Research ArticleIntegrated Analyses of lncRNA and mRNA Profiles RevealCharacteristic and Functional Changes of Leukocytes inQi-Deficiency Constitution and Pi-Qi-Deficiency Syndrome ofChronic Superficial Gastritis

Leiming You ,1 Aijie Liu ,1 Xiaopu Sang,1 Xinhui Gao,1 Ting’An Li,1 Shen Zhang,1

Kunyu Li,1 Wei Wang,1 Guangrui Huang,1 Ting Wang,1 and Anlong Xu 1,2

1School of Life Sciences, Beijing University of Chinese Medicine, Beijing 100029, China2State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes,School of Life Sciences, Sun Yat-sen University, Higher Education Mega Center, Guangzhou 510006, China

Correspondence should be addressed to Anlong Xu; xuanlong@bucm.edu.cn

Leiming You and Aijie Liu contributed equally to this work.

Received 7 December 2019; Revised 2 June 2020; Accepted 15 June 2020; Published 16 July 2020

Academic Editor: Mark Moss

Copyright © 2020 Leiming You et al. .is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objects. To investigate the lncRNA-mediated trans- and cis-regulation of genes expression underlying leukocyte functionsand characteristics, especially the leukocyte-related biomarkers implicated in the linkage between traditional Chinesemedicine- (TCM-) defined qi-deficiency constitution (QDC) and Pi-qi-deficiency syndrome (PQDS) of chronic superficialgastritis (CSG). Methods. We adopted RNA-sequencing approach to identify differential lncRNAs and genes in leukocytes,clustered expression profiles, and analyzed biological functions and pathways of differential genes to decode their potentialroles in contributing to characteristics and functions of leukocytes. In addition, interaction networks were created to detailthe interactions between differential genes. In particular, we explored differential lncRNAs-mediated regulation of dif-ferential genes and predicted the subcellular location of lncRNAs to reveal their potential roles. Results. Compared withTCM-defined balanced constitution (BC), 183 and 93 genes as well as 749 and 651 lncRNAs were differentially expressed(P

1. Introduction

Traditional Chinese medicine (TCM) is an ancient medicalpractice system with the longest history in Asia [1]. Qi,pronounced “chee,” means energy in TCM. It is spelled“Chi” or even “Ki” in Japanese, but they all carry the samemeaning. Qi is the energy of the body, of the meridians.TCM-defined constitution is innate and relatively stable,relying on the intrinsic characteristics of body. It can beinfluenced by postnatal environment and defined by inte-grating the morphological structure and physiologicalfunction with psychological state [2–5]. Qi-deficiencyconstitution (QDC) is one of nine typical body constitutionsdefined in TCM [3, 4]. Following TCM theories, especiallythe clinical data, individuals with QDC seem to have atendency toward the Pi-qi-deficiency syndrome (PQDS),one of the commonly matched TCM syndromes in patientswith chronic superficial gastritis (CSG) [6–8]. Also, indi-viduals with the QDC or PQDS share very similar featuresincluding lacking vitality (Qi) and getting sick easily, beingcharacterized by languid lazy words, low voice, pale tongue,and weak pulse [4, 8]. .ese phenomena together couldsuggest that there is a possible linkage between QDC andPQDS. However, little is known about the underlyingmolecular characteristics of QDC and PQDS, particularlythe potential biomarkers implicated in linking QDC toPQDS of CSG. In recent years, advances in next-generationsequencing (NGS) technology enable the multiomics anal-ysis of human diseases, especially the discovery of disease-related biomarkers [9–11]. Long noncoding RNAs(lncRNAs), a type of noncoding RNAs (ncRNAs) longerthan 200 nucleotides, have been reported to be involved inmultiple pathological processes such as inflammation, car-cinogenesis, and tumor progression [12–15].

.us, in this study, the popular RNA-sequencing (RNA-seq) approach was adopted to identify differentiallyexpressed lncRNAs and genes in the blood leukocytes ofindividuals from different populations, including the controlpopulation (balanced constitution, BC), case population 1(QDC), and case population 2 (PQDS of CSG). .e specificlncRNAs and genes identified in a certain case populationare probably case-specific, and the common lncRNAs andgenes may be the potential candidate biomarkers involved inlinking QDC to PQDS. Enrichment analyses, including GO,pathway, disease, and domain, were performed on thecommon genes and the case-specific genes to explore theirpotential roles in contributing to characteristics and func-tions of leukocytes. Also, interactions network analyses weredone for the case-specific genes to detail the physical andfunctional protein-protein associations. We analyzed thelncRNA-mediated trans- and cis-regulation of gene ex-pression for each case population. In particular, we detailedlncRNA-mediated regulation of candidate biomarkers, in-cluding direct binding of lncRNAs to mRNAs and mRNAs-corresponding proteins. We also analyzed the subcellularlocation of common lncRNAs regulating these biomarkergenes and predicted the RNA-binding proteins (RBPs) forthe exosome-contained lncRNAs to reveal their potentialroles when traveling throughout the body.

2. Materials and Methods

2.1. Ethics Approval. .e study was registered at Clinical-Trials.gov (identifier: NCT02915393). .e protocol was ap-proved (JDF-IRB-2016031002) by the Institutional ReviewBoard of Dongfang Hospital affiliated to Beijing University ofChinese Medicine. All the methods were performed in ac-cordance with the relevant guidelines and regulations. Par-ticipants were informed of the purpose, general contents, anddata use of the study, and they all signed the informed consent.

2.2. Participants and Experimental Design. All the subjects(detailed in Supplemental Table S1) were recruited at thehepatobiliary and gastroenterological outpatient’s depart-ment, Dongfang Hospital affiliated to Beijing University ofChinese Medicine. According to the Constitution of TCMClassification and Judgment published by China Associationof Chinese Medicine in 2009 [16], individuals with variousTCM constitutions (BC and QDC) were identified andrecruited. CSG patients with PQDS were diagnosed andrecruited, based on the Guiding Principle for Clinical Re-search on New Drugs of TCM published in 2002 [17] and theCSG pathological diagnosis and grading standards whichwere proposed on the China Chronic Gastritis Consensus inShanghai, 2012 [18]. .e experimental design (SupplementalFigure S1), including the diagnosis, inclusion, and exclusioncriteria for all the subjects in this study, is detailed in thesupplemental information (Supplemental Methods).

2.3. Leukocytes. Blood samples (5mL) were collected in theadditive-free blood collection tubes, and the leukocytes wereisolated using the lymphocyte separation reagent (Solarbio)according to the manufacture’s instruction.

2.4. RNA Sequencing. RNA sequencing was performed byOEbiotech Company (Shanghai, China). Briefly, total RNAs ofleukocytes were extracted using the RNA isolation kit(Ambion) following themanufacturer’s instructions and storedat −80°C. RNA integrity number (RIN) was detected using thepopular Agilent 2100 Bioanalyzer. RNA samples (RIN≥7) weresubjected to the subsequent high-throughput sequencinganalysis. All the transcriptomic sequencing libraries wereconstructed using the commercial kit (Illumina, RS-122-2301)termed “TruSeq Stranded Total RNA with Ribo-Zero Globin”(this kit keeps an efficient work flow enabling removal of ri-bosomal RNA and globin mRNA in a single step). .ese re-sultant sequencing libraries were sequenced by the well-knownIllumina sequencing platform (HiSeqTM 2500), and 150bp/125 bp paired-end raw reads were generated. .e generatedraw reads were filtered, mapped to the human reference ge-nome (GRCh38.p7) (Supplemental Table S2), and processedfor the subsequent annotation and differential expressionanalyses of lncRNAs and mRNAs.

2.5. Identifying Differentially Expressed lncRNAs and Genes..e expression levels of lncRNAs andmRNAs, including thenovel lncRNAs discovered in this study, were standardized

2 Evidence-Based Complementary and Alternative Medicine

https://clinicaltrials.gov/ct2/show/NCT02915393

and indicated using FPKM (fragments per kilobase of exonmodel per million mapped reads). .e identification oflncRNAs and genes between groups and the P value cal-culations were performed using the R package of DESeq [19]..e differential lncRNAs and genes among groups werefiltered (P value 0.8 and P value 0.8)of gene and lncRNA, if the gene is close (less than 100 kb) tothe lncRNA in a genome, it was considered as a candidatetarget of the lncRNA. However, trans-acting lncRNA and itspossible target genes are located in different chromosomes,and especially lncRNA is capable of binding to mRNA..us,we first eliminated the coexpression pairs (r>0.8) whichhave lncRNAs and genes encoded in the same chromosome.Further, using the software “RIsearch” v2.0 (a large scaleRNA-RNA interaction prediction tool) [31], we evaluatedthe binding ability of lncRNA and mRNA for each obtainedcoexpression pair, setting parameters as hybridization siteslength >20 nt, binding free energy 0.5 and RF (random forest)>0.5. Accordingly, the predictions with probabilities >0.5were considered “positive,” indicating that the corre-sponding RNA and protein are likely to interact with eachother.

