Abstract

   Water buffalo are less susceptible to Schistosoma japonicum infection
   than yellow cattle. The factors that affect such differences in
   susceptibility remain unknown. A Bos taurus genome-wide gene chip was
   used to analyze gene expression profiles in the peripheral blood of
   water buffalo and yellow cattle pre- and post-infection with S.
   japonicum. This study showed that most of the identified differentially
   expressed genes(DEGs) between water buffalo and yellow cattle pre- and
   post-infection were involved in immune-related processes, and the
   expression level of immune genes was lower in water buffalo. The unique
   DEGs (390) in yellow cattle were mainly associated with inflammation
   pathways, while the unique DEGs (2,114) in water buffalo were mainly
   associated with immune-related factors. The 83 common DEGs may be the
   essential response genes during S. japonicum infection, the highest two
   gene ontology (GO) functions were associated with the regulation of
   fibrinolysis. The pathway enrichment analysis showed that the DEGs
   constituted similar immune-related pathways pre- and post-infection
   between the two hosts. This first analysis of the transcriptional
   profiles of natural hosts has enabled us to gain new insights into the
   mechanisms that govern their susceptibility or resistance to S.
   japonicum infections.

Introduction

   Schistosomiasis remains an important global public health problem that
   affects 200 million people in 76 countries. Schistosomiasis control in
   China has been remarkably successful, with the number of cases being
   reduced from 11,000,000 to 286,836 by the end of 2011 [[49]1]. Endemic
   areas of uncontrolled schistosomiasis in China are mostly distributed
   in the marsh, lake, and mountainous regions [[50]2,[51]3]. Studies have
   shown that bovine infection is one of the main threats for human
   infection [[52]4,[53]5]. Water buffalo(Bubalus bubalis) and yellow
   cattle(Bos indicus) are considered to be the main transmission sources
   for schistosomiasis in China [[54]6,[55]7].

   Schistosoma japonicum has a wide range of host species, and at least 46
   species of mammals, other than humans, are known to be naturally
   infected by S. japonicum. The degree of susceptibility to S.japonicum
   has been previously demonstrated to vary among several host species
   [[56]8]. In addition, host self-curing and parasite clearance have been
   observed in water buffalo and pigs after a certain period of infection
   [[57]9,[58]10]. Parasites that survive in such less-susceptible hosts
   also showed substantial changes in morphology, being shorter in length,
   having poorly developed gonads, and demonstrating a lower rate of worm
   pairing and spawning by female worms [[59]11,[60]12].

   As a consequence of extensive host–parasite co-evolution, parasites
   exhibit a complex relationship with their hosts. Although the
   susceptibility/resistance mechanism for host infection with parasites
   remains unknown, immunological factors have proved to be intimately
   linked with parasite development [[61]13,[62]14]. Recently, there has
   been considerable interest in defining genetic and immunological
   markers that could be important for disease resistance [[63]15,[64]16].

   In our lab, we have compared the gene expression profiles of
   schistosomes from two natural hosts, water buffalo and yellow cattle
   [[65]17]. In this study, we used a bovine whole genome microarray to
   analyze the host molecular mechanism against schistosome infection. We
   report the first analysis of the transcriptional profiles of genes in
   peripheral blood from water buffalo and yellow cattle pre- and 7 weeks
   post-infection with S.japonicum. Understanding the molecular mechanisms
   affecting and regulating the development and survival of schistosomes
   will be essential for improving our knowledge of the host–pathogen
   relationship, revealing different susceptibilities within the natural
   host environment, and providing new ideas for the prevention of this
   disease.

Materials and Methods

Ethics Statement

   All work was conducted in accordance with the guidelines of the
   Association for Assessment and Accreditation of Laboratory Animal Care
   International (AAALAC). The animal study protocol was approved by the
   Animal Care and Use Committee of the Shanghai Veterinary Research
   Institute, Chinese Academy of Agricultural Sciences (CAAS), People’s
   Republic of China.

Collection of blood samples and RNA preparation

   Water buffalo and yellow cattle (three per group, 15–18-month-old
   males) were infected with cecariae of S.japonicum according to the
   method described in previous reports [[66]17, [67]18]. Before infection
   and 7 weeks post-infection, peripheral blood samples were collected
   from each animal using ordinary vacuum tubes containing a sterile
   anticoagulant EDTA dipotassium salt. RNA extraction of blood samples
   was completed within 24h. Total RNA was extracted and purified using
   the RNeasy Micro Kit and RNase-Free DNase Set (Qiagen, Germany)
   according to the manufacturer’s instructions. RNA samples were
   amplified and labeled using the Low Input Quick Amp Labeling Kit
   (Agilent Technologies, USA) as follows. A 200-ng aliquot of total RNA
   from each sample was converted into complementary RNA, labeled with the
   fluorophore cyanine 3-CTP (CY3c) and hybridized according to the
   manufacturer’s instructions. Samples were examined at A260 and A550
   using a ND-1000 spectrophotometer (Thermo Scientific, USA) to determine
   the yield, concentration, amplification efficiency, and abundance of
   CY3c. The labeled complementary RNA (cRNA) was further purified using
   the RNeasy MiniKit (Qiagen).

