Comprehensive LncRNA and Potential Molecular Mechanism Analysis in Noninfectious Uveitis

Abstract

Purpose: Long noncoding RNA (lncRNA) is noncoding RNA and have played a key role or be treated as a biomarker in a variety of diseases such as tumors. However, extensive lncRNA analysis for uveitis has not been explored completely. In this study, we analyzed the lncRNAs with altered expression in peripheral blood comprehensively for three major autoimmune diseases (ankylosing spondylitis [AS], Behҫet's disease [BD], and sarcoidosis) to search potential hub gene and molecular mechanism for noninfectious uveitis.

Methods: In total, we included 18 patients with AS and 12 patients with sarcoidosis versus 25 controls for GSE18781; we also included 15 patients with BD versus 14 controls for GSE17114 in this study. The lncRNA and messenger RNA (mRNA) expression levels were determined by microarray using serum samples from patients and healthy controls.

Results: Twenty-one lncRNAs and 1073 mRNAs were detected in patients with AS, 4 lncRNAs and 62 mRNAs in patients with BD, and 196 lncRNAs and 5376 mRNAs in patients with sarcoidosis. Thus, we suspected lncRNA XIST and MIAT, mRNA FCGBP, CD247, CTSW, AES, NCR3, TIGIT, CASP5, DUSP2, and TBX21 may be the most possible hub genes for AS, BD, and sarcoidosis. These RNAs were involved in the mitogen-activated protein kinase signaling pathway and inflammatory cytokine pathways.

Conclusions: In this study, comprehensive bioinformatics analysis identified lncRNAs with altered expression in three major autoimmune diseases that may combine with noninfectious uveitis. This study provides novel insights into the molecular pathogenetic mechanisms and key information toward developing new diagnostic biomarkers and special therapeutic targets for noninfectious uveitis in AS, BD, and sarcoidosis.

Translational Relevance: LncRNAs and their potential mechanisms provide new strategies for prevention and treatment for noninfectious uveitis in patients with AS, BD, and sarcoidosis.

Introduction

Uveitis is a vision-threatening ocular inflammatory diseases that predominantly affects young and middle-aged subjects.¹ The prevalence of uveitis affecting only 120/100,000 people, although it accounts for 10% to 15% of blindness.² The pathogenesis of uveitis often accompanied by bacterial, viral, parasite, or spirochete (especially Treponema pallidum) infections (infectious uveitis) or a series of autoimmune diseases such as ankylosing spondylitis (AS), Behçet’s disease (BD), sarcoidosis, Vogt–Koyanagi–Harada syndrome, juvenile arthritis, rheumatoid arthritis, and so on (noninfectious uveitis).³ It is generally believed that noninfectious uveitis is an immune-mediated ocular inflammation frequently accompanied by systemic autoimmune diseases, characterized by intraocular inflammation in the absence of infection.⁴ In brief, understanding the pathogenesis is the key point to find effective therapy of noninfectious uveitis. 

LncRNA, a group of noncoding potential transcripts longer than 200 nucleotides, does not encode proteins and has been considered as transcriptional noise over the past few decades.⁵ Currently, more and more studies have found that lncRNAs act as functional RNAs in biological functions and pathogenic roles of series cell biological and physiological processes, such as cancer development, pathogenic infection,⁶ and immune response. Cells infected with certain pathogen trigger the innate immune response by synthesizing inflammatory mediators and cytokines through transcriptional or post-transcriptional gene regulations. LncRNAs regulate the expression of inflammatory mediators or cytokines by working with RNA binding proteins in a variety of ways. LncRNAs and RNA binding protein complexes bind to the promoters of inflammatory mediators or cytokine genes to regulate transcription or to modify the chromatin,⁷ such as lncRNA-CD244, lincRNA-Cox2, THRIL, TH2-LCR, and lnc-DC, which have been shown to be expressed in T cells, macrophages, and dendritic cells. These lncRNAs regulate the transcription of immune response genes that then produce related cytokines, such as interferon-γ, tumor necrosis factor (TNF)-α, IL-13, IL-5, IL-4, and IL-12.⁸ The pathogenesis of autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis, were associated with lncRNAs. Lnc-USP50-2, lnc-ZNF354A-1, lnc-FRG2C-3, and lnc-LIN54-1 also have been shown to be involved in the progression of AS.⁹ Furthermore, lncRNAs have also been found to be implicated with ocular disorders, such as ocular tumors, glaucoma, diabetic retinopathy, proliferative vitreoretinopathy, and corneal vascularization.¹⁰ However, the regulation of inflammatory mediators or cytokines, or autoimmune processes in uveitis, especially noninfectious uveitis by lncRNAs, remains poorly understood. 

