Long noncoding RNA expression profile from cryptococcal meningitis patients identifies DPY19L1p1 as a new disease marker

Summary Aims LncRNAs play a vital role in the pathological and physiological process. This study aimed to explore the involvement of lncRNAs in cryptococcal meningitis. Methods Microarray was performed in cryptococcal meningitis patients, and then, GO and KEGG pathways were analyzed. Coexpression relationship between lncRNA and mRNA was explored. The expressions of the lncRNAs and mRNAs, and their changes after treatment were detected by PCR. Results A total of 325 mRNAs (201 upregulated and 124 downregulated) and 497 lncRNAs (263 upregulated and 234 downregulated) were identified. The top three enriched GO terms for the mRNAs were arachidonic acid binding, activin receptor binding, and replication fork protection complex. The top three pathways in KEGG were asthma, one carbon pool by folate, and allograft rejection. A total of 305 coexpression relationships were found between 108 lncRNAs and 87 mRNAs. LncRNA‐DPY19L1p1 was significantly increased in patients and decreased after treatment. ROC analysis revealed DPY19L1p1 was a potential diagnostic marker (AUCROC = 0.9389). Furthermore, the target genes of DPY19L1p1 in cis or trans regulation were mainly involved in immune‐related pathways like the interleukin signaling pathway. Conclusions This study analyzed the differential lncRNA profile in cryptococcal meningitis patients and revealed DPY19L1p1 could be used for treatment evaluation and disease diagnosis.

opportunistic fungi, the progress and prognosis of cryptococcosis predominantly depend on the interplay between the host immune response and the fungus. However, most current studies used murine models or healthy human cells to assess the aberrant levels of immune-related factors. Although a recent study has shown the differentially expressed genes of Cryptoccoccus at the site of human meningitis infection, 4 the key immune system regulators in patients with cryptococcal meningitis are poorly known.
LncRNAs are a large family of noncoding RNAs, accounting for approximately 85% of the transcribed human genome; 5 LncRNAs are widely expressed in a variety of immune cells, including T cells, B cells, monocytes, and dendritic cells and can function as key regulators of immunogene transcription. 6 The precise regulation of lncRNAs is important in maintaining homeostasis. Abnormally expressed lncRNAs participate in many immune-related diseases, such as autoimmune diseases, 7 bacterial diseases, and viral diseases. 8,9 The fungal lncRNA RZE1 has been reported to control the Cryptococcus yeast-to-hypha transition by regulating the key morphogenetic regulator Znf2, 11 indicating that lncRNAs are involved in fungal virulence. However, little is known about the role of host lncRNAs during fungal infection, especially during Cryptococcus infection, in clinical settings.
In this study, based on microarray and bioinformatic analysis, for the first time, we reported the differential lncRNA profile in cryptococcal meningitis patients and revealed DPY19L1p1 could be used not only in treatment evaluation but also for disease diagnosis through receiver operating characteristic curve analysis.

| Subjects
A 5 mL volume of venous blood was collected from twenty cryptococcal meningitis patients and eighteen healthy donors from the Changhai hospital and Changzheng hospital. The diagnosis of cryptococcal meningitis was based on India ink staining and/or positive culture of C. neoformans from cerebrospinal fluid. 12 The age and gender of the healthy control and the cryptococcal meningitis groups were not significantly different. All subjects were confirmed to be HIV-negative.
Ficoll density gradient centrifugation was used to harvest peripheral blood monocytes (PBMCs) as previously described. 13 PBMCs were then stored in liquid nitrogen. Informed consent was obtained from all subjects, and this study was approved by the ethics committees of the Changhai hospital and Changzheng hospital (Shanghai, China).

| RNA extraction and chip analysis
Total RNA was extracted and purified using a miRNeasy Mini Kit following the manufacturer's instructions, and RNA integrity was evaluated by the RNA integrity number (RIN) with an Agilent Bioanalyzer 2100. For the chip analysis, total RNA was amplified and labeled by a Low Input Quick Amp WT Labeling Kit following the manufacturer's instructions. Labeled cRNA was purified by an RNeasy Mini Kit. Each slide was hybridized with 1.65 μg Cy3-labeled cRNA using a Gene Expression Hybridization Kit in a hybridization oven to the manufacturer's instructions. After hybridization for 17 hours, the slides were washed in staining dishes with a Gene Expression Wash Buffer Kit following the manufacturer's instructions. The slides were scanned by an Agilent scanner the default settings (dye channel: green, scan resolution = 3 μm, PMT 100%, 20 bit). The data were extracted with Feature Extraction software 10.7. The raw data were normalized by the quantile algorithm of the limma package in R.