2.12. Predicting RNA-Binding Proteins (RBPs) of lncRNA..e lncRNAs were applied to analyze their possible bindingproteins, using the web interface of RBPDB (v1.3.1) [34], anRNA-binding protein database collecting the experimentalobservations of RNA-binding sites, both in vitro and in vivo..e threshold was set to 0.8 (match scores that are greaterthan 80% of the maximum score for that PWM).

3. Results

3.1. Differential lncRNAs and Genes in Leukocytes.Compared with BC (control population, n� 5), 183 and 93differential genes (Supplemental Tables S3 and 4) as well as749 and 651 differential lncRNAs (Supplemental Tables S5and 6) were discovered in the QDC (case population 1, n� 2)and the CSG patients with PQDS (case population 2, n� 5),respectively. In particular, 12 common genes and 111common lncRNAs were found in the case populations(Figure 1(a)), considered as the leukocyte candidate bio-markers implicated in QDC and PQDS of CSG. We per-formed HCL analyses of expression profiles of differentialgenes and lncRNAs in leukocytes from the three populations.

Evidence-Based Complementary and Alternative Medicine 3

http://string-db.org

638

171 8112

540111

Case population 1(case 1)

Qi-deficiency constitution

Case population 2(case 2)

Pi-qi-deficiency syndrome

�e common lncRNAs (111)

�e common genes (12)

lncRNAs

Genes

(a)

Case 2(n = 5)

Case 1(n = 2)

Control(n = 5)

log

2 (fo

ld ch

ange

)

0–1–2–3

321

(b)

Case 2(n = 5)

Case 1(n = 2)

Control(n = 5)

log

2 (fo

ld ch

ange

)

0–1–3–5

531

(c)

Case 2(n = 5)

Case 1(n = 2)

Control(n = 5)

log 2 (fold change)

0–1–2–3 321ADAMTSL5COL26A1LOC105371430LOC390937COL27A1ZFP57LOC105376526MSH5CORINSLC4A10MATN2OR1J2

(d)

Figure 1: Continued.

4 Evidence-Based Complementary and Alternative Medicine

HCL heatmaps showed that the expression patterns of thecommon differential genes and lncRNAs of each person inboth case populations were so similar (Figures 1(d) and 1(e)),obviously distinguished from the control population (BC).Also, for all the identified differential genes and lncRNAs,similar expression profiles were observed in both case pop-ulations (Figures 1(b) and 1(c)). In particular, the expressionprofiles of lncRNAs of the individuals in the two case pop-ulations were clustered into a big group consisting of twosubgroups which, respectively, corresponded to those of in-dividuals in case populations 1 and 2 (Figure 1(c)), suggestingthat there is a general linkage between QDC and PQDS, butthere are also differences between them.

3.2. GO Functions of Differential Genes. A total of 183 dif-ferential genes were found in case population 1 (QDC),containing 114 upregulated genes and 69 downregulatedgenes. GO enrichment analysis was performed to exploretheir functions. We found that 10 GO (biological process)terms were significantly enriched, including peptidyl-tyro-sine phosphorylation, protein tetramerization, amino acidtransport, and especially the terms related to cell-cell

adhesion and communication such as extracellular matrixorganization, cell adhesion via plasma membrane adhesionmolecules, and integrin-mediated signaling (Figure 2(a)).Moreover, GO (cellular component, molecular function)terms indicated that these enriched genes were responsiblefor encoding plasma membrane proteins (Figure 2(c)) andmaintaining the protein binding and transmembranetransporter activity (Figure 2(b)).

Also, 93 differential genes were identified in case pop-ulation 2 (PQDS of CSG), consisting of 51 upregulated genesand 42 downregulated genes. Interestingly, these genesshared the GO (biological process) terms which were alsomatched in QDC, associated with cell-cell adhesion andcommunication including extracellular matrix organizationand cell adhesion via plasma membrane adhesion molecules(Figure 2(a)). However, the GO (biological process) termsspecifically enriched in the PQDS of CSG were these termssuch as the regulation of complement activation, collagencatabolic process, and innate/adaptive immune response(Figure 2(a)), belonging to extracellular region/space pro-teins or the integral components of membrane (Figure 2(c)).Considering all results together, common biological pro-cesses, namely, extracellular matrix organization and cell

Case 2(n = 5)

Case 1(n = 2)

Control(n = 5)

C

log 2 (fold change)

0–1–3–5 531lnc-ARHGAP27-2:1EDNRB-AS1:2LINC00152:10lnc-BTK-1:2lnc-RNF19A-8:4CDC42-IT1:1LINC00299:3lnc-UTP11L-6:1PSMD5-AS1:3lnc-JMJD7-PLA2G4B-2:3lnc-EID3-1:3TCL6:19TCONS_00001685∗lnc-PLEKHG2-1:1GAS5:31LINC01089:2TCONS_00000205∗lnc-AP001793.1-4:1lnc-AC233264.2-1:1lnc-DNAH10OS-8:1GAS5:41lnc-KLB-2:1lnc-CELF6-1:5lnc-RP11-108K14.4.1-1:2lnc-FGA-3:1PSMA3-AS1:6lnc-VPS37A-5:1lnc-RNF219-6:1TCONS_00049093∗TCONS_00049110∗lnc-DEFB128-1:1lnc-IFI44-8:1lnc-CACYBP-2:1lnc-RNF24-4:1lnc-GGCT-1:12lnc-EID2B-1:1TCONS_00048675∗lnc-SWI5-1:1LINC01237:6lnc-AMZ2-5:1lnc-MDK-4:2lnc-ARL1-3:1lnc-CSNK1D-1:1lnc-PGPEP1L-3:1lnc-PLEKHA7-2:1lnc-RPUSD2-2:1TCONS_00052867∗lnc-LRRFIP2-1:1lnc-VASH2-1:1lnc-HES5-1:7lnc-RP11-706O15.1.1-2:8lnc-DYDC1-1:1lnc-ZNF114-2:1TCONS_00048674∗lnc-PPIAL4C-5:2lnc-PPIAL4C-5:3lnc-AC131097.4.1-8:1lnc-EGLN1-1:3lnc-HIST1H2AI-1:3lnc-PRPF4B-4:4lnc-WDR7-6:2lnc-ARAF-1:1TCONS_00031767∗lnc-BCL2L2-PABPN1-1:1lnc-PRPF4B-1:2lnc-LIMA1-1:7PITPNA-AS1:2lnc-GFAP-4:1lnc-RPL10-1:1lnc-ZBTB37-2:1lnc-F13A1-2:3PAN3-AS1:5lnc-CLEC2D-8:6lnc-AP000525.1-4:1lnc-IGFBP7-1:2lnc-MTPN-1:1lnc-ING2-5:2lnc-RAB23-17:1lnc-HYOU1-1:3lnc-RPL10L-2:1lnc-YME1L1-1:1DLX6-AS1:13PRKCQ-AS1:28lnc-TWIST1-1:3TCONS_00052318∗TCONS_00048671∗lnc-FBXL2-4:1lnc-FAM32A-2:1lnc-FAM96A-1:1LINC01506:2lnc-AKAP11-1:2lnc-C12orf50-6:2lnc-HIVEP3-1:1lnc-CGNL1-6:1TCONS_00048609∗lnc-CA10-1:1lnc-ZNF212-2:2lnc-NPBWR1-1:2TCONS_00052868∗TCONS_00052862∗lnc-ZNF91-4:2lnc-MPPE1-5:1lnc-ZFAND4-1:1lnc-PTBP3-6:1lnc-CEP170-11:1lnc-ZMAT5-4:3lnc-RPRML-3:20lnc-KIAA0226-4:1lnc-AC103810.1-1:2lnc-TULP2-2:1lnc-THAP10-1:1

(e)

Figure 1: Expression profile analyses of differential genes and lncRNAs in leukocytes of individuals from different populations. (a) Venn diagramof the differential genes and lncRNAs identified in the two case populations. (b) Hierarchical clustering (HCL) analysis for expression profiles of allthe differential genes. (c) HCL analysis for expression profiles of all the differential lncRNAs. (d)HCL heatmap generated for expression patterns ofthe common differential genes. (e) HCL analysis for expression patterns of the common differential lncRNAs. Control: balanced constitution; case1: qi-deficiency constitution; case 2: chronic superficial gastritis patients with Pi-qi-deficiency syndrome.