Microarray composition

   A whole genome microarray (Bovine V2, Agilent Technologies) was used to
   analyze gene expression profiles in peripheral blood from water buffalo
   and yellow cattle pre- and 7 weeks post-infection with S.japonicum. The
   microarray design was based on the sequence data of Btau 4.0 (Refseq
   Release 34, Mar 2009[[68]19]; TIGR Release12, Sep 2006; UniGene Build
   93, Sep 2008), 60-merSurePrint technology, and included 43,803
   contiguous sequences (contigs), printed in a 4×44 k feature format.
   Full details of this bovine microarray design have been deposited in
   the Gene Expression Omnibus (GEO) public database with the platform
   accession number [69]GPL11648.

Microarray hybridization

   For the samples from each host, three independent biological replicates
   were designed for microarray hybridization. The samples from yellow
   cattle were named Group 1 (g1) and Group 2 (g2) for pre- and 7 weeks
   post-infection, respectively; the samples from water buffalo were named
   Group 3 (g3) and Group 4 (g4). The microarray hybridization experiments
   were fulfilled by Shanghai Biotechnology Corporation (China). All
   procedures were operated according to the standard processes and
   supporting kits provided for Agilent microarray hybridization.

Feature extraction and data analysis

   Microarrays were scanned using an Agilent Microarray Scanner (G2565CA)
   and processed with Feature Extraction Software 10.7 (Agilent
   Technologies) to produce standardized data for statistical analysis.
   Feature-extracted data were analyzed using GENESPRING (version 11.0;
   Agilent Technologies/Silicon Genetics, USA). Data sets were further
   analyzed based on one-color experiments published previously [[70]20].
   The gProcessed Signal values were determined in GENESPRING using
   Agilent’s Feature Extraction software. The gProcessed Signal represents
   the signal after localized background subtraction and includes
   corrections for surface trends. Features were deemed ‘Absent’ when the
   processed signal intensity was less than twice the value of the
   processed signal error value; ‘Marginal’ when the measured intensity
   was at a saturated value or if there was a substantial amount of
   variation in the signal intensity within the pixels of a particular
   feature. Features that were neither Absent nor Marginal were deemed
   ‘Present’. Data points were included only if they were Present or
   Present-Absent, and probes or contigs were retained if all data points
   were Present or Present-Absent.

   The statistical analysis between two groups was performed using the
   Student’s test. Raw intensity data were analyzed using the R
   statistical language software ([71]www.r-project.org). The q-value
   estimation for false discovery rate (FDR) control was applied to
   analyze the data for enrichment analysis of Go function and KEGG
   pathway (See [72]S3, [73]S4, [74]S6, [75]S8 and [76]S9 Tables) [[77]21,
   [78]22]. Heatmap and principal component analysis (PCA) were plotted
   using Java Treeview software (Stanford University, USA) and a
   multidimensional scaling algorithm [[79]23].

Gene ontology and pathway pattern analysis

   Those genes considered as differentially expressed genes (DEGs), i.e.,
   those having at least two-fold changes in expression between two
   groups, were analyzed further. The analysis was performed online at
   [80]http://www.ebioservice.com/(supported by Shanghai Biochip
   Corporation, China), including screening DEGs, hierarchical clustering
   analysis, gene ontology (GO), and pathway pattern analysis. The
   analysis of GO terms associated with the DEGs in peripheral blood from
   the two host groups was performed using the combined graphs function of
   the BLAST software [[81]24]. GO correlations with relative gene
   expression values were made using Ermine J software [[82]25]. The Kyoto
   Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was analyzed
   using the maps at [83]http://www.genome.jp/kegg.