| Gene ontology and Kyoto encyclopedia of genes and genomes analyses
GO analysis covers three domains as follows: cellular component, molecular function, and biological process. The GO and KEGG enrichment analyses were performed with Fisher's exact test based on the data package ClusterProfiler (R/bioconductor); the selection criterion was that the fold change in the gene expression must be ≥2 with a P-value of <0.05. The enrichment factor (enrich_factor) was defined as follows: enrich_ factor = (number of differentially expressed genes in the GO term/total number of differentially expressed genes)/(total number of genes in the database term/total number of genes in the database).

| Correlation analysis between lncRNAs and mRNAs
The network between lncRNAs and mRNAs was constructed based on the correlation analysis of differentially expressed lncRNAs and protein-coding genes. For each lncRNA-mRNA pair, Pearson correlation was performed to assess the correlation.
Pairs for which the absolute value of the Pearson correlation coefficient was not <0.80 and the P-value was <0.05 were selected to generate the network using Cytoscape (National Resource for Network Biology).

| Real-time PCR
For real-time PCR, total RNA was extracted using TRIzol reagent, and qRT-PCR was performed to verify the RNA sequencing (RNAseq) data using SYBR Green (TaKaRa, Japan) and an ABI 7500 SDS system (Applied Biosystems, USA). The primer sequences are shown in Table 1. Beta-actin was used as the endogenous control. The relative expression value of the gene of interest was calculated via the 2 -ΔΔCt method.

| Statistical analysis
Differential comparisons between groups were made by a t test. A P-value of <0.05 was considered statistically significant. All statistical analysis was performed with GraphPad Prism software (La Jolla, CA, USA).

| Clinical characteristics of cryptococcal meningitis patients
Twenty cryptococcal meningitis patients (eight female, twelve male; age range: 21-56 years, median age: 43 years) and eighteen healthy controls (six female, twelve male; age range: 19-50 years, median age: 38 years) were included in this study. The clinical information is shown in Table 2. PBMCs from three randomly selected cryptococcal meningitis patients (P1-P3) and three healthy controls were used for the microarray analysis.

| Differential expression profiles of lncRNAs and mRNAs between cryptococcal meningitis patients and healthy controls
In this study, 18  tified. In addition, circos plots were generated to demonstrate the chromosomal distribution of these differentially expressed lncRNAs and mRNAs ( Figure 1E). The top five KEGG pathways were as follows: asthma (hsa05310), one carbon pool by folate (hsa00670), allograft rejection (hsa05330), biosynthesis of unsaturated fatty acids (hsa01040), and p53 signaling pathway (hsa04115).

| Classification of differentially expressed lncRNAs
The type of lncRNA can indicate its regulatory function. As shown in Figure 3A, the majority of the lncRNAs were intergenic (40.12%),

| LncRNA and mRNA coexpression analysis
Coexpression network analysis is another method used to predict lncRNA function. In this study, 305 coexpression relationships were found between 108 lncRNAs and 87 mRNAs ( Figure 4). Many mRNAs, including those coding for genes such as smad family member 6 (SMAD6), centromere protein A (CENPA), kinesin family member 20A (KIF20A), defensin alpha 6 (DEFA6), and oxidized LDL receptor 1 (OLR1), were found to interact with several lncRNAs. In addition, connections were also found between several lncRNAs, such as RP11-11D12.2, and several mRNAs.

| Validation of differential expression of mRNA and lncRNA and dynamic changes after treatment
Nine mRNAs and seven lncRNAs were randomly selected for   showed a significant decrease after treatment ( Figure 5E,F), which was consistent with titer changes ( Figure 5G). Then, the receiver operating curves (ROC) were drawn for evaluating the diagnostic potential of DPY19L1p1 for cryptococcal meningitis, which revealed that DPY19L1p1 was able to discriminate between patients and healthy controls with an AUC ROC of 0.9389 ( Figure 5H); P < 0.0001.

| Functional prediction of lncRNA DPY19L1p1 acting in a cis or trans manner
LncRNAs regulate genes of interest mainly in a trans or a cis manner.
Cis-regulated genes were selected within a 10 kb distance. LncRNA targets are shown in Figure 6A. One cis target gene, namely, AVL9, and one hundred and twenty-four trans target genes were predicted. The top five pathways ( Figure 6B,C). involving these target genes were as follows: interleukin signaling pathway, apoptosis signaling pathway, insulin/insulin growth factor (IGF) pathway-protein kinase B signaling  Most of the lncRNA differentially expressed in cryptococcal meningitis patients was intergenic lncRNA. A total of 305 coexpression relationships were found between 108 lncRNAs and 87 mRNAs.

