to each lncRNA-mRNA pair using the PCC method, and then a P-value was calculated according to the Fisher's Z-transformation test. The cut-off for interesting lncRNA-mRNA pairs between PO patients and control samples was P<0.05 in the present study. As displayed in Table I, in total 75 interesting lncRNA-mRNA pairs (representing 17 lncRNAs and 74 mRNAs) were identified. The most interesting pair between DLEU2 and SEC24A possessed the most significant P of 5.44E-06, and the next two pairs were CROCCP2-CUL5 (P=2.08E-04) and JRK-FGFR2 (P=2.37E-04).

Table I.

Interesting lncRNA-mRNA pairs with P<0.05.

Table I.

Interesting lncRNA-mRNA pairs with P<0.05.

Pairs Pairs


lncRNAmRNAP-valuelncRNAmRNAP-value
DLEU2SEC24A0.00000544CROCCP2PTEN0.0183
CROCCP2CUL50.000208TTTY15CACNG40.0188
JRKFGFR20.000237TTTY15HMGA20.0191
CROCCP2KAT2B0.000317YLPM1EIF4G30.0203
DLEU2ACSL40.000458PVT1CCND10.0209
DLEU2DNAJC30.000522DLEU2PPP1CC0.0210
DLEU2STX70.000679LINC00312SMAD40.0217
LINC00342SOCS10.0013DLEU2LSM10.0224
YLPM1ITPR10.0015JRKIGF1R0.0224
JRKRARA0.0016DLEU2UTP60.0226
LINC00312RBL20.0016SEMA3BCHEK10.0231
DLEU2AP3S10.0017DLEU2PRPF40A0.0239
UBXN8CDKN1B0.0018YLPM1EIF5B0.0251
DLEU2SPCS30.0020LINC00342MYCN0.0253
DLEU2KPNA10.0022MCF2L-AS1TERT0.0261
LINC00312RB10.0023DLEU2EIF2B10.0265
DLEU2PRKAA10.0027TTTY15GYS20.0272
DLEU2PAIP10.0031DLEU2PLA2G4A0.0274
TTTY15GEMIN70.0032SLC38A3PIK3R20.0278
MAP3K14CCND20.0032C11orf95E2F10.0293
DLEU2PPP3R10.0041YLPM1NOTCH20.0317
DLEU2PPP1R12A0.0051DLEU2GABARAPL20.0325
TTTY15AMT0.0083SEMA3BFGF20.0331
DLEU2SEC11A0.0084LINC00342BMPR20.0342
DLEU2PPP2R5C0.0087YLPM1MDN10.0343
UBXN8KRAS0.0095SEC22BESR10.0350
DLEU2CUL20.0097JRKRPS6KA10.0373
YLPM1ARHGDIA0.0115YLPM1ZYX0.0379
UBXN8NRAS0.0121JRKEIF4EBP10.0392
MCF2L-AS1CDH10.0123YLPM1HSPA50.0432
DLEU2PPP3CA0.0128CROCCP2NOTCH20.0435
DLEU2CSNK1G30.0136DLEU2ARHGEF60.0446
JRKGRIN2A0.0156DLEU2BMS10.0447
SEMA3BIFNG0.0157LINC00663ZNF7640.0464
UBXN8PMAIP10.0165TTTY15CACFD10.0476
CROCCP2SIRT10.0168TTTY15CCNB20.0481
LINC00663ITPKA0.0176TTTY15ATG4B0.0499
SPON1SMO0.0181

[i] lncRNA, long non-coding RNA; mRNA, messenger RNA.

LRCPs

As described above, the KEGG pathway enrichment analysis was carried out on 12,437 genes in the gene expression dataset. A total of 42 significant pathways with P<0.001 were identified for PO patients (Table II). We found that PI3K-Akt signaling pathway (P=2.18E-09), cell cycle (P=2.07E-08), and p53 signaling pathway (P=2.91E-07) were the three most significant pathways. In addition, each significant pathway was assigned to an ID, and the pathway stood by the end of four of the ID in the subsequent analysis. Next, 42 KEGG pathways were converted into undirected graphs that retained their original pathway structural information, and then the 17 lncRNAs were embedded into the graphs as signature nodes by linking to their regulated-mRNAs. Consequently, the total 42 significant pathways could be connected to lncRNAs by the regulation of mRNAs, and thus 42 LCRPs were explored for PO. In detail, 16 lncRNA-mRNA interactions were enriched in the cell cycle. Besides, 11 lncRNAs regulated PI3K-Akt signaling pathway through 15 mRNAs since one lncRNA could regulate multiple mRNAs synchronously.

Table II.

LCRPs with P<0.001.

Table II.

LCRPs with P<0.001.