Evidence-Based Complementary and Alternative Medicine 5

adhesion via plasma membrane adhesion molecules, wereimplicated in both QDC and PQDS of CSG, indicating thepossible alternation of cell-cell adhesion and communica-tion that contributed to the characteristics and functions ofcircling leukocytes in a body.

3.3. GO Functions, KEGG, and Reactome Pathways Identifiedby GSEA. Using the healthy population as control, the ob-served common GO biological processes with positive corre-lation with QDC and PQDS included negative regulation ofexecution phase of apoptosis, as well as regulation of apoptoticcell clearance, excitatory synapse assembly, postsynapse as-sembly, and postsynaptic density assembly. Also, commonbiological processes that had negative correlation with QDCand PQDS were implicated in the cotranslational protein andSRP-dependent cotranslational protein targeting tomembrane,maintenance of organelle location, and DNA replication-de-pendent nucleosome organization and assembly (SupplementalFigure S2). In addition, regarding the enriched pathways, theidentified common pathways keeping positive correlation withQDC and PQDS were the amphetamine addiction and otherReactome pathways, such as activation of C3 and C5, synthesisof 12-eicosatetraenoic acid derivatives, WNT5A-dependentinternalization of FZD4, cargo concentration in the ER, and

disinhibition of SNARE formation (Supplemental Figure S3)..e obtained GSEA results of genome-wide expression profilesrevealed that the TCM-defined QDC and PQDS share severalcommon enriched biological processes and pathways, sug-gesting the potential common contribution to the character-istics and functions of leukocytes in a body.

3.4. Enriched Pathways and Diseases: Interaction Network ofQDC-Specific Genes. .e QDC-specific genes in this studymean the differential genes which appeared only in indi-viduals fromQDC population rather than CSG patients withPQDS. As indicated (Figure 1(a)), a total of 171 differentialgenes were QDC-specific, and the pathway-based enrich-ment analyses of them were performed, including theKEGG, Panther, and Reactome pathways. .e enrichedKEGG/Panther pathways with higher rich factor(Figure 3(a)) contained the ionotropic glutamate receptor,cocaine addiction, and cell adhesionmolecules..ematchedReactome pathways (Figure 3(b)) included the immune celland system pathways (immunoregulatory interactions be-tween a lymphoid and a nonlymphoid cell, interleukin-7signaling, caspase activation via extrinsic apoptotic signal-ing, factors involved in megakaryocyte development andplatelet production), the cell cycle regulation pathways

GO:0051262~protein tetramerizationGO:0035092~sperm chromatin condensationGO:0006865~amino acid transportGO:0015804~neutral amino acid transportGO:0010842~retina layer formationGO:0007283~spermatogenesisGO:0007156~homophilic cell adhesion via plasma membrane adhesion moleculesGO:0007275~multicellular organism developmentGO:0018108~peptidyl-tyrosine phosphorylationGO:0007229~integrin-mediated signaling pathwayGO:0030449~regulation of complement activationGO:0030574~collagen catabolic processGO:0030198~extracellular matrix organizationGO:0045087~innate immune responseGO:2000427~positive regulation of apoptotic cell clearanceGO:0002250~adaptive immune responseGO:0051216~cartilage developmentGO:1903027~regulation of opsonizationGO:0006958~complement activation, classical pathwayGO:0030199~collagen fibril organizationGO:0046618~drug exportGO:0045959~negative regulation of complement activation, classical pathway

Case

1Ca

se 2

GO term (biological process)

0–1.3–2.3–3.3–4.3

32333

105

1054005000000000

0000023211556624324322

log(P

valu

e)

(a)

GO:0042802~identical protein bindingGO:0015175~neutral amino acid transmembrane transporter activityGO:0004714~transmembrane receptor protein tyrosine kinase activityGO:0032051~clathrin light chain bindingGO:0005201~extracellular matrix structural constituentGO:0001849~complement component C1q bindingGO:0008237~metallopeptidase activityGO:0017124~SH3 domain bindingGO:0008201~heparin binding

Case

1Ca

se 2

GO term (molecular function)

4

3

44

3

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0002

33

211

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GO:0005886~plasma membraneGO:0005887~integral component of plasma membraneGO:0005615~extracellular spaceGO:0072562~blood microparticleGO:0016021~integral component of membraneGO:0043025~neuronal cell bodyGO:0030425~dendriteGO:0005578~proteinaceous extracellular matrixGO:0005788~endoplasmic reticulum lumenGO:0031012~extracellular matrixGO:0005581~collagen trimerGO:0005576~extracellular regionGO:0044216~other organism cell

Case

1Ca

se 2

GO term (cellular component)

48

0

20

00

000

00

00

0

33

5

7

213

656

66

433

12

0–1.3–2.3–3.3–4.3

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(c)

Figure 2: Comparison of GO function enrichments of differential genes identified in the two case populations. (a) Comparison of biologicalprocess enrichments of differential genes. (b) Comparison of molecular function enrichments of differential genes. (c) Comparison ofcellular component enrichments of differential genes. Case 1: qi-deficiency constitution; case 2: Pi-qi-deficiency syndrome of chronicsuperficial gastritis.

6 Evidence-Based Complementary and Alternative Medicine

(CHK1/CHK2-mediated inactivation of cyclin B, TP53-regulated transcription of genes involved in G2 cell cyclearrest, TP53-regulated transcription of cell cycle genes), thetransmembrane transport pathways (amino acid transportacross the plasmamembrane, transport of inorganic cations/

anions and amino acids/oligopeptides, SLC-mediatedtransmembrane transport), and other pathways. In partic-ular, the InterPro domain and feature enrichment analysesdemonstrated that these QDC-specific genes-correspondingproteins contained immune-related domains including

Rich factor

KEGG/Panther FDR0.040.030.020.01

3

4

0.02 0.04 0.06 0.08

5

Gene count

Regulation of actin cytoskeletonp53 signaling pathway

Oocyte meiosisIonotropic glutamate receptor pathway

Hepatitis CGlycosphingolipid biosynthesis-lacto and neolacto series

Glutamatergic synapseCocaine addiction

Cell adhesion molecules (CAMs)

Path

way

term

(a)

0.040.030.020.01

51015

00 0.05 0.10 0.15

Transport of inorganic cations/anions and amino acids/oligopeptidesTP53 regulates transcription of genes involved in G2 Cell Cycle Arrest

TP53 regulates transcription of Cell Cycle GenesSLC-mediated transmembrane transport

Signaling by Rho GTPasesSignal transductionNetrin-1 signaling

Interleukin-7 signalingImmunoregulatory interactions between a Lymphoid and a non-Lymphoid cell

Immune systemHemostasis

HDMs demethylate histonesFactors involved in megakaryocyte development and platelet production

DiseaseChk1/Chk2(Cds1) mediated inactivation of Cyclin B: Cdk1 complex

Caspase activation via extrinsic apoptotic signaling pathwayBasigin interactions

Axon guidanceAmino acid transport across the plasma membrane

Reactome

Rich factor

FDR

Gene count

Path

way

term

(b)

FDR

Gene count

Rich factor

DiseaseOMIM

KEGG DISEASENHGRI GWAS Catalog

Spinocerebellar ataxia (SCA)

Sitting height ratio

Reproductive system disease

Prostate cancer

Lupus nephritis in systemic lupus erythematosus

Attention deficit hyperactivity disorder

Age-related macular degeneration

Dise

ase

0.04

2.02.22.5

0.08 0.12 0.16

2.73.0

0.030.02

(c)

Rich factor

FDR

Gene count

Interpro domainsTyrosine-protein kinase, active siteNatural killer cell like receptor

Immunoglobulin subtype 2Immunoglobulin subtype

Immunoglobulin I-setImmunoglobulin-like fold

Immunoglobulin-like domain superfamilyImmunoglobulin-like domain

Haemoglobin, beta-typeClaudin, conserved site

ClaudinC-type lectin-like/link domain superfamily

C-type lectin-like

Dom

ain

term

0.040.030.020.01

481216

0.1 0.2 0.3 0.4 0.5

(d)

Meaning of network nodes:

Line thickness indicates the interaction strength of data support

Meaning of network edges:

IPR006187: ClaudinIPR017974: Claudin conserved stie

IPR008266: Tyrosine-protein kinase, active site

IPR002337: Haemoglobin, beta-type

IPR003599: Immunoglobulin subtypeIPR007110: Immunoglobulin-like domainIPR013098: Immunoglobulin I-setIPR013783: Immunoglobulin-like foldIPR036179: Immunoglobulin-like domain superfamilyIPR003598: Immunoglobulin subtype 2

IPR033992: Natural killer cell receptor-like, C-type lectin-like domainIPR001304: C-type lectin-likeIPR016186: C-type lectin-like/link domain superfamily

Interpro domain and feature

Circle size represents the number of lncRNAs targeting the corresponding gene. �e number is shown on the right, detailed in format of “trans binding lncRNAs/cis regulation lncRNAs”.