In previous studies, multiple lncRNA-related studies have shown significant associations between lncRNAs and different kind of eye ocular pathologies, especially for inflammatory damage, like uveitis. In 2020, a systematic analyses on the lncRNAs in retinal epithelial cells have validate the specific role of lncRNAs and inflammatory eye diseases.¹¹ Oxidative stress has been validated as a key pathological process to which lncRNAs contribute and is associated with specific neurovascular diseases, including uveitis.^12–14 In 2018, a systematic long noncoding RNA (lncRNA) analyses on a group of complex diseases like AS, Vogt–Koyanagi–Harada disease, and BD have shown potential relationships between lncRNA regulation and BD,¹⁵ implying that lncRNAs may play a specific role during the pathogenesis. Apart from that, a specific lncRNA associated with herpes simplex virus type-1 has also been shown to be associated with uveitis caused by alpha herpesviruses.¹⁶ LncRNA H19 has also been reported to be associated with age-related cataract caused by uveitis via the miR-29a/TDG axis, reflecting the complex regulatory effects of lncRNAs on the pathogenesis of uveitis.¹⁷ 

Apart from lncRNAs, we also analyzed competing endogenous RNA (ceRNA) during the pathogenesis of uveitis. CeRNAs participated in the regulation of post-transcriptomics biological processes by competing for shared microRNAs.¹⁸ As a newly recognized group of post-transcriptomics regulators, potential pathogenic contributions of ceRNAs have been widely reported.^19–21 As for uveitis, a functional single nucleotide polymorphism—rs7130280 in ceRNA NONHSAT159216.1—been reported to be associated with uveitis, reflecting the potential pathogenic role of such transcriptomics factor.²² 

In this study, we investigate the role of lncRNAs and their potential mechanisms in AS, BD, and sarcoidosis by bioinformatics analysis using a series of high throughput sequencing datasets (GSE18781 and GSE17114) and target gene predicted databases or software. In all, we identified 2149 differentially expressed genes (DEGs) (1930 messenger RNA [mRNA]s and 219 lncRNAs) in three diseases as discussed elsewhere in this article. Then, the gene interaction network was established based on those lncRNAs and ceRNA subnetworks may be involved in the pathogenesis of noninfectious uveitis. A series of newly submitted gene expression profiling including (GSE198533) provided us larger datasets for diseases associated lncRNAs exploration. In future study, we will include more datasets to perform integrative analysis for disease-associated lncRNA exploration. Overall, this study helps us to better understand the effects of lncRNAs involved in the pathogenesis of noninfectious uveitis to develop new strategies for prevention and treatment. 

Methods

Data Acquisition and Identification of Peripheral Blood of Patients With AS, BD, and Sarcoidosis

All expression datasets were obtained from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). The following search terms were applied: (Ankylosing Spondylitis OR AS OR Behcet Disease OR BD OR Sarcoidosis) AND (lncRNA OR long non-coding RNA OR mRNA OR Gene). The retrieval results were in accordance with the following inclusion criteria: (1) studies involving two groups (patients vs healthy people), (2) the studies were conducted in humans (homo sapiens), and (3) the sample size was 10 or more. Studies not satisfying the criteria as described were considered ineligible and therefore excluded. After a systematic search, GSE18781 and GSE17114 were finally included and downloaded. 