Real-time PCR

   A subset of genes predicted to be DE in the microarray analysis was
   selected for validation using real-time RT-PCR. Gene-specific primers
   were designed using PRIMER3
   ([84]http://frodo.wi.mit.edu/primer3/input.htm). Total amplified RNA
   from peripheral blood samples from each animal in every group was used
   for reverse transcription (RT) in a final volume of 20 μL using the
   PrimerScript RT Kit with gDNA Eraser (Cat# DRR047, Takara, Japan).
   Products were amplified using the SYBR Premix Ex Taq (Cat#DRR041A,
   Takara) in an ABI 7500 Real-time System (Applied Biosystems, USA) with
   the following profile: 50°C for 2 min, 95°C for 30 s; 40 cycles of 95°C
   for 5 s and 60°C for 34 s; 95°C for 15 s, and 60°C for 1 min. Each
   reaction was performed using 20 μL of cDNA from the RT reaction in a
   final volume of 50 μL. Expression levels of Bos taurus beta-actin
   (ACTB, GenBank Accession [85]NM_173979) were used as an endogenous
   control within each sample. Relative levels of gene expression were
   calculated using the 2^-ΔΔCT method [[86]26]. Each sample was analyzed
   for primer dimers, contamination, mispriming and specificity by
   advanced inspection of their melt curves and gel images. All the
   samples were done by three repeats, the analysis tool for qPCR result
   was ABI 7500 PCR system software (SDS V1.4). The correlation between
   real-time PCR and microarray data was performed by SPSS16.0 using
   Spearman’s Rho measure of correlation [[87]27].

Results

Global gene expression profiles of the microarray

   Three biological replicates of blood samples from each host were
   evaluated and the correlation among a total of 12 samples was 0.97–0.99
   ([88]Fig 1A). The microarray data were submitted to the Gene Expression
   Omnibus public database, under the GEO series accession number:
   [89]GSE34021 ([90]http://www.ncbi.nlm.nih.gov/geo). To evaluate the
   overall data structure, we plotted the first two principal components
   of the PCA to capture the overall variance of all samples in two
   dimensions. This analysis clearly separated the data into four
   subgroups, which clustered the biological replicates together and
   separated the samples by the host and the infection time, i.e., yellow
   cattle (g1 and g2), water buffalo(g3 and g4) ([91]Fig 1B). Two main
   clusters separated the genes of the two natural hosts, and the gene
   expression patterns of samples pre-infection and post-infection in each
   host group were clustered ([92]Fig 2A). In the yellow cattle groups and
   water buffalo groups, significantly DEGs for hierarchical clustering
   were drawn by heatmap using Genespring software separately (fold change
   >2, p<0.01) ([93]Fig 2B&2C).

Fig 1. Global microarray data analysis.

   [94]Fig 1
   [95]Open in a new tab

   (a) Correlation analysis for all samples; (b) Principal component
   analysis (PCA) of the transcript profiles from g1, g2, g3 and g4. g1
   and g2 represent the samples from pre-infection and post-infection,
   respectively, in yellow cattle (C, cattle);g3 and g4 represent the
   samples from pre-infection and post-infection, respectively, in water
   buffalo (B, buffalo).

Fig 2. Cluster analysis for DEGs.

   [96]Fig 2
   [97]Open in a new tab

   (a) Cluster analysis for g1, g2, g3 and g4 (p<0.01, top 5,000); (b)
   Cluster analysis for g1 and g2 (p<0.01); (c) Cluster analysis for g3
   and g4 (p<0.01).

DEGs between water buffalo and yellow cattle pre-infection

   Between water buffalo and yellow cattle, there were 5,740 DEGs at week
   zero. Compared with the samples from yellow cattle, there were 2,594
   up-regulated and 3,146 down-regulated DEGs in water buffalo at week
   zero. Before infection, compared with yellow cattle, the up-regulated
   genes in the water buffalo group were mainly associated with tight
   junctions and the TGF-beta signaling pathway, while the down-regulated
   genes in the water buffalo group were mainly associated with the p53
   signaling pathway, B cell receptor signaling pathway, T cell receptor
   signaling pathway, hematopoietic cell lineage, Toll-like receptor
   signaling pathway, and natural killer cell-mediated cytotoxicity
   ([98]Table 1). In detail, the up-regulated genes in the water buffalo
   group included: IL-22R, IL-24, IL-17B, protein kinase C, growth
   differentiation factor 7, protein phosphatase 2A ([99]S1 Table), while
   the down-regulated genes in the water buffalo group included: the MHC-I
   molecule, interferon family, IL-2R, IL-4R, CD80, CD46, TNF family
   (TNFSF5, TNFSF8), vascular endothelial growth factor C ([100]S2 Table).
   In the cytokine–cytokine receptor interaction pathway, the DEGs in the
   interferon family, hematopoietins, chemokines, and TNF family were
   under-expressed in water buffalo, while the IL-10 family was
   over-expressed compared with yellow cattle before infection ([101]Table
   2). In total, before infection, the DEGs were mainly associated with
   immune-related pathways, and these differences are likely responsible
   for the resistance of water buffalo to schistosome infections.