Gene-targetted lncRNA count35/1

�e upregulated gene. Shade of red depends on the degree of upregulation of gene expression.

�e downregulated gene. Shade of green relies on the degree of downregulation of gene expression.

Gene upregulated or downregulatedCocaine addiction

pathway

Hepatitis C pathway

Cell adhesion molecules (CAMs) pathway

Glutamatergic synapse pathway

Oocyte meiosis pathway

Regulation of actin cytoskeleton

pathway

Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell pathway

P53 signalingpathway

13

0

557/1

0/1 15

68/2

31 35

24 0 3/1

10

76

0

4

36

1109

85

80

21

4

12

18/5

0/2

4/3

82

26 7

26/1

66

14

14

11

16

2/1 4 60 51 3/7 71/1 53/1 38/10/7 0/2

15/8 9/7 15/1 17/1 13/1 12/2 15/2 6/1 15/7

40

49 7

8

9 73/177 16

20

525

1 0

3

101

1

28 28

28

18

9

1

0

4

19

14

6

3

4626

48

139

16

1

12

23/1

27

6

13

4

11/1

KDM5D USP9Y B4GAT1 KIAA1324L NTHL1 TARP ZFY NFXL1 UTY

LOC101927789LOC105372343

KCNK7SLX1BDDX3YHSD17B13

TEAD2CFAP45RPS4Y1

DDX39B TAF4

SLC7A9

PUF60

HBD HBG2

ALAS2HIP1

PDE9A

DNM1

CSF2RA

GTPBP6

YTHDC1

CLK2

PDZD2

FAT4

FAM127B

NUDT11

RASA4

TRPC3

SLC43A2

SLC7A8

EIF1AY

(e)

Figure 3: Enrichment and interaction network analysis for the qi-deficiency constitution- (QDC-) specific genes. (a) Enriched KEGG andPanther pathways of QDC-specific genes. (b) Enriched Reactome pathways of QDC-specific genes. (c) Enriched diseases associated withQDC-specific genes. (d) Enriched domains and features of the proteins coded by QDC-specific genes. (e) Network to detail the interactionsamong QDC-specific genes. .e number close to a node denotes the number of QDC-lncRNAs targeting the corresponding node gene.

Evidence-Based Complementary and Alternative Medicine 7

immunoglobulin-like domain, natural killer cell receptor,C-type lectin-like domain, tyrosine-protein kinase activesites, and claudin conserved sites (Figure 3(d)). .e corre-sponding proteins containing various immune-related do-mains were specially marked by colorful arrows(Figure 3(e)). Moreover, the disease-based enrichmentanalysis showed that they were related to diseases such asage-related macular degeneration, lupus nephritis in sys-temic lupus erythematosus, and spinocerebellar ataxia(Figure 3(c)).

To detail the interactions of QDC-specific genes (Sup-plemental Table S7), as well as the regulatory relationships ofa gene and its trans- and cis-acting lncRNAs (QDC-specific)(Supplemental Tables S8 and 9), an interaction complexnetwork was carefully created, integrated with any type ofedge and node attribute data such as gene expression patternand the number of trans- and cis-acting lncRNAs(Figure 3(e)). In particular, several enriched pathways werehighlighted and annotated in the created network. .e re-sultant network not only detailed the interactions of QDC-specific genes (edge thickness indicates interaction strengthof data support), but also presented the gene expressionpattern (shade of green or red depends on degree ofdownregulation or upregulation of gene expression) and thenumber of trans- and cis-acting lncRNAs (node size denotesthe number of lncRNAs targeting the node gene, and thenumber is specifically shown in format of “trans-actinglncRNAs number/cis-acting lncRNAs number”). As shownin the network, overall, almost each gene was regulated bymultiple trans-acting lncRNAs and even additional cis-acting lncRNAs. For instance, each of these genes could beregulated by many trans-acting lncRNAs (more than 80),including CNOT3, SHANK1, KCNT1, CACNA1A, SLC9A1,and GRIK4. Also, each of the genes such as KDM5D, UTY,USP9Y, DDX3Y, and KLRG1 could be controlled by ad-ditional multiple cis-acting lncRNAs (over 5). Althoughmany genes had no interactions with each other (no edgebetween each other in network), they were also found to beregulated by trans- or cis-acting lncRNAs. Notably, multipletrans-acting lncRNAs and cis-acting lncRNAs were observedto target the genes encoding for the immune-related do-mains-contained proteins in the pathways associated withthe cell-cell adhesion and communication, such as celladhesion molecules (PTPRM, CLDN9) and immunoregu-latory interactions between a lymphoid and a nonlymphoidcell (KLRB1, KLRC, KLRG1). .erefore, the above-men-tioned results indicated that the QDC-specific lncRNAsplayed important roles in regulating the QDC-specific geneexpression profiles which determined the characteristics andfunctions of leukocytes in QDC population.

3.5. Interaction Network of PQDS-Specific Genes for EnrichedPathways and Diseases. A total of 81 PQDS-specific geneswere found in CSG patients with PQDS (Figure 1(a)). .eywere enriched in the KEGG/Panther pathways includingcomplement and coagulation cascades; ether lipid meta-bolism; and pathways involved in cell-cell junction/adhesionand communication such as cadherin signaling, extracellular

matrix- (ECM-) receptor interaction, protein digestion andabsorption, integrin signaling, and cholinergic synapsepathway (Figure 4(a)). Furthermore, the enriched Reactomepathways contained the immune-related pathways impli-cated in complement cascades and regulation of comple-ment cascades and immunoregulatory interactions betweena lymphoid and a nonlymphoid cell. Also, in particular,multiple enriched Reactome pathways were associated withcell-cell interactions such as adhesion, junction, and com-munication (integrin cell surface interactions, extracellularmatrix organization, nonintegrin membrane-ECM interac-tions, degradation of the extracellular matrix, collagenbiosynthesis and modifying enzymes, collagen formation/degradation, assembly of collagen fibrils and other multi-meric structures, ECM proteoglycans, syndecan interac-tions, tight junction interactions, NCAM1 interactions andsignaling) (Figure 4(b)). In particular, domain and featureenrichment analysis showed that these PQDS-specific pro-teins generally contained domains and features such asimmunoglobulin-like fold, collagen triple helix repeat,cadherin N-terminal or C-terminal domain, and comple-ment system and component domain (Figure 4(d)). Inaddition, the disease enrichment analysis indicated thatthese PQDS genes were associated with the diseases relatedto complement regulatory protein defects, immune systemdiseases, and primary immunodeficiency.

.e interaction network (Figure 4(e)) not only shows thepossible interactions and expression pattern of PQDS-spe-cific genes (Supplemental Table S10), but also indicates thenumber of possible trans/cis-acting lncRNAs directly tar-geting to node genes (Supplemental Tables S11 and 12).Obviously, almost each gene has the corresponding trans-acting lncRNAs, and the genes CLEC4C and LOC100130520(CD300H) have additional cis-acting lncRNAs. C4BPB, animportant negative factor of complement activation, wasregulated by over 30 trans-acting lncRNAs in the comple-ment and coagulation cascades pathway. COL2A1 wasregulated by 25 trans-acting lncRNAs; it is an importantgene responsible for cross talk of two pathways involved incell-cell adhesion and communication such as extracellularmatrix- (ECM-) receptor interaction pathway and proteindigestion and absorption pathway. Also, GNAO1 andPCDHGC5 were governed by multiple trans-actinglncRNAs, which were matched in the other two pathwaysrelated to cell-cell adhesion and communication, namely,cholinergic synapse pathway and cadherin signaling path-way (Figure 4(e)). Together, these results suggested that thePQDS-specific lncRNAs seemed to be crucial in the regu-lation of expression profile of PQDS-specific genes, espe-cially the expression regulation of genes in multiplepathways associated with complement and coagulationcascades as well as cell-cell adhesion/junction and com-munication, which contributed to the characteristics andfunctions of leukocytes in CSG patients of PQDS.

3.6. lncRNA-Gene Regulation Pairs. Based on the results oftarget gene prediction of lncRNAs, we drew Venn diagramsto show the numbers of differential lncRNAs and genes

8 Evidence-Based Complementary and Alternative Medicine

identified in the leukocytes of individuals from the two casepopulations, adding lists of differential genes for both casepopulations to display and compare the top 30 genes whichhave corresponding trans- or cis-acting lncRNAs

(Figure 5(a)). In the gene lists, for case population 1 (QDC),the black font denotes the number of QDC-specific lncRNAsregulating the QDC genes, and the blue font shows thenumber of common lncRNAs targeting the QDC genes.