Analysis and Identification of DEGs in AS, BD, and Sarcoidosis

Gene expression data of GSE18781 and GSE17114 were analyzed using GEO2R. GEO2R compares the original submitted–supplied processed data table. Normalized data from GSE18781 and GSE17114 dataset were integrated with separated batch effects. Differential expression analysis was performed using limma (Linear Models for Microarray Analysis) R package as one of the most widely used statistical tests for identifying DEGs. The P values were adjusted using the false discovery rate. Comparisons for differential expression analysis were performed between each two of AS, BD, and sarcoidosis. All available transcripts in both datasets were included. Transcripts with significant cut-off value setting at |log2 FC|>1, an adjusted P value of less than 0.05 using the unpaired t-test. A Log2FC of greater than 1 was considered an up-regulated and a Log2FC of less than −1 was considered a down-regulated gene. In this study, limma R packages (Bioconductor project, http://www.bioconductor.org/) and GEO2R were used to detect DEGs from different groups in AS, BD, and sarcoidosis with age, gender, and batch (set) adjusted. The P values were used for multiple testing and gene with the smallest P values were considered to be the most reliable of this disease. 

Integration of LncRNA–MicroRNA (miRNA)–mRNA Interaction Network and Modular Analysis

MiRDB, miRTarBase, and TargetScan databases were used to calculate and evaluate the lncRNA–miRNA–mRNA interaction information to expose the potential correlation among DEGs in different groups. The selected genes for integration network should be mentioned in all three databases. If not, genes were eliminated. Following these target databases analysis, we further used the Cytoscape software (V3.8.2, integrating biomolecular interaction networks with high-throughput expression data and other molecular states) to identify the interaction of hub genes. The related lncRNA in the central nodes were thought of as the core genes that have important biological regulatory functions. 

Gene Ontology (GO) Function and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis

GO analysis is widely used for gene functional classification and gene annotation including biological process, cellular component, and molecular function. KEGG is a database resource that integrates genomic, chemical, and systemic functional information. GO functional enrichment and KEGG pathway analyses of DEGs were investigated through clusterProfiler R packages (Bioconductor project, http://www.bioconductor.org/). The highest connectivity among the hub genes and pathways were selected for subsequent analysis. 

Software and Versions

Active-perl (version 5.28) and Rstudio (x64, version 1.4.1106) software were used for statistical calculations and graphs. 

Results

In this study, we performed peripheral blood DEGs analysis between patients with AS, BD, and sarcoidosis and healthy controls and confirmed the functions and pathways of these DEGs. Finally, we identified lncRNA MIAT and XIST and mRNA FCGBP, CD247, CTSW, AES, NCR3, TIGIT, CASP5, DUSP2, and TBX21 as prognostic hub genes in patients with AS, BD, and sarcoidosis. Further study will investigate the relationship between autoimmune response in noninfectious uveitis and these genes. 

Target Samples and Microarray Information

The gene expression of AS and sarcoidosis was obtained from profile GSE18781: Gene Expression in Inflammatory Diseases. The microarray data from GSE18781 was based on the GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). Fifty-five homo sapiens peripheral blood samples were obtained from 18 patients with AS, 12 patients with sarcoidosis, and 25 controls (submission date: October 28, 2009; last update date: March 25, 2019). The gene expression of BD was obtained from profile GSE17114: Blood genomic expression profile for Behçet's disease. The microarray data from GSE18781 was based on the GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). Twenty-nine homo sapiens peripheral blood samples were obtained from 15 patients with BD and 14 controls (submission date: July 15, 2009; last update date: March 25, 2019). 

Identification of DEGs

A total of DEGs were identified for patients with AS (1673 mRNA, 21 lncRNA), patients with BD (62 mRNA, 4l ncRNA), and patients with sarcoidosis (5376 mRNA, 196 lncRNA) based on GSE18781 and GSE17114, with |log2 FC| >1 and a P value of less than 0.05. By intersecting the DEGs described elsewhere in this article, we found that nine mRNA and two lncRNA were common to the three diseases. These genes will be identified for further investigation. A volcano plot distribution map and cluster heatmap of these DEGs are shown in Figure 1. Figure 2 shows a Venn diagram to indicate the genes in common. 

Figure 1.

 

Volcano plot distribution and heatmap of the DEGs. (A) Volcano plot of DEGs for AS (A1), BD (A2), and sarcoidosis (A3). The red points indicate upregulated DEGs, the green points indicate downregulated DEGs, and the black points indicate DEGs with no significant difference in expression. (B) Heatmap clustering of DEGs. (C) mRNA and lncRNA DEG heatmaps of AS (C1, C4), BD (C2, C5), and sarcoidosis (C3, C6). From red to black to green, or medium blue to white to dark orange, the expression level of the mRNA or lncRNA in the samples gradually decreases.

