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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;
[email protected]
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
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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
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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
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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
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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
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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
2
0002
33
211
13
00
–1.3
0
–2.3
log(P
valu
e)
(b)
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
log(P
valu
e)
(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
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(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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