Rich factor

KEGG/Panther FDR0.040.030.02

0.02 0.03 0.04 0.05 0.06

0.01

345

Gene count

Path

way

term

Protein digestion and absorptionPertussis

Oxytocin signaling pathwayIntegrin signalling pathway

Ether lipid metabolismECM-receptor interaction

Complement and coagulation cascadesCholinergic synapse

Cadherin signaling pathway

(a)

Tight junction interactionsSyndecan interactions

Regulation of Complement cascadeO-glycosylation of TSR domain-containing proteins

Non-integrin membrane-ECM interactionsNCAM1 interactions

NCAM signaling for neurite out-growthIntegrin cell surface interactions

Innate Immune SystemInitial triggering of complement

Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cellImmune system

Extracellular matrix organizationECM proteoglycans

Developmental biologyDegradation of the extracellular matrix

Defective B3GALTL causes Peters-plus syndrome (PpS)Complement cascade

Collagen formationCollagen degradation

Collagen biosynthesis and modifying enzymes

Assembly of collagen fibrils and other multimeric structuresAxon guidance

Activation of C3 and C5

ReactomeFDR

Gene count

2

0.03

0.02

0.01

4681012

Path

way

term

Rich factor0.00 0.05 0.10 0.15 0.20 0.25

(b)

Rich factor

FDR0.08

3

0.1 0.2 0.3

34

0.060.040.02

Gene count

DiseaseOMIM

KEGG DISEASENHGRI GWAS Catalog

Dise

ase t

erm

Schizophrenia or bipolar disorder

Metaphyseal dysplasiasImmune system diseases

Complement regulatory protein defectsChromium levels

Avascular necrosis of femoral headAllergies and autoimmune diseasesAge-related macular degeneration

Primary immunodeficiency

(c)

Tissue inhibitor of metalloproteinases-like, OB-foldTerpenoid cyclases/protein prenyltransferase

Somatomedin B, chordataSomatomedin B domain

Somatomedin B-like domain superfamilyNetrin module, non-TIMP type

Netrin domainMacroglobulin domain

Immunoglobulin-like foldHemopexin, conserved site

Hemopexin-like repeatsHemopexin-like domain superfamily

Hemopexin-like domainFibrillar collagen, C-terminal

Complement component C4-ACollagen triple helix repeat

Cadherin, N-terminalCadherin, cytoplasmic C-terminal domain

Cadherin, C-terminal catenin-binding domainAnaphylatoxin/fibulin

Anaphylatoxin, complement system domainAnaphylatoxin, complement systemAlpha-macroglobulin, TED domain

Alpha-macroglobulin, receptor-binding domain superfamilyAlpha-macroglobulin, receptor-bindingAlpha-2-macroglobulin, conserved site

Alpha-2-macroglobulin, bait region domainAlpha-2-macroglobulin

Rich factor

FDR0.040

2

4

6

8

10

0.00 0.25 0.50 0.75 1.00

0.0350.0300.025

Gene count

Interpro domains

Dom

ains

and

feat

ures

term

(d)

Complement and coagulation cascades pathway

ECM-receptor interaction pathway

2

333

2

1

1

25

9

7

2

2

150 1

9 4

1

2

2 2

0

1

265

Protein digestion and absorption pathway

Cholinergic synapsepathway

3

6 3 0 Line thickness indicates the interaction strength of data support. �e active interaction sources include Text mining, Experiments Databases and Co-experiment.

Meaning of network nodes:

Meaning of network edges:

Gene-targetted lncRNA count

�e upregulated gene. Shade of red depends on the degree of up-regulation of gene expression.

�e downregulated gene. Shade of green relies on the degree of down-regulation of gene expression.

Gene upregulated or downregulatedCadherin signalingpathway

Ether lipid metabolism pathway

2/2

0/1

Circle size represents the count of lncRNAs targeting the corresponding gene. Also, the count is shown on the right, detailed in format of “trans binding lncRNAs/cis regulation lncRNAs”.

207/2

1

0

2

2

8

(CD300H)

(e)

Figure 4: Enrichment and interaction network analysis for the Pi-qi-deficiency syndrome- (PQDS-) specific genes. (a) Enriched KEGG andPanther pathways of PQDS-specific genes. (b) Enriched Reactome pathways of PQDS-specific genes. (c) Enriched diseases associated withPQDS-specific genes. (d) Enriched domains and features of the proteins coded by PQDS-specific genes. (e) Network to detail the in-teractions among PQDS genes. .e number near a node denotes the number of PQDS-lncRNAs targeting the corresponding node gene.

Evidence-Based Complementary and Alternative Medicine 9

Similarly, regarding case population 2 (CSG patients withPQDS), the black font denotes the number of PQDS-specificlncRNAs controlling the PQDS genes, and the blue fontshows the number of common lncRNAs targeting the PQDSgenes. For the listed top 30 genes in each case population,each gene was regulated by many lncRNAs. Interestingly,five (bold font) of them, COL27A1, ADAMTSL5, MSH5,COL26A1, and LOC390937, belonged to the common dif-ferential genes found in both case populations (Figure 5(a)),indicating that the common genes seemed to be undergoingtight and complex regulation of more lncRNAs due to theirpotential roles in linking QDC to PQDS.

.us, in order to detail the interactions and regulationbetween the differential lncRNAs and all the 12 commongenes (Supplemental Table S13), an interaction network wascarefully created (Figure 5(b)). .e 12 common genes andtheir corresponding regulation lncRNAs were shown, in-cluding the QDC-specific lncRNAs (grey nodes), PQDS-specific lncRNAs (brown nodes), and the common lncRNAs(blue nodes) identified in both QDC and PQDS. Also, thenumbers of trans- and cis-acting lncRNAs targeting eachcommon gene were carefully presented (Figure 5(b)). Asindicated, most common genes underwent very tight

regulation of many lncRNAs, not only the specific lncRNAsin QDC or PQDS but also the common lncRNAs in bothQDC and PQDS. In particular, the genes ADAMTSL5,COL26A1, MSH5, LOC390937, and COL27A1 could beregulated by the QDC-specific lncRNAs, PQDS-specificlncRNAs, and the common lncRNAs. Overall, these resultsshow that lncRNAs play important roles in maintenance andregulation of gene expression profiles which determine thecharacteristics and functions of leukocytes in QDC andPQDS populations. Notably, the common differential genesundergo so tight and complex regulation of lncRNAs in-cluding the common lncRNAs, which may be due to theirpotential roles in linking QDC to PQDS.

3.7. De Common lncRNAs-Mediated Regulation of CommonGenes. lncRNAmay directly bind to mRNAs, mediating theposttranscriptional regulation of mRNAs. Also, if possible, italso binds to the mRNA-coded protein to affect proteinfunctions. Herein, in order to decode the common differ-ential lncRNAs-mediated regulation of the common genesand their coded proteins, we first carefully analyzed theRNA-binding ability between the common lncRNAs (total

COL27A1CNOT3

SYT15SHANK1

ADAMTSL5KCNT1LENG8

SPEGCACNA1A

TMC8OBSCNSLC9A1

GRIK4MSH5

LOC390937TAF4FOSB

LOC105376684VSTM1SLC7A8

SLX1BCLDN9

COL26A1AR

NYXMYADM

TEAD2KLHL33

RCN3PVRL2

11111491908483787575676972666563686663546355555559535450474945

3025231922182817101813101415159

101221101513137

128

10129

12

ADAMTSL5GNAO1C4BPBCOL26A1COL2A1LRFN3LILRB3BRSK1DPYSL3COL5A3PCDHGC5SYNGAP1MSH5NAPRTPLEKHN1COL27A1TRIM73ADAM12ADAMTS2DHRS2HOXA3LOC105371430LOC390937CLDN7CPLX2CRB3NELFEPTPRBB3GALNT2LOC105376526

8252332425201612129898

107876757556435543

1773754444232203111020220120001

Case 2Case 1

Case 2Case 1

638 540111lncRNAs

172 8112Genes

Target genes (top 30)

(a)

ADAMTSL5205

AD

AM

TSL5

LOC1

0537

1430

LOC1

0537

6526

SLC4

A10

CORI

N

COL26A1101

LOC39093785

OR1J219

LOC10537652611 MSH5

90

LOC10537143029

COL27A1150

ZFP5748

MATN211

CORIN2

Case 1Case 2

COL2

6A1

13/557/24

LOC3

9093

7

15/632/5

OR1

J2

4/150/0

MSH

5

15/652/8

COL2

7A1

30/1111/8

ZFP5

7

8/380/2

7/152/5

1/61/3

0/20/0

0/10/1

MAT

N2

0/81/2

22/8417/82

Genes

LncRNAs Observed only in case 2Observed only in case 1

Observed both in cases 1 and 2

Observed both in cases 1 and 2

2SLC4A10

(b)

Figure 5: Overview of differential genes undergoing regulation of differential lncRNAs in each case population. (a) Top 30 targeting genesregulated by different differential lncRNAs in each case population. Venn diagrams denote the numbers of differential lncRNAs and genesfound in each case population. 111 lncRNAs and 12 genes were common in both case populations. (b) Interaction network to detail thecommon genes and their possible regulating lncRNAs. Node size depends on the number of edges, and the number of regulating lncRNAs isshown near the corresponding node. .e numbers of lncRNAs targeting each common gene were carefully presented in format of “numberof the common regulating lncRNAs (blue)/number of the case-specific lncRNAs identified in case 1 (grey) or case 2 (brown).” Case 1: qi-deficiency constitution; case 2: Pi-qi-deficiency syndrome of chronic superficial gastritis.