Figure 2.

 

Venn diagrams showing the numbers of mRNAs (A) and lncRNAs (B) in each disease and overlapping part. Each color represents one disease.

CeRNA Network Analysis and Construction

All DEGs were imported and matched three databases described above to construct the ceRNA network using active-perl software. Cytoscape (version 3.8.2) was applied for visualization of the network, and plug-in Affinity Purification was used for hub gene network construction. Network for three diseases were presented in Figure 3 in sequence. 

Figure 3.

 

CeRNA network of DEGs in different diseases showed by cytoscape. Red diamonds indicate lncRNAs, green triangles indicate miRNAs, and blue circles indicate mRNAs. Gray edges indicate the lncRNA–miRNA–mRNA interactions. (A) AS patient versus healthy control. (B) BD patient versus healthy control. (C) Sarcoidosis patient versus healthy control.

GO Functional and KEGG Pathway Analysis

GO functional analysis showed these DEGs were mainly enriched in protein tyrosine/threonine phosphatase activity, MAP kinase tyrosine/serine/threonine phosphatase activity, mitogen-activated protein kinase (MAPK) binding, SMAD binding, and ubiquitin-like protein ligase binding. Barplot and dotplot of GO enrichment analysis was shown in Figure 4 (P < 0.05). 

Figure 4.

 

GO function analysis of statistically significant DEGs in AS (A), BD (B), and sarcoidosis (C). The red barplots and dotplots were mostly enriched.

KEGG pathway analysis demonstrated these DEGs were mainly involved in the signaling by Salmonella and Yersinia infection, fluid shear stress and atherosclerosis, cellular senescence, autophagy, MAPK signaling pathway, gonadotropin-releasing hormone signaling pathway, IL-17 signaling pathway, and neurotrophin signaling pathway. However, it should be noted that pathways in BD were all not statistically significant (P > 0.05) and the data were eliminated. Barplot and dotplot of KEGG pathway analysis for AS and sarcoidosis was shown in Figure 5 (P < 0.05). 

Figure 5.

 

KEGG pathway analysis of statistically significant DEGs in AS (A) and sarcoidosis (B). The red barplots and dotplots were mostly enriched.

Discussion

In this study, we explored and filtered novel lncRNAs that specifically change (up- or down-regulate) in expression level for three major systemic autoimmune diseases most likely complicated with noninfectious uveitis by performing a bioinformatics analysis of publica dataset (GEO). We also aimed to illuminate (1) that AS, BD, and sarcoidosis possess distinct lncRNA and mRNA expression profiles when compared with healthy controls, and (2) the potential mechanisms for gene interactions or targeting effects identified for noninfectious uveitis among lncRNA, miRNA, and mRNA. Currently, the diagnosis of noninfectious uveitis is based on clinical symptoms and ophthalmologist experience because sensitive and specific biomarkers are still controversial. Furthermore, treatment therapy for noninfectious uveitis remains limited to steroid or immunosuppressants with great side effects. Thus, it is significant that our research achieved discrimination with high confidence of these three major forms of noninfectious uveitis from healthy condition; these genes we screened and identified may contain novel biomarkers or therapeutic targets for noninfectious uveitis or other autoimmune disease. 

XIST, also known as lncRNA X-inactive specific transcript, described by Brown et al.²³ in 1992, was one of the first identified lncRNAs. Human XIST cDNA is totaling 17 kb and contains at least eight exons, among them include several tandem repeats and a highly conserved 5ʹ segment.²³ Because XIST expressed exclusively from inactive X chromosome, it has been considered the master regulator of X-chromosome inactivation, the epigenetic process equalizes dosage of sex-linked genes between female (XX) and male (XY).²⁴ Previous literature reported that XIST induces gene silencing through the recruitment of several chromatin-modifying molecules that related to the process of X inactivation and may be the first important function of XIST.²⁴ In recent years, accumulating evidence suggests that XIST also serves as an important regulator of cell metabolism and by functioning as a ceRNA in carcinogenesis or other human diseases, although the regulatory mechanism remains controversial.²⁵ At present, most studies have proved that, as an oncogene, XIST is related to tumorigenesis. Previous articles have reported XIST can exert its effects via sponging miR-362-5p; thus, the down-regulation XIST and UBAP1 (miR-362-5p target gene) can intensify breast cancer cells proliferation, migration, and invasion.²⁶ Otherwise, Zong et al.²⁷ revealed the up-regulation of XIST sponge miR-125b-5p and subsequent influence on NLRC5 expression. 