Pathway IDPathwayP-valueNumber of matched lncRNA-mRNA pairs
hsa04151PI3K-Akt signaling pathway2.18E-0915
hsa04110Cell cycle2.07E-0816
hsa04722Neurotrophin signaling pathway2.91E-0713
hsa04010MAPK signaling pathway3.35E-0711
hsa04720Long-term potentiation3.70E-07  9
hsa04810Regulation of actin cytoskeleton7.25E-07  9
hsa05218Melanoma2.00E-0610
hsa05219Bladder cancer2.18E-06  5
hsa05200Pathways in cancer2.24E-06  9
hsa05215Prostate cancer4.69E-0610
hsa05212Pancreatic cancer4.70E-06  7
hsa05203Viral carcinogenesis7.68E-06  6
hsa05213Endometrial cancer1.44E-05  6
hsa05161Hepatitis B1.78E-05  7
hsa04114Oocyte meiosis1.98E-05  9
hsa05223Non-small cell lung cancer2.22E-0510
hsa05221Acute myeloid leukemia2.46E-05  6
hsa04370VEGF signaling pathway3.64E-05  6
hsa05214Glioma6.77E-05  9
hsa04910Insulin signaling pathway9.25E-05  9
hsa05216Thyroid cancer2.10E-04  4
hsa05222Small cell lung cancer2.51E-04  5
hsa04150mTOR signaling pathway3.72E-04  9
hsa04730Long-term depression3.72E-04  6
hsa04066HIF-1 signaling pathway6.64E-04  6
hsa05031Amphetamine addiction6.65E-04  5
hsa04140Regulation of autophagy6.74E-04  4
hsa05166HTLV–I infection6.99E-04  9
hsa04662B cell receptor signaling pathway8.64E-04  5
hsa04660T cell receptor signaling pathway1.28E-03  6
hsa04510Focal adhesion1.30E-03  6
hsa03060Protein export1.65E-03  3
hsa05220Chronic myeloid leukemia1.65E-03  7
hsa04012ErbB signaling pathway2.02E-03  3
hsa04914 Progesterone-mediated oocyte maturation2.13E-03  5
hsa04650Natural killer cell mediated cytotoxicity2.89E-03  6
hsa05210Colorectal cancer3.82E-03  4
hsa05211Renal cell carcinoma4.78E-03  3
hsa04664Fc epsilon RI signaling pathway5.90E-03  4
hsa04724Glutamatergic synapse6.96E-03  5
hsa04115p53 signaling pathway8.01E-03  5
hsa03013RNA transport8.48E-03  6

[i] lncRNA, long non-coding RNA; mRNA, messenger RNA; LCRPs, lncRNA competitively regulated pathways.

Abnormal sub-pathways

After identification of LCRPs for PO, their sub-pathways were explored through the integration of the shortest path similarity and LCRP network topology. The ID for sub-pathways were in accordance with the corresponding LCRPs, for example, the first sub-pathway for PI3K-Akt signaling pathway (ID: 4151) was described as 4151_1 and the second one was 4151-2. In fact, these sub-pathways mostly belonged to 14 of 42 LCRPs. Immediately, the Wallenius approximation methods was used to evaluate the statistical significance of sub-pathways and executed dependent on R package BiasedUrn. Consequently, a total of 14 abnormal sub-pathways were determined under the condition of P<0.01, as shown in Table III.

Table III.

Abnormal sub-pathways with P<0.01.

Table III.

Abnormal sub-pathways with P<0.01.

Sub-IDPathwayWeightP-value
4151_2PI3K-Akt signaling pathway1.4854<0.001
4720_1Long-term potentiation1.6374<0.001
5200_2Pathways in cancer1.5475<0.001
5215_1Prostate cancer1.6374<0.001
4810_1Regulation of actin cytoskeleton1.51461.11E-16
4110_2Cell cycle1.65212.22E-16
4010_1MAPK signaling pathway1.22242.22E-15
5223_1Non-small cell lung cancer1.41508.88E-15
5218_2Melanoma1.73706.59E-14
5166_2HTLV–I infection1.00001.26E-13
4114_2Oocyte meiosis1.48542.63E-12
5214_1Glioma1.48542.63E-12
4150_1mTOR signaling pathway1.67817.00E-12
4722_1Neurotrophin signaling pathway1.67817.09E-12

In order to illustrate the correlations more directly and clearly, a sub-pathway network for signature nodes and abnormal sub-pathways was constructed and visualized by Cytoscope (Fig. 1). As one LCRP might have several sub-pathways, we selected the ID for corresponding LCRPs to represent its multiple sub-pathways in the sub-pathway network conveniently. The network comprised of 66 nodes and 129 edges, of which 14 were LCRPs, 14 signature lncRNAs and 38 mRNAs. Then, we calculated the topology centrality of this network using the degree index. Interestingly, the degree for YLPM1 and PI3K-Akt signaling pathway (ID: 4151) in both was 58, and followed Neurotrophin signaling pathway (ID: 4722, degree = 48) and TTTY15 (degree = 47). These nodes with high degree might play a more important role in the progression of PO patients, which also were suitable for sub-pathways.