10 Evidence-Based Complementary and Alternative Medicine

111) and genes (total 12). A total of 70 lncRNAs possiblybind to the genes’ (total 11) corresponding mRNAs in bothcase populations (Supplemental Table S14), and an inter-action network was drawn to detail the binding pairs oflncRNAs and genes (Figure 6(a)). Besides, the HCL heatmapwas generated to review the expression patterns of all the 70lncRNAs (Figure 6(b)). .e upregulated lncRNAs and thedownregulated lncRNAs identified in both case populationswere specifically labeled in red or green font. Also, wepredicted the subcellular location for all the 70 lncRNAs inthe network. As indicated (Figure 6(b)), four lncRNAsmightbe located in nucleus of leukocyte, two lncRNAs could beribosome-related, and 62 lncRNAs were distributed in cy-tosol or cytoplasm. Notably, two lncRNAs, lnc-FAM32A-2:1and lnc-MDK-4:2, could be encapsulated in exosomes whichwere capable of transferring them to other recipient cells allover the body.

Moreover, to explore the common differential lncRNAs-mediated regulation of the common genes-correspondingproteins, we evaluated the RNA-protein interactions betweenthe common lncRNAs and genes-corresponding proteinsunder the prerequisite of coexpression of a lncRNA and a gene.A total of 12 lncRNAs were capable of directly binding to 6genes-coded proteins in both case populations (SupplementalTable S15). .e resultant network was shown to detail theRNA-protein interactions between the lncRNAs and proteins(Figure 6(c)). .e upregulated lncRNAs (red font) and thedownregulated lncRNAs (green font) identified in the two casepopulations were labeled, respectively.

.ese results indicated that the common lncRNAs seemed tobe very important posttranscriptional regulators, controlling thecommon genes by direct binding to either the transcribedmRNAs or the translated proteins. .e common genes under-went so complex regulation of the common lncRNAs, suggestingonce again their possible potential roles in linkingQDC to PQDSof CSG. .erefore, we performed another interaction networkanalysis of the common genes, and the obtained interactionnetwork was drawn and annotated (Figure 6(d)). .e functionalenrichment in the generated network includingGO and pathwaywas detailed (Supplemental Table S16 to 20). .e enrichmentanalyses results showed that theymainly belonged to intracellularorganelle lumen and extracellular matrix component includingcollagen-containing extracellular matrix and were involved inpathways such as collagen chain trimerization and collagenbiosynthesis and modifying enzymes. In particular, relying onfunctional partners of CORIN and MSH5, several additionalpathways were involved, including mismatch repair pathway,meiotic recombination pathway, and Fanconi anemia pathway(Figure 6(d)). .e obtained results demonstrate the commonchanges in the extracellular matrix related to cell-cell adhesion/junction and communication, which contribute to alteration incharacteristics and functions of leukocytes of individuals in bothcase populations and may be involved in the linkage betweenQDC and PQDS.

3.8. Functions of the Exosome-Contained lnc-MDK-4:2 andlnc-FAM32A-2:1. .ementioned lncRNAs, lnc-FAM32A-2:1 and lnc-MDK-4:2, were predicted to be encapsulated in

exosomes, especially keeping higher transcript levels in bothcase populations (Figure 7(b)). .e exosomes might transferthe lncRNAs to other far away recipient cells, making themfunction all over the body. .us, in order to analyze theirpotential roles, we predicted their possible RBPs (Supple-mental Table S21), based on the well-known RBP databasethat collects the experimental observations of RNA-bindingsites, both in vitro and in vivo. As shown (Figure 7(a)), theinteraction network was drawn to detail RNA-protein in-teractions between the lncRNAs and their RBPs (edgethickness indicates interaction strength of RNA-bindingsites support). Furthermore, the enrichment analysis of theobtained RBPs revealed their potential pathways related toRNA processing and transport, IL17 signaling, biosynthesisand metabolism, and regulation of autophagy initiation(Figure 7(c)). Also, the interaction network was created todetail the interactions between multiple RBPs, includingtheir physical and functional association (Figure 7(d))..eseresults demonstrated that the exosomes-contained lnc-MDK-4:2 and lnc-FAM32A-2:1 might play potential im-portant roles in regulating biosynthesis and metabolism inthe recipient cells all over the body, especially the IL17-mediated signaling and autophagy initiation regulationwhich contributed to host defense and pathogenesis ofvarious autoimmune diseases.

4. Discussion

TCMwas developed through thousands of years of empiricaltesting and refinement. Nowadays, it is getting more fre-quently adopted in countries in the west [1]. Syndrome, athousand-year-old key therapeutic concept in TCM, is de-fined as a pattern of symptoms and signs in a patient at aspecific stage during the course of a disease [35, 36]. Personswith QDC seem to have a tendency toward PQDS, one of thecommonly matched TCM syndromes in CSG patients [6–8].In particular, they have common features including lackingvitality (Qi) and getting sick easily, being generally char-acterized by languid lazy words, low voice, pale tongue, andweak pulse [4, 8]. .at is to say, there seemed to be a linkagebetween QDC and PQDS, indicating a decrease in immunityin both QDC and PQDS. Leukocytes, as important immunecells throughout the body, play crucial roles in host defenseagainst infection and contribute to pathogenesis of variousautoimmune diseases. .erefore, the alternations in char-acteristics and functions of leukocytes may implicate linkingQDC to PQDS. Because gene expression profile determinescell characteristics and functions, the similar gene expressionprofiles observed in the leukocytes from QDC and PQDSpopulations (Figure 1) suggested several similar alterationsin characteristics and functions of leukocytes. Functionenrichment analyses of differential genes just showed thatcommon biological processes, including extracellular matrixorganization and cell adhesion via plasma membrane ad-hesion molecules, were involved in both QDC and PQDS,indicating the possible alternations in cell-cell adhesion/junction and communication which contributed to char-acteristics and functions of leukocytes all over the body(Figure 2(a)).

Evidence-Based Complementary and Alternative Medicine 11

CORIN

LOC390937

ZFP57

MSH5

MATN2

LOC105371430 COL27A1

COL26A1

ADAMTSL558

568

39

17

4857

3721

27

SLC4A10

LOC105376526

GAS5:41

lnc-IGFBP7-1:2

GAS5:31DLX6-AS1:13CDC42-IT1:1TCONS_00052867∗

TCONS_00052862∗TCONS_00052318∗

TCONS_00049110∗TCONS_00049093∗

TCONS_00048675∗TCONS_00048674∗

TCONS_00048671∗TCONS_00048609∗

TCL6:19PSMD5-AS1:3

PITPNA-AS1:2

lnc-KIAA0226-4:1

lnc-LRRFIP2-1:1lnc-KLB-2:1

lnc-MDK-4:2lnc-MPPE1-5:1

lnc-NPBWR1-1:2lnc-PGPEP1L-3:1lnc-PLEKHA7-2:1lnc-PLEKHG2-1:1

lnc-PRPF4B-4:4lnc-RAB23-17:1

lnc-RNF19A-8:4lnc-RP11-108K14.4.1-1:2

lnc-RP11-706O15.1.1-2:8lnc-RPL10L-2:1

lnc-RPRML-3:20lnc-SWI5-1:1lnc-THAP10-1:1 PAN3-AS1:5

lnc-ZNF91-4:2

lnc-ZNF212-2:2

lnc-ZNF114-2:1

lnc-TWIST1-1:3

lnc-VASH

2-1:1lnc-W

DR7-6:2

lnc-ZFAN

D4-1:1

lnc-ZMAT5-4:3

lnc-HYOU1-1:3lnc-HIVEP3-1:1

lnc-HIST1H2AI-1:3lnc-HES5-1:7lnc-GGCT-1:12

lnc-FBXL2-4:1lnc-FAM96A-1:1

lnc-FAM32A-2:1 lnc-EGLN

1-1:3

LINC00152:10LINC00299:3

LINC01237:6lnc-AC103810.1-1:2

lnc-AC233264.2-1:1lnc-AKAP11-1:2

lnc-AMZ2-5:1lnc-ARAF-1:1

lnc-ARHGAP27-2:1lnc-ARL1-3:1lnc-D

YDC1-1:1

lnc-CSNK1D

-1:1lnc-CEP170-11:1lnc-CELF6-1:5lnc-CACYBP-2:1lnc-CA

10-1:1lnc-C12orf50-6:2

1 4 6 4 6 44

79

611

119

9688451

12

25

24

38

645135371

3533

510

76

11

62

7516778

31

15

35

52 3

3 4 36

1

(a)