XIST is also an immune-associated lncRNA. Multiple publications have shown the association between XIST and B-cell immune responses, which is observed commonly in autoimmune diseases.^28–30 Apart from that, in 2019, researchers have observed specific XIST effects in T cells that contribute to the pathogenesis of autoimmune diseases like systemic lupus erythematosus.³¹ With specific skewed allelic expression patterns and sex-biased effects, XIST is associated with biased risks in women against autoimmune diseases from an epidemiological study, reflecting the pathogenic effects of XIST on autoimmune diseases. 

XIST is the trigger of X-chromosome inactivation that, according to previous studies, is exclusively expressed in females. The variance of XIST expression we observed between different disease status reflects the differences between XIST expression in females only, although diluted by male subjects during sex adjustment. The significant differences for XIST across the status reflect that XIST may be a disease-associated gene in females only, potentially even more significant than we have observed. 

Myocardial infarction-associated transcript (MIAT) has been initially identified as a lncRNA by Ishii et al.³² in 2006. MIAT located on human chromosome 12q12.1 and existed as four transcripts: NR_003491.3, NR_033319.2, NR_033320.2, and NR_033321.2.³² It is generally recognized that MIAT exhibits two main functions, namely, transcriptional and post-transcriptional level gene regulation. MIAT plays a regulatory role by interacting with nuclear factors at transcriptional level in the nucleus, whereas at the post-transcriptional level, it mediates through the mechanism of competitive endogenous RNA (ceRNA) in the cytoplasm.³³ Thus, MIAT has been endowed ability to extensively influence serious cellular functions such as cell proliferation, apoptosis, cycle progression, and migration.³⁴ 

Potential mechanisms of MIAT regulatory function may include three aspects: (1) MIAT as a competitive endogenous RNA, which has been regarded as an important molecule to regulate gene expression at the post-transcriptional level; (2) MIAT as an upstream regulatory gene that targets proteins, several researchers determined the proteins that show the most significant changes after the knockout of MIAT; and (3) MIAT as an upstream molecule regulating multiple signaling pathways, such as tumor suppressor pathways (p53/p21 and p16/pRb), PI3K/Akt/mTOR, and Wnt/β-catenin signaling pathway.³⁴ 

Similar to XIST, MIAT is also an autoimmune disease–associated gene. With a specific role in immune cells,³⁵ MIAT has been shown to contribute to the pathogenesis of systemic lupus erythematosus by interacting with miR-222.³⁶ Another independent study on inflammatory diseases shown that lncRNA MIAT regulates IL-1 beta and TNF-α and contributed to another autoimmune disease, namely, rheumatoid arthritis.³⁷ Early in 2013, a summary on lncRNAs and complex human diseases also shown specific associations between MIAT and autoimmune diseases.³⁸ 

In a review summary on circular and lncRNAs and their role in ophthalmologic diseases, our top lncRNAs, XIST and MIAT, have been shown to be associated with the prevention of inflammatory eye diseases, including uveitis. Therefore, such recognized biomarkers can also help us to develop new methods or medicine against uveitis, reflecting their potential therapeutic values. Although the clinical evaluation on the selected biomarkers is planned to be done in our next step analyses, two such selected biomarkers are associated with uveitis with significant clinical values. 