Table 1. Functional pathways of DEGs in water buffalo compared with yellow
cattle, pre-infection[102] ^a .

   Pathway                                   Genes included in pathway
                      Pathway of up-regulated genes
   Tight junction
   ACTIN1,CLDN10,HCLS1,MPDZ,MRK,MYH3,MYH4,PPP2R1A,PPKCB,PRKC1
   TGF-beta signaling pathway                DCN,GDF7,LEFTY2,PPP2R1A
                     Pathway of down-regulated genes
   p53 signaling pathway                     CCNB1,CCNE2,CDK1,CDK4,SESN3
   B cell receptor signaling pathway         IFITM1,IKBKG,JUN,LYN
   T cell receptor signaling pathway         CDK4,IKBKG,JUN,MAPK13
   Hematopoietic cell lineage                IL12RA,IL4R,ITGA4
   Toll-like receptor signaling pathway
   CD40,CD80,IFNAR1,IKBKG,JUN,LBP,MAPK13
   Natural killer cell mediated cytotoxicity
   IFNGR1,IFNGR2,JSP.1,GZMB,IFNAR1,SH2D1A
   [103]Open in a new tab

   ^a More details are provided in [104]S1&[105]S2 Tables.

Table 2. Some DEGs involved in cytokine–cytokine receptor interaction
pathways in water buffalo compared with yellow cattle, pre-infection.

   Gene ID Symbol Probe ID p value g3_vs_g1fold change Gene name
   Interferon family
   508619 IFNGR1 A_73_115260 3.0E-4 1.44E-3 interferon gamma receptor 1
   514889 IFNGR2 A_73_107789 0.0 5.39E-3 interferon gamma receptor 2
   282257 IFNAR1 A_73_116078 0.035 0.24 interferon alpha receptor 1
   Hematopoietins
   281861 IL2RA A_73_109472 0.0042 0.07 interleukin 2 receptor, alpha
   404154 IL4R A_73_112133 0.0014 0.37 interleukin 4 receptor
   Chemokines
   281735 CXCL5 A_73_106079 0.024 0.22 chemokine (C-X-C motif) ligand 5
   616732 CCL16 A_73_105123 7.0E-4 0.08 chemokine (C-C motif) ligand 16
   408018 CCR3 A_73_103766 0.0090 0.16 chemokine (C-C motif) receptor 3
   510668 CCR7 A_73_113931 1.0E-4 0.057 chemokine (C-C motif) receptor 7
   TNF family
   286849 CD40 A_73_110113 0.033 0.36 CD40 molecule, TNF receptor
   superfamily member 5
   574056 TNFSF8 A_73_106418 0.0044 0.24 tumor necrosis factor (ligand)
   superfamily, member 8
   IL-10 family
   508044 IL22RA1 A_73_115811 0.0 2.53 interleukin 22 receptor, alpha 1
   526285 IL24 A_73_111317 4.0E-4 8.86 interleukin 24
   [106]Open in a new tab

DEGs between water buffalo and yellow cattle 7 weeks post-infection

   After 7 weeks of infection with S.japonicum, there were 6,353 DEGs
   between the two hosts. Among these, 4,185 DEGs were also present prior
   to infection, accounting for 72.9% of the 6,353 DEGs at 7 weeks. Among
   the 2,168 DEGs that were newly presented after infection, 1,246 were
   up-regulated, and 922 were down-regulated in the water buffalo group
   compared with the yellow cattle group.

   After 7 weeks post-infection, the GO function analysis of the newly
   presenting up-regulated genes in water buffalo showed that they were
   mainly related to the regulation of locomotion, cell motion,
   transmembrane transporter activity, negative regulation of immune
   system process, responses to stimuli, transport, metabolism, and
   biological processes ([107]S3 Table). The newly presenting
   down-regulated genes in water buffalo were mainly correlated with T
   cell selection, responses to endogenous stimuli, the positive
   regulation of developmental processes, and immune system processes,
   such as immune system development, developmental maturation, etc.
   ([108]S4 Table). Further pathway analysis of these newly present DEGs
   between the two natural hosts after 7 weeks of infection showed that
   they are focused on the following important pathways: natural killer
   cell-mediated cytotoxicity, complement and coagulation cascades,
   endocytosis, the hematopoietic cell lineage, cytokine–cytokine receptor
   interactions, the p53 signaling pathway, the MAPK signaling pathway,
   apoptosis, the Jak-STAT signaling pathway, the Toll-like receptor
   signaling pathway, purine metabolism, the B cell receptor signaling
   pathway ([109]S5 Table). These results revealed that, prior to
   infection, the newly presenting DEGs take part in many immune-related
   pathways. Additionally, DEGs belonging to some other important
   pathways, including cytokine–cytokine receptor interactions, apoptosis,
   purine metabolism ([110]Table 3), were enriched after infection.