CDC42-IT1:1LINC00299:3PSMD5-AS1:3PAN3-AS1:5lnc-IGFBP7-1:2LINC00152:10lnc-RNF19A-8:4PITPNA-AS1:2lnc-ARAF-1:1TCL6:19lnc-PLEKHG2-1:1GAS5:31TCONS_00049093lnc-AC233264.2-1:1GAS5:41lnc-KLB-2:1lnc-CELF6-1:5lnc-RP11-108K14.4.1-1:2TCONS_00048674TCONS_00049110TCONS_00048675lnc-SWI5-1:1LINC01237:6lnc-AMZ2-5:1lnc-MDK-4:2lnc-ARL1-3:1lnc-CSNK1D-1:1lnc-PGPEP1L-3:1lnc-PLEKHA7-2:1TCONS_00052867lnc-LRRFIP2-1:1lnc-VASH2-1:1lnc-CACYBP-2:1lnc-HES5-1:7lnc-RP11-706O15.1.1-2:8lnc-GGCT-1:12lnc-DYDC1-1:1lnc-ZNF114-2:1lnc-EGLN1-1:3lnc-HIST1H2AI-1:3lnc-PRPF4B-4:4lnc-WDR7-6:2lnc-AKAP11-1:2lnc-C12orf50-6:2lnc-HIVEP3-1:1lnc-RPRML-3:20TCONS_00048609lnc-CA10-1:1lnc-ZNF212-2:2lnc-NPBWR1-1:2lnc-FAM96A-1:1DLX6-AS1:13TCONS_00052318lnc-FBXL2-4:1TCONS_00048671TCONS_00052862lnc-ZNF91-4:2lnc-MPPE1-5:1lnc-ZFAND4-1:1lnc-TWIST1-1:3lnc-FAM32A-2:1lnc-CEP170-11:1lnc-ZMAT5-4:3lnc-ARHGAP27-2:1lnc-HYOU1-1:3lnc-RPL10L-2:1lnc-RAB23-17:1lnc-AC103810.1-1:2lnc-KIAA0226-4:1lnc-THAP10-1:1

Case 2(n = 5)

Case 1(n = 2)

Control(n = 5)

log

2 (fo

ld ch

ange

)

0–1–2–3

321

Cyto

sol/C

ytop

lasm

Ribo

som

eN

ucle

us

Exos

ome

Subcellular locationExpression clustering

(b)

6

Inc-EID2B-1:1Inc-C12orf50-6:2

Inc-PLEKHA7-2:1Inc-PGPEP1L-3:1

Inc-VASH2-1:1

Inc-LRRFIP2-1:1

Inc-RPRML-3:20

EDNRB-AS 1:2

Inc-CEP170-11:1

Inc-ZNF114-2:1

Inc-RAB23-17:1

Inc-BTK-1:2

COL26A1

COL27A1

MSH5

ADAMTSL5

LOC390937

MATN2

11

11

2

2

1

1

1

2

1

1

2

2

1

1

3

(c)

Mismatch repair pathway

Collagen biosynthesisand modifying enzymes pathway

Meiotic recombination pathway

Fanconi anemia pathway

Collagen chain trimerization pathway

SLC4A10MLH3

MLH1

RAD51

ZFP57

ADAMTSL5

MATN2

CORIN

SERPINH1

COL26A1COL27A1

Node content

Empty nodes:proteins of unknown 3D structure

Filled nodes:some 3D structure is known or predicted

OthersText mining

Co-expression

Protein-homology

Predicted interactions

Gene neighborhood

Gene fusions

Gene co-occurrence

Known interactions

From curated databases

Experimentally determined

(d)

Figure 6:.e common differential lncRNAs-mediated regulation of the common differential genes in two case populations. (a) Network todetail the possible direct binding of lncRNAs to the mRNAs of common genes. 70 lncRNAs could directly bind to the mRNAs of 11 genes.Nodes with red font labels indicate a consistent upregulation expression in both case populations. Nodes with green font denote a consistentdownregulation expression in both case populations. Node size depends on the number of edges, and the number is shown near thecorresponding node..e “∗”-labeled lncRNAs were novel in this work. (b) Expression clustering and subcellular location analyses for the 70common lncRNAs capable of directly binding to the mRNAs of common genes. Prediction of subcellular location involves exosome,ribosome, nucleus, cytosol, and cytoplasm. Red or green labels indicate that the corresponding lncRNA is consistent upregulation ordownregulation expression in both case populations. (c) Network to detail the possible interactions between the common lncRNAs and thecommon genes-coded proteins. 17 lncRNAs could directly bind to 7 proteins. Node size depends on the number of edges, and the number isshown near the corresponding node. (d) Interaction network analysis of the common genes.

12 Evidence-Based Complementary and Alternative Medicine

For QDC-specific genes, their enriched pathways(Figures 3(a) and 3(b)) included cell cycle regulationpathways, transmembrane transport pathways, and espe-cially immune pathways related to cell adhesion and sig-naling. In particular, they were enriched in proteins that

contained immune-related domains (Figure 3(d)). Notably,multiple QDC-specific lncRNAs targeted genes (coding forimmune domains-contained proteins) in the pathways as-sociated with cell-cell adhesion and communication(Figure 3(e)), including the cell adhesion molecules

(a)

Case 1(n = 2)

Control(n = 5)

Case 2(n = 5)

Case 1(n = 2)

Control(n = 5)

Case 2(n = 5)

lnc-FAM32A-2:1lnc-MDK-4:2

FPKM

FPKM

P = 0.039P = 0.029

P = 0.021P = 0.001

0.0

7.5

5.0

2.5

4

2

0

(b)

PI3K-Akt signaling pathwayProteoglycans in cancer

Transcriptional misregulation in cancermTOR signaling pathwayAMPK signaling pathway

Carbon metabolismHerpes simplex infection

mRNA surveillance pathwayRNA degradation

Biosynthesis of amino acidsGlyoxylate and dicarboxylate metabolism

Citrate cycle (TCA cycle)2-Oxocarboxylic acid metabolism

RNA transportIL-17 signaling pathway

Spliceosome

KEGG pathway term

SRSF

10RB

MX

SRSF

1SN

RPA

SRSF

9EL

AVL1

EIF4

BPA

BPC1

ACO

1FU

S

3.56e-080.0251 0.0094 0.0221 0.0319 0.0305 0.0609 0.0620 0.0702 0.0477 0.0825 0.0882 0.1032 0.1152 0.12710.1865

FDR

(c)

Herpes simplex infectionpathway

IL17 signaling pathway

Spliceosome pathway

RNA transport pathway

Node contentempty nodes:proteins of unknown 3D structure

filled nodes:some 3D structure is known or predicted

Action types

Activation

Phenotype

Blinding

Reaction

Inhibition

Catalysis

Posttranslational modification

Transcriptional regulation Unspecificed

Negative

Positive

Action effects

(d)

Figure 7: Potential functions of lnc-MDK-4:2 and lnc-FAM32A-2:1 contained in leukocytes-derived exosome. (a) Network to detail RNA-binding proteins (RBPs) of lnc-MDK-4:2 and lnc-FAM32A-2:1. Node size depends on the number of edges. (b) Expression patterns of lnc-MDK-4:2 and lnc-FAM32A-2:1. (c) Pathway enrichment analysis of the RBPs of lnc-MDK-4:2 and lnc-FAM32A-2:1. (d) Interactionnetwork analysis for the RBPs of lnc-MDK-4:2 and lnc-FAM32A-2:1. Control: balanced constitution; case 1: qi-deficiency constitution; case2: Pi-qi-deficiency syndrome of chronic superficial gastritis.