In this study, we also found lncRNA XIST and MIAT were both significantly up-regulated in the peripheral blood of patients with AS, BD, and sarcoidosis, which implies that XIST and MIAT may correlate with autoimmunity and noninfectious uveitis. MIAT was significantly up-regulated and promoted inflammation in sepsis-induced cardiac injury via the miR-330-5p/TRAF6/nuclear factor κB (NF-κB) axis. MIAT knockdown inhibited the expression of inflammatory cytokines such as TNF-α, IL-6, and IL-1β.³⁹ MIAT expression pattern was also positively correlated with TNF-α, IL-6, and IL-8 and negatively correlated with IL-10 expression level in patients with coronary artery disease.⁴⁰ Most other studies also certified that MIAT played a regulatory role by activating the NF-κB pathway and is closely related to the up-regulation of several inflammatory cytokine, especially the interleukin family expression level. For XIST, recent findings also demonstrated the pivotal role of XIST in the progression of inflammatory response, which facilitates acute inflammatory responses and bovine mammary epithelial cell inflammatory responses via the NF-κB/NLRP3 inflammasome signaling pathway.⁴¹ The NF-κB signaling pathway is involved in regulating the production of inflammatory cytokines, including TNF-α, IL-1β, IL-6, and IL-8.⁴² In addition, the regulation of XIST exists in most inflammation processes including acute inflammation response in female cells, inflammatory injury of microglia cells after spinal cord injury, and oxidized low-density lipoprotein–induced inflammatory responses and pain in rat.²⁵ In this study, through the analysis of the gene expression data of GSE18781 and GSE17114, we also observed a statistically significant up-regulation of TNF-α and NF-κB expression in peripheral blood of patients with AS (P = 0.02 and 0.001) and patients with sarcoidosis (P = 0.003 and P < 0.001); TNF-α and NF-κB were up-regulated in patients with BD, although there was no statistical significance (P = 0.55 and P = 0.27). In addition, in the peripheral blood of patients with AS and patients with sarcoidosis, the expressions of IL-1β, IL-6, and IL-8 in interleukin family were significantly up-regulated (P = 0.03, P = 0.01, P = 0.03; P = 0.03, P < 0.001, and P < 0.001), whereas the expression of IL-10 was significantly down-regulated (P = 0.003 and P < 0.001). For patients with BD, although the expression changes of these four were the same as those in patients with AS and sarcoidosis, they were not statistically significant (P = 0.6, P = 0.5, P = 0.6, and P = 0.5). 

This project also revealed that the pathogenesis of noninfectious uveitis may be linked strongly with several protease/protein kinase activization or binding, autoinflammatory reactions, and, interestingly, certain pathogen infections by GO and KEGG pathway enrichment. Notable among the former is the MAPK signaling pathway, a well-known inflammatory cytokine pathway associated with TNF-α. The pathway analysis revealed that several altered expression genes found were involved in the MAPK signaling pathway, possibly through post-transcriptional regulation by lncRNAs. The fact that MAPK signaling pathway ranked the top pathway in the analysis using target genes provides additional evidence that this pathway is activated in noninfectious uveitis, which may be the reason for the therapeutic effect of anti–TNF-α drugs such as adalimumab and golimumab.⁴³ In contrast, Salmonella and Yersinia, two pathological gut microbiota infections, were also found to be associated with one of uveitis entities (sarcoidosis) in pathway analysis. Previous studies hypothesis formulated in this regard proposed that dysbiosis can increase intestinal permeability by facilitating the presentation of microbial products that trigger ocular inflammation, both through direct effects on eyes and indirectly through mechanisms of molecular mimicry and immune sensitization.⁴⁴ Our research also provides evidence for this hypothesis. 

There remain several limitations that might be considered seriously when interpreting the results in this study. First, the conclusion was only based on the bioinformatic analysis instead of experimental verification; thus, further in vitro and in vivo experiments should be performed to validate our findings. Second, the RNA microarray data included the peripheral blood of patients with AS, BD, and sarcoidosis; the limitation of a small variety of diseases and samples may lead to an overestimated conclusion or declinational results; therefore, a large disease and sample size combined multicenter clinical studies should be conducted to confirm the results. 

In conclusion, to our knowledge, this study is the first to be published of a comprehensive lncRNA analysis that identifies peripheral blood lncRNA profiles in patients with AS, BD, and sarcoidosis, providing new insight into the pathophysiology and diagnosis of these three major uveitis entities. Our next analyses will apply our newly identified biomarkers to build up effective therapeutic approaches to improve the diagnosis rate and clinical outcome of noninfectious uveitis. In the future, the functions of these lncRNAs will be analyzed using animal models, such as the experimental autoimmune uveitis model. 

Acknowledgments

Disclosure: S. Lu, None; P. Lu, None