Table 3. Functional pathways for newly presenting DEGs in water buffalo
compared with yellow cattle 7 weeks after infection[111] ^a .

   Pathway New presenting genes included in pathway (g4_vs_g2)
   Natural killer cell mediated cytotoxicity
   Up-regulated genes FCGR3, NFATC3, PRKCA
   Down-regulated genes IFNA14L, CD244, FAS, NCR1, PIK3CD
   Complement and coagulation cascades
   Up-regulated genes PLAT
   Down-regulated genes C1QA, C2, CD55, PROS1, SERPING1
   Endocytosis
   Up-regulated genes MET, NTRK1
   Down-regulated genes ADRB3, CCR5, CHMP2B, FAM125A, TFRC, WWP1
   Hematopoietic cell lineage
   Up-regulated genes -
   Down-regulated genes CD14, CD55, IL1B, ITGA2B, TFRC
   Cytokine-cytokine receptor interaction
   Up-regulated genes MET,TNFSF13B
   Down-regulated genes IL14L, CCR5, FAS,IL-15,IL1B,IL20RA, TNFSF13
   p53 signaling pathway
   Up-regulated genes IGF1
   Down-regulated genes FAS,IGFBP3,SESN2
   MAPK signaling pathway
   Up-regulated genes GNG12, NTRK1, PRKCA
   Down-regulated genes CD14, FAS, IL1B, MAP3K4, MAPT,
   Apotosis
   Up-regulated genes NTRK1
   Down-regulated genes FAS, IL1B,PIK3CD
   Jak-STAT signaling pathway
   Up-regulated genes -
   Down-regulated genes IL14L, IL15, IL20RA, PIK3CD, STAT2
   Toll-like receptor signaling pathway
   Up-regulated genes -
   Down-regulated genes IL14L,CD14, IL1B, PIK3CD
   Purine metabolism
   Up-regulated genes PDE3B,POLR2C
   Down-regulated genes DCK,GDUOK,ENPP1
   B cell receptor signaling pathway
   Up-regulated genes LOC515489, NFATC3
   Down-regulated genes PIK3CD
   [112]Open in a new tab

   ^a In detail find in [113]S6 Table.

The common DEGs in water buffalo and yellow cattle post-schistosome infection

   After 7 weeks of infection with S.japonicum, only 83 DEGs were found to
   be in common in water buffalo and yellow cattle ([114]Fig 3A, p<0.05,
   fold change>2). The cluster analysis of these common genes is shown in
   [115]Fig 3B. The GO function enrichment analysis showed that the common
   DEGs are mainly involved in the regulation of fibrinolysis, lipid
   binding, cell adhesion, nucleic acid binding, transmembrane transporter
   activity ([116]S6 Table). The GO function analysis for biological
   process, molecular function, and cellular component is shown in
   [117]Fig 3C (p<0.05). These common DEGs maybe essential response genes
   during S. japonicum infection; a detailed gene list is given in [118]S7
   Table.

Fig 3. Common DEGs in water buffalo and yellow cattle post-schistosome
infection.

   [119]Fig 3
   [120]Open in a new tab

   (a) Common DEGs in two hosts, DEGs both in water buffalo and yellow
   cattle, 7 weeks post-infection with S. japonicum compared to
   pre-infection. (p<0.05, fold change>2); (b) Cluster analysis for common
   DEGs. (p<0.05, fold change>2); (c) Go Function distribution of common
   DEGs. (p<0.05).

DEGs found only in yellow cattle post-schistosome infection

   After 7 weeks of infection, 390 DEGs, compared with pre-infection, were
   found only in yellow cattle, accounting for 78.5% of the total DEGs
   (497) in yellow cattle. The enrichment analysis of the KEGG signaling
   pathway for these DEGs showed that they are associated with neuroactive
   ligand–receptor interactions, cytokine–cytokine receptor interactions,
   glycosphingolipid biosynthesis, chemokine signaling pathways ([121]S8
   Table).

DEGs found only in water buffalo post-schistosome infection

   Compared with yellow cattle, the less-susceptible host water buffalo
   showed many more DEGs after 7 weeks of infection with S.japonicum
   (2,197 versus 497). At the same time, the majority of the DEGs were
   different in each host; 2,114 were unique to water buffalo, while 390
   were unique to yellow cattle.