Evidence-Based Complementary and Alternative Medicine 13

(PTPRM, CLDN9) and immunoregulatory interactionsbetween a lymphoid and a nonlymphoid cell (KLRB1,KLRC, KLRG1). PQDS-specific genes were enriched inpathways including complement and coagulation cascades;ether lipid metabolism; and pathways involved in cell-celljunction/adhesion and communication (Figures 4(a) and4(b)). In particular, C4BPB, a negative factor of complementactivation, was regulated by over 30 lncRNAs in the com-plement and coagulation cascades pathway. COL2A1, animportant gene responsible for cross talk of two pathwaysinvolved in cell-cell adhesion and communication, wasregulated by 25 lncRNAs. GNAO1 and PCDHGC5 weregoverned by multiple lncRNAs, which were in the pathwaysrelated to cell-cell adhesion and communication(Figure 4(e)). All the results indicated that QDC- or PQDS-specific lncRNAs played very important roles in regulatingthe QDC- or PQDS-specific gene expression which deter-mined characteristics and functions of leukocytes in dif-ferent case populations.

Regarding the common differential genes appearing inboth QDC and PQDS, most of them underwent very tightregulation of many lncRNAs. In particular, the genesADAMTSL5, COL26A1, MSH5, LOC390937, andCOL27A1 could be regulated by the QDC-specificlncRNAs, the PQDS-specific lncRNAs, and the commonlncRNAs (Figures 5(b) and 6(a)). A total of 12 lncRNAswere capable of directly binding to 6 genes-coded proteinsin both case populations (Figure 6(c); SupplementalFigures S4 and S5). .ese results indicated that thecommon lncRNAs seemed to be very important post-transcriptional regulators, controlling the common genesby direct binding to either the transcribed mRNAs or thetranslated proteins. .e common genes underwent socomplex regulation of common lncRNAs, suggesting theirpotential roles in linking QDC to PQDS. In particular, thecommon changes in the extracellular matrix and integralcomponents of plasma membrane, which are related tocell-cell adhesion/junction and communication, may beinvolved in the linkage between QDC and PQDS, con-tributing to the alterations in characteristics and functionsof leukocytes of individuals in both case populations.

In addition, the high expression of the exosomes-carriedlnc-MDK-4:2 and lnc-FAM32A-2:1 (Figures 6(b) and 7) inboth case populations probably implicates linking QDC toPQDS, because of their potential roles in regulating bio-synthesis and metabolism in the recipient cells all over thebody, especially the IL17-mediated signaling and autophagyinitiation which contributed to host defense and patho-genesis of various autoimmune diseases.

.e estimation of lncRNA from RNA sequencingenabled the identification of both lncRNAs and mRNAsonly by constructing an RNA-seq library, but such anapproach could underestimate the possible lncRNAs,especially the novel and unannotated lncRNAs. .etranscriptomic analyses of blood leukocytes revealed thelncRNA-mediated complex regulation of candidates im-plicated in the possible link between QDC and PQDS ofCSG. However, the study population was not large enoughto draw definitive conclusions; in particular, only two

female individuals were included in case population 1.Also, there were significant differences in age betweenCSG and health control groups, which may lead to biasedconclusions. Hence, further studies involving largersample sizes are required to strengthen the conclusions ofthis study. Additionally, the PCR validation of the ex-pression of the common differential lncRNAs and genesshould be performed in larger populations.

5. Conclusions

A total of 12 common differential genes, obtained in QDC andPQDS, could be regulated by the QDC-specific lncRNAs, thePQDS-specific lncRNAs, or the common lncRNAs. Notably,some of them underwent very tight and complex regulation ofthe common differential lncRNAs by directly binding thetranscribed mRNAs and the translated proteins. Severalcommon biological processes, including extracellular matrixorganization and cell adhesion via plasma membrane adhesionmolecules, were implicated in both QDC and PQDS, therebyindicating the possible alternations in cell-cell adhesion/junction and communication, which contributed to charac-teristics and functions of leukocytes all over the body. .eseresults may provide new insights into the characteristic andfunctional changes of leukocytes in QDC and PQDS, especiallythe mechanism underlying the linkage of QDC to PQDS, withpotential leukocytes biomarkers for future application in in-tegrative medicine.

Data Availability

All sequence data have been deposited in GenBank underBioProject accession number PRJNA591186 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA591186). .e RNA-seqreads have been deposited in the NCBI Sequence ReadArchive (SRA) (http://www.ncbi.nlm.nih.gov/sra) underaccession numbers SRR10513209, SRR10513208,SRR10513204, SRR10513203, SRR10513202, SRR10513205,SRR10513201, SRR10513200, SRR10513199, SRR10513198,SRR10513207, and SRR10513206.

Ethical Approval

.e study was registered at ClinicalTrials.gov (identifier:NCT02915393). .e protocol was approved (JDF-IRB-2016031002) by the Institutional Review Board of DongfangHospital affiliated to Beijing University of ChineseMedicine.All the methods were performed in accordance with therelevant guidelines and regulations.

Consent

Participants were informed of the purpose, general contents,and data use of the study, and they all signed the informedconsent.

Conflicts of Interest

.e authors declare that they have no conflicts of interestregarding the publication of this paper.

14 Evidence-Based Complementary and Alternative Medicine

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA591186https://www.ncbi.nlm.nih.gov/bioproject/PRJNA591186http://www.ncbi.nlm.nih.gov/srahttp://ClinicalTrials.govhttps://clinicaltrials.gov/ct2/show/NCT02915393

Authors’ Contributions

Anlong Xu conceived the study. Leiming You, Xinhui Gao,Xiaopu Sang, and Anlong Xu designed the research. AijieLiu, Xiaopu Sang, Xinhui Gao, Honghao Sheng, Tingan Li,Kunyu Li, and Shen Zhang performed the experiments. AijieLiu, Leiming You, Xiaopu Sang, Guangrui Huang, and TingWang analyzed the data. Leiming You and Aijie Liu drew thefigures and wrote the paper. Anlong Xu wrote and edited thepaper. All authors read and approved the final manuscript.Leiming You and Aijie Liu contributed equally to this work.

Acknowledgments

.is study was supported by the National Natural ScienceFoundation of China (grant nos. 81430099 and 91231206 toA.X.).

Supplementary Materials

Supplemental Methods: the inclusion and exclusion criteria forsubjects and the experimental design and route. Table S1: listfor leukocyte samples of the clinical subjects. Table S2: statisticalresults of the alignment of the cleaned raw reads to the ref-erence genome. Table S3: differential genes identified in theQDC population compared with the BC control population.Table S4: differential genes identified in the PQDS populationcomparedwith the BC control population. Table S5: differentiallncRNAs identified in the QDC population compared with theBC control population. Table S6: differential lncRNAs iden-tified in the PQDS population compared with the BC controlpopulation. Table S7: interactions between QDC-specificgenes-coded proteins. Table S8: the QDC-specific differentialgenes regulated by QDC-specific differential trans-actinglncRNAs order by genes. Table S9: detailed cis-regulation pairsbetween the QDC-specific differential genes and lncRNAs inQDC population. Table S10: interactions between PQDS-specific genes-coded proteins. Table S11: the PQDS-specificdifferential genes regulated by the PQDS-specific differentiallncRNAs order by genes. Table S12: detailed cis-regulation pairsbetween the PQDC-specific differential genes and lncRNAs inPQDS population. Table S13: detailed list for binding pairs ofthe differential lncRNAs and the 12 common differential genes.Table S14: the possible lncRNA-mRNA binding among thecommon differential lncRNAs and genes. Table S15: thelncRNA-protein interactions between the common differentiallncRNAs and genes-coded proteins. Table S16: the 17 biologicalprocess (GO) terms enriched with the common targets and thepredicted functional partners. Table S17: the 11 cellularcomponent (GO) terms enriched with the common targets andthe predicted functional partners. Table S18: the 11 molecularfunction (GO) terms enriched with the common targets andthe predicted functional partners. Table S19: reactome path-ways significantly enriched with the common targets and thepredicted functional partners. Table S20: KEGG pathwayssignificantly enriched with the common targets and the pre-dicted functional partners. Table S21: the RNA-binding pro-teins (RBPs) and binding motifs of lnc-FAM32A-2:1 and lnc-MDK-4:2. Figure S2: comparison of GO function enrichments

identified by the GSEAmethod in two case populations. FigureS3: comparison of pathway enrichments identified by GSEAmethod in two case populations. Figure S4: mismatch repairpathway (hsadd03430). Figure S5: Fanconi anemia pathway(hsadd03460). (Supplementary Materials)

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16 Evidence-Based Complementary and Alternative Medicine