Taking the top three ranked abnormal sub-pathways as examples, we extracted their sub-networks for the sub-pathway network, as shown in Fig. 2. The first one, abnormal sub-pathway for PI3K-Akt signaling pathway (ID: 4151_2), consisted of 21 nodes (7 signature lncRNAs, 10 interesting mRNAs and 4 genes) and 23 edges, such as TTTY15, DLEU2 and UBXN8. There were 8 interesting mRNAs, 4 signature lncRNAs, 4 genes and 23 edges in the sub-network for long-term potentiation (ID: 4720_1). The signature lncRNA DLEU2 connected more modes than the others, and thus, it was critical for this sub-pathway. As for the sub-network for pathways in cancer (ID: 5200_2), a total of 27 nodes and 37 edges were mapped on it, of which 17 interesting mRNAs, 8 signature lncRNAs and 2 genes, especially for UBXN8.

Discussion

Although numerous studies have concentrated on the relevance of lncRNAs to tumorigenesis as well as other diseases, the exact functions and activities of lncRNAs in PO progression remains unclear. Accurate molecular mechanisms underlying PO are controversial. For instance, Stuss et al considered that hormonal changes, which occurred throughout perimenopause and the immediate postmenopausal years, led to the accelerated BMD loss by stimulating the receptor activator of nuclear factor-κβ (RANK) and its ligand (RANKL) production (26). To shed new light on the dispute of PO, in this study, we revealed the pathological mechanism of PO from the view point of lncRNAs.

Since the RNA competitive interaction can influence the various critical functions of disease, investigating LCRPs not only can gain insight into the potential mechanism but also help investigate the lncRNAs functional role in disease (12). Thus, we identified abnormal lncRNAs between PO and controls by the integration of lncRNA-mRNA expression profiles and pathway network topology. As lncRNAs and mRNAs were not worked on directly, we built their relations through the shared miRNAs from lncRNA-miRNA and miRNA-mRNA interactions, and obtained interesting lncRNA-mRNA pairs for PO. To enhance the feasibility and confidence of our study, we chose the significant KEGG pathways as graphs to link lncRNA to pathways, and consequently obtained LCRPs. It was revealed that signature lncRNAs combined with topology of LCRPs could efficiently give light on positioning of lncRNA-regulated sub-regions (12). Under this condition, abnormal sub-pathways between PO patients and controls were detected by the Wallenius approximation methods. A total of 14 abnormal sub-pathways were investigated, such as sub-pathways of PI3K-Akt signaling pathway and long-term potentiation. Besides, we uncovered several critical lncRNAs that regulated the important abnormal sub-pathways, for example, TTTY15, DLEU2 and UBXN8. The mRNAs in the sub-network of sub-pathway might be biomarkers for treatment and prevention of PO to some extent. On the basis of these results, the potential molecular mechanism of PO should be clarified.

Taking PI3K-Akt signaling pathway for an example, it is activated via multiple types of toxic insults or cellular stimuli and adjusts the fundamental functions of cells (i.e. proliferation, transcription, growth, translation, and survival) (27). Generally, the PI3K and AKT combined with mTOR pathways are deregulated in human cancer leading to genetic alterations in their components or upstream activation of cell-surface receptors (28). In this study, we also found that mTOR signaling pathway was a significant pathway for PO patients, which confirmed a previous study. In addition, Sabine et al revealed that deregulation of PI3K/AKT pathway genes contributed to the endocrine resistance of breast cancer (29), which indicated that this signaling pathway correlated with the endocrine changes in women. Thus, we might infer that the abnormal regulations of certain parts in PI3K-Akt signaling pathway caused the BMD loss in PO patients by affecting their endocrine changes.

In conclusion, we have successfully identified abnormal sub-pathways for PO patients compared with normal controls by integrating lncRNA related analysis and pathway topologies. The findings might shed new light on revealing potential molecular mechanism underlying PO, and provide several signature lncRNAs with significant functions in the progression of PO. Further studies should focus on validation of these signature lncRNAs.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

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

Authors' contributions

BGG conceived the study and drafted the manuscript. XXC acquired the data. BGG and XXC analyzed the data and revised the study. Both authors read and approved the final study.

Ethics approval and consent to participate

Not applicable.

Patients consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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April-2019
Volume 17 Issue 4

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Spandidos Publications style
Guan B and Guan B: Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis. Exp Ther Med 17: 2894-2900, 2019
APA
Guan, B., & Guan, B. (2019). Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis. Experimental and Therapeutic Medicine, 17, 2894-2900. https://doi.org/10.3892/etm.2019.7326
MLA
Guan, B., Cai, X."Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis". Experimental and Therapeutic Medicine 17.4 (2019): 2894-2900.
Chicago
Guan, B., Cai, X."Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis". Experimental and Therapeutic Medicine 17, no. 4 (2019): 2894-2900. https://doi.org/10.3892/etm.2019.7326