   The enrichment analysis of the KEGG signaling pathway for those DEGs
   found only in water buffalo showed that they are mainly involved in
   metabolic pathways, T cell receptor signaling pathways, histidine
   metabolism, vascular smooth muscle contraction, base excision repair,
   primary immunodeficiency, leukocyte transendothelial migration, natural
   killer cell-mediated cytotoxicity, the neurotrophin signaling pathway,
   the insulin signaling pathway, the Wnt signaling pathway, antigen
   processing and presentation ([122]S9 Table).

Validation of mRNA expression by real-time PCR

   To validate the microarray transcriptional data, the mRNA expression
   profiles for a subset of genes from different categories were assessed
   using quantitative real-time PCR. The selected genes and validation
   results are presented in [123]Table 4. 22 of the 24 (91.67%) selected
   genes were validated, which confirmed the results for the
   directionality of regulation and the fold-change of the microarray
   experiments.

Table 4. Real-time PCR validation of the microarray results[124] ^a .

   Accession number Gene symbol Gene name Microarray fold change Real-time
   PCR fold change
   DEGs in both water buffalo and yellow cattle post-infection compared to
   pre-infection g3_VS_g1 g4_VS_g2 g3_VS_g1 g4_VS_g2
   [125]NM_001033617 CTSC cathepsin C 2.30E-02 3.41E-02 0.66 0.74
   [126]AB098980 ND1 NADH dehydrogenase subunit 1 3.66E-03 2.65E-03 0.05
   0.09
   [127]NM_001098958 CDKN1A cyclin-dependent kinase inhibitor 1A 23.75
   22.66 5.09 1.54
   [128]NM_001076517 LY6D lymphocyte antigen 6 complex, locus D 62.14
   56.03 98.30 6.77
   [129]NM_001102498 NKAPL NFKB activating protein-like 35.33 31.67 1.35
   3.28
   [130]NM_001038611 ZPBP zonapellucida binding protein 7.79 11.16 19.41
   8.28
   DEGs in water buffalo post-infection compared to pre-infection g4_vs_g3
   g4_vs_g3
   [131]NM_001081520 LY6G6E lymphocyte antigen 6 complex, locus G6E 0.23
   0.04
   [132]NM_176872 THBS2 thrombospondin 2 0.33 0.07
   DEGs in yellow cattle post-infection compared to pre-infection g2_vs_g1
   g2_vs_g1
   [133]NM_001206292 ZMYM6 PREDICTED: Bos taurus zinc finger, MYM-type 6
   2.37 1.29
   [134]NM_001100304 GPR52 GPR52,G protein-coupled receptor 52 2.34 3.55
   [135]NM_174006 CCL2 CCL2,chemokine (C-C motif) ligand 2 0.12 0.16
   [136]NM_001192973 AGMO Bos taurus alkylglycerolmonooxygenase
   (AGMO),TMEM195 0.29 0.17
   [137]XM_002694054 TNFRSF8 PREDICTED: Bos taurus Tumor necrosis factor
   receptor superfamily member 8-like 0.38 0.42
   DEGs common to both yellow catte and water buffalo post-infection
   compared to pre-infection g2_vs_g1 g4_vs_g3 g2_vs_g1 g4_vs_g3
   [138]NM_001077991 RECQL RecQ protein-like (DNA helicase Q1-like) 2.26
   2.02 2.41 1.12
   [139]NM_001014956 NFYA NFYA,nuclear transcription factor Y, alpha 2.16
   2.17 1.39 1.67
   [140]NM_174452 ROCK2 Rho-associated, coiled-coil containing protein
   kinase 2 2.15 2.75 1.97 2.45
   DEGs in water buffalo compared to yellow cattle at 7w post-infection
   g4_vs_g2 g4_vs_g2
   [141]NM_001080247 JKAMP JNK1/MAPK8-associated membrane protein 2.62
   1.36
   [142]NM_001034492 C2 Bos taurus complement component 2 9.17E-02 0.006
   [143]NM_175703 EBD Bos taurus defensin, beta 1 0.11 0.41
   [144]NM_174093 IL1B interleukin 1, beta 0.18 0.25
   [145]NM_173925 IL8 interleukin 8 0.19 0.21
   [146]NM_174008 CD14 CD14 molecule 0.32 0.74
   [147]Open in a new tab

   ^a The primer sequences used for the validation experiment are provided
   in [148]S10 Table.