| D ISCUSS I ON
LncRNA DPY19L1p1 was found to be highly expressed in cryptococcal meningitis patients via PCR validation and tended to decrease after antifungal treatment. In addition, ROC analysis showed DPY19L1p1 had an AUC ROC of 0.9389, indicating an excellent diagnosis potential. Furthermore, the target genes of DPY19L1p1 in cis or trans regulation were predicted, and most were involved in immune-related pathways such as the interleukin signaling pathway.
LncRNAs, a large class of noncoding RNAs, are known to be key regulators in many cellular activities, including chromatin remodeling, transcription, splicing, mRNA stabilization, protein translation, and protein translocation. 14 Several lncRNAs regulate immunogene expression in response to pathogens, and their expression level is dynamically regulated by the interaction between host and microbes. 15 Aberrant host lncRNA expression was observed upon viral, bacterial, and fungal infection in vitro. 16,17 Furthermore, some pathogens may utilize host-expressed lncRNAs to decrease the host immune response. 19 For the first time, we described the aberrant expression been previously reported to be involved in oncogenesis by activating the PI3K/AKT/mTOR and Wnt signaling pathways in vitro. 20,21 Therefore, the exact involved mechanism of these lncRNAs needs further research.
As lncRNAs with similar functions can interact or present similar network data profiles, 22,23 a coexpression network between lncRNAs and mRNAs was constructed to predict the potential function of lncRNAs. A total of 305 coexpression relationships were found between 108 lncRNAs and 87 mRNAs. Many key immune regulators, such as SMAD6, were connected to several lncRNAs.
During microbial defense, SMAD proteins are activated to induce a protective inflammatory response and are essential for immune system balance. 24 Another method to predict the function of ln-cRNAs is lncRNA classification 25  In addition to protein-noncoding RNAs, most of the protein-coding genes identified by PCR, including CAMP, LT, CTSG, OLR1, BPI, PGLYRP1, CEACAM8, and OLFM4, which were highly expressed in this study, were found for the first time to be involved in cryptococcal meningitis, although these genes were already known to be involved in antimicrobial and inflammatory responses. 27,28 Notably, most of the overexpressed genes in our study were related to the NF-kappaB pathway, consistent with a previous report that the NF-kappaB signaling pathway can be manipulated by C. neoformans within macrophages. 39 The effective defense against microbes depends on the elaborate collaborative function of the innate and adaptive immune systems. To this end, cytokine signaling could shape the outcome of cryptococcal infection; 40  Although the asthma and p53 signaling pathways were previously reported to be related to cryptococcal infection, 39,42 new related pathways, including the one carbon pool by folate, allograft rejection, and biosynthesis of unsaturated fatty acids pathways, were identified in this study. These novel pathways furnished another perspective on the immune response triggered by cryptococcal infection.
The dynamic changes in these differentially expressed mRNAs and lncRNAs were also explored. Interestingly, the expression of DPY19L1p1 significantly decreased after antifungal treatment. In addition, ROC analysis showed DPY19L1p1 had an AUC of 0.9389, which indicated that DPY19L1p1 may not only be an indicator for treatment evaluation but also for disease diagnosis. However, these results still need to be confirmed by a larger sample size.
Because lncRNAs are importanttrans and cis regulators, trans and cis targets of DPY19L1p1 were predicted to suggest the potential involved pathways. One cis target gene and one hundred twentyfour trans target gene were predicted, and their related pathways were explored. 43 Most target genes of DPY19L1p1 were involved in immune-related pathways such as the interleukin signaling pathway and p53 pathway feedback loops, indicating that DPY19L1p1 may be involved in the host antimicrobial response against cryptococcal by targeting these immune-related pathways. However, functional experiments are needed to validate this hypothesis.

| CON CLUS ION
In conclusion, for the first time, the aberrant expression of lncR-NAs in the patients of cryptococcal meningitis was described, and new involved pathways were identified. Moreover, the results indicated that lncRNA DPY19L1p1 could be used not only in treatment evaluation but also for disease diagnosis. Our study provides new perspectives of the host immune response in cryptococcal meningitis and may aid in future immune-based therapy research.

CO N FLI C T O F I NTE R E S T
The authors declare there are no conflicts of interest.