Discussion

   Helminth parasites are generally well-adapted to some hosts, but
   certain genotypes of their natural host organism are less susceptible
   to infection, i.e., the water buffalo is less susceptible to S.
   japonicum than yellow cattle. However, the mechanism underlying this
   difference in susceptibility remains unknown. Resistance to infection
   and the immune clearance of S.japonicum do not rest on a single
   molecular mechanism of killing, but rather on the orchestration of
   multiple pathways that disable and degrade parasites, leading to their
   expulsion [[149]28, [150]29]. Both innate and adaptive
   immuno-mechanisms for helminth infection finely govern host
   susceptibility. Studies have shown that type 2 immunity protects the
   host from helminth infections. CD4^+ T cells can drive a suite of type
   2 anti-parasite mechanisms, including class-switched antibodies,
   activated leukocytes, and innate defensive molecules; the coordination
   effects of these multiple pathways disable, degrade and wipe out the
   parasites, leading to their destruction or expulsion [[151]29,
   [152]30].

   In our previous studies, water buffalo and yellow cattle were
   sacrificed 7 weeks post-infection with S.japonicum, at which time their
   livers showed many histopathological differences; the parasites derived
   from yellow cattle showed significantly more worm recovery, better worm
   development, and a different microscopic morphology compared with those
   derived from water buffalo [[153]12, [154]17]. Further analysis of the
   genes responsible for the differences in susceptibility between water
   buffalo and yellow cattle was performed in this study. Domestic cattle
   are artificially grouped into 2 species, Bos taurus and Bos indicus.
   Indicus cattle are the predominant breed in China. The first taurine
   genome was sequenced in 2009 [[155]19], while the indicine genome was
   sequenced more recently in 2012 [[156]31]. Comparative analysis of
   these genomes showed that both breeds shared high similarity at the
   nucleotide level in all autosomes and the X chromosome, covering 97% of
   taurine protein coding genes [[157]19, [158]31]. As water buffalo and
   yellow cattle are both members of the Bovidae family, they are closely
   related, and the vast amount of cattle genomic resources might serve as
   shortcuts for further advances genome science and biotechnology in this
   species [[159]32]. In 2014, the water buffalo genome research group
   also declared that the bovine genome could be utilized in association
   studies [[160]33]. In this paper, a Bos taurus genome-wide gene chip
   was used to analyze and compare the overall gene expression profiles of
   peripheral blood from water buffalo and yellow cattle, both pre- and
   post infection with S.japonicum. The results revealed that prior to
   schistosome infection, most of the DEGs between water buffalo and
   yellow cattle were mainly related to immune system function, including
   the B cell receptor signaling pathway, the T cell receptor signaling
   pathway, the Toll-like receptor signaling pathway, hematopoietic cell
   lineage, natural killer cell-mediated cytotoxicity ([161]Table 1).
   Prior to infection, many genes associated with the immune
   response/system were expressed at lower levels in the less-susceptible
   water buffalo, which exhibited less pathological damage and fewer worms
   developing to a mature state. When the animals were challenged with S.
   japonicum for 7 weeks, some other genes of molecules correlated with
   metabolism, apoptosis, and signal transduction were found to be
   differentially expressed, and immune-related genes constituted the
   majority of the DEGs. Genome research on schistosomes has shown that
   many molecules of schistosomes are similar to those of their hosts.
   Thus, the molecules of schistosomes could act as receptors for the
   host’s signals, thereby enabling their growth and development
   [[162]34,[163]35]. Our findings are consistent with a report on an
   immunodeficient mouse model infected with S. mansoni [[164]14]. This
   study revealed that in immunodeficient hosts, parasites failed to
   receive appropriate signals from the host immune system, resulting in
   the appearance of attenuated forms that prolonged the survival of host
   and parasite. Subsequent studies suggest that the type 2 response
   driven by CD4^+ T cells during pre-patent infections of immunocompetent
   hosts is exploited by schistosomes to complete their development toward
   reproductively mature adult parasites [[165]16]. In our previous study,
   the percentage of CD4^+ T cells in water buffalo was significantly
   lower than that in yellow cattle [[166]12]; and in this study, the
   expression levels of some immuno-associated genes in water buffalo were
   also lower than in yellow cattle, which supports the above observation
   that an appropriate level of immuno-stimulation from the host is
   necessary for the development and survival of the worms in their hosts.

   Prior to infection, DEGs in the IL-10 family were expressed at higher
   levels in water buffalo compared with yellow cattle ([167]Table 2).
   IL-10 family cytokines emerged before the adaptive immune response, and
   these cytokines elicit diverse host defense mechanism, especially from
   epithelial cells. IL-10 family cytokines are essential factors for
   maintaining the integrity and homeostasis of tissue epithelial layers
   [[168]36]. During infection, IL-10 family cytokines can promote innate
   immune responses from tissue epithelia to limit the damage from
   infection. To establish a successful infection, schistosomes first need
   to penetrate the host by breaking through the skin. The IL-10 family
   cytokines IL-22R and IL-24 were over-expressed by 2.53- and 8.86-fold,
   respectively, in water buffalo, which indicated that the skin layer
   defense for schistosomes in water buffalo might be more effective than
   that in yellow cattle.

   After 7 weeks of infection with S.japonicum, there were 6,353 DEGs
   between the two hosts; 2,168 of these DEGs were newly presented after
   infection. Further analysis showed that the newly presenting DEGs
   between the two hosts were mainly associated with the innate immune
   system, immune regulation, hematopoietic cells, thep53 signaling
   pathway, purine metabolism, etc., and most of the DEGs were
   under-expressed in water buffalo compared with yellow cattle. The
   pathway enrichment analysis showed that the DEGs constituted similar
   immune-related pathways, both pre- and post-infection, between the two
   hosts, which suggests that native differences between the two hosts are
   likely to be the key factors that affect the establishment and
   maintenance of S. japonicum infections.

   After infection, water buffalo and yellow cattle showed different
   changes compared with pre-infection, and most DEGs were uniquely found
   only in yellow cattle or water buffalo; only 83 DEGs were observed in
   both hosts. These 83 common DEGs maybe the essential response genes
   during S.japonicum infection, and GO function analysis showed that the
   highest two GO functions were associated with the regulation of
   fibrinolysis ([169]S6 Table), which is the special pathological damage
   resulting from schistosome infections. The unique DEGs (390) in yellow
   cattle were mainly enriched in pathways of cytokine–cytokine receptor
   interactions, neuroactive ligand–receptor interactions, chemokine
   signaling pathways, etc., which is consistent with the phenomenon that
   more intense inflammation occurred in yellow cattle than in water
   buffalo after infection [[170]12]. The unique DEGs (2,114) in water
   buffalo were mainly associated with immune-related pathways, such as T
   cell receptor signaling, primary immunodeficiency, antigen processing
   and presentation, etc., which again indicates that immune factors
   likely contribute to S. japonicum resistance in water buffalo.

   This study was the first to compare and analyze the overall gene
   expression profiles in the peripheral blood from water buffalo and
   yellow cattle, both pre- and post-infection with S.japonicum. It will
   increase our understanding of the relationship between parasites and
   the different susceptibilities of natural hosts.

Supporting Information

   S1 Table. Function pathway of up-regulated genes in water buffalo
   compared to yellow cattle pre-infection.

   (DOC)
   [171]Click here for additional data file.^ (43.5KB, doc)
   S2 Table. Function pathway of under-regulated genes in water buffalo
   compared to yellow cattle pre- infection.

   (DOC)
   [172]Click here for additional data file.^ (64.5KB, doc)
   S3 Table. Enrichment analysis of Go function for new presenting
   over-expressed genes in water buffalo compared to those in yellow
   cattle 7weeks after infection.

   (DOC)
   [173]Click here for additional data file.^ (85KB, doc)
   S4 Table. Enrichment analysis of Go function for new presenting
   under-expressed genes in water buffalo compared to those in yellow
   cattle 7weeks after infection.

   (DOC)
   [174]Click here for additional data file.^ (73KB, doc)
   S5 Table. Some function pathway for new presenting DEGs between water
   buffalo and yellow cattle 7weeks after infection.

   (DOC)
   [175]Click here for additional data file.^ (125KB, doc)
   S6 Table. Enrichment analysis of Go function for DEGs both in water
   buffalo and yellow cattle post-infection with Sj 7w compared to
   pre-infection.

   (DOC)
   [176]Click here for additional data file.^ (82KB, doc)
   S7 Table. List of some DEGs both in water buffalo and yellow cattle
   post-infection with Sj 7w compared to pre-infection.

   (DOC)
   [177]Click here for additional data file.^ (127KB, doc)
   S8 Table. Enrichment analysis of KEGG pathway for DEGs unique to yellow
   cattle 7 weeks post-inection with S. japonicum compared with
   pre-infection.

   (DOC)
   [178]Click here for additional data file.^ (54KB, doc)
   S9 Table. Enrichment analysis of KEGG pathway for DEGs unique to water
   buffalo 7 weeks post-infection with S. japonicum compared with
   pre-infection.

   (DOC)
   [179]Click here for additional data file.^ (104.5KB, doc)
   S10 Table. Primers in real-time PCR validation for microarray results.

   (DOC)
   [180]Click here for additional data file.^ (50.5KB, doc)

Acknowledgments