Journal of Immunology Research
Volume 2018 (2018), Article ID 9419204, 26 pages
https://doi.org/10.1155/2018/9419204

Research Article

Immune Response to Rotavirus and Gluten Sensitivity

Antonio Puccetti,1,2 Daniele Saverino,³ Roberta Opri,⁴ Oretta Gabrielli,⁴ Giovanna Zanoni,⁴ Andrea Pelosi,¹ Piera Filomena Fiore,¹ Francesca Moretta,⁵ Claudio Lunardi,⁵ and Marzia Dolcino⁵

¹Immunology Area, Pediatric Hospital Bambino Gesù, Viale San Paolo 15, 00146 Rome, Italy
²Department of Experimental Medicine, Section of Histology, University of Genova, Via G.B. Marsano 10, 16132 Genova, Italy
³Department of Experimental Medicine, Section of Human Anatomy, University of Genova, Via De Toni 14, 16132 Genova, Italy
⁴Immunology Unit, University Hospital of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy
⁵Department of Medicine, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy

Correspondence should be addressed to Marzia Dolcino; marziadolcino@gmail.com

Received 31 July 2017; Revised 18 December 2017; Accepted 25 December 2017; Published 15 March 2018

Academic Editor: Peirong Jiao

Copyright © 2018 Antonio Puccetti et al. This 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.

Abstract.

Rotavirus is a double-stranded RNA virus belonging to the family of Reoviridae. The virus is transmitted by the faecal-oral route and infects intestinal cells causing gastroenteritis. Rotaviruses are the main cause of severe acute diarrhoea in children less than 5 years of age worldwide. In our previous work we have shown a link between rotavirus infection and celiac disease. Nonceliac gluten sensitivity (NCGS) is emerging as new clinical entity lacking specific diagnostic biomarkers which has been reported to occur in 6–10% of the population. Clinical manifestations include gastrointestinal and/or extraintestinal symptoms which recede with gluten withdrawal. The pathogenesis of the disease is still unknown. Aim of this work is to clarify some aspects of its pathogenesis using a gene array approach. Our results suggest that NCGS may have an autoimmune origin. This is based both on gene expression data (i.e., TH17-interferon signatures) and on the presence of TH17 cells and of serological markers of autoimmunity in NCGS. Our results also indicate a possible involvement of rotavirus infection in the pathogenesis of nonceliac gluten sensitivity similarly to what we have previously shown in celiac disease.

1. Introduction

Nonceliac gluten sensitivity (NCGS) can be defined as a nonallergic condition in which the consumption of gluten can lead to symptoms similar to those observed in celiac disease (CD). NCGS is characterized by the absence of celiac specific antibodies (against tissue transglutaminase, endomysium, and/or deamidated gliadin peptide) and absence of classical enteropathy (Marsh 0-1) although an increased density of CD3+ intraepithelial lymphocytes can be observed in duodenal biopsies. Patients with NCGS may have variable HLA status, and positivity for HLA-DQ2 and/or DQ8 has been found in roughly 50% of patients with NCGS. Serological analyses of NCGS patients revealed a high prevalence (40–50%) of first generation antigliadin IgG antibodies. NCGS is characterized by symptoms that usually occur soon after gluten ingestion and disappear or improve with gluten withdrawal but relapse following gluten challenge. The clinical presentation of NCGS may be a combination of gastrointestinal symptoms, including abdominal pain, bloating, bowel habit abnormalities (diarrhoea or constipation), and systemic manifestations, that is “foggy mind,” fatigue, muscle and joint pain, leg or arm numbness, eczema and skin rash, depression, and anemia. Similarly to patients with CD, subjects with clinical manifestations compatible with NCGS should start a gluten-free diet. Since it is still not clear whether NCGS is a permanent or transient condition, reintroduction of gluten after 1-2 years on a gluten-free diet can be considered [1, 2].

Rotavirus is a double-stranded RNA virus belonging to the family of Reoviridae.

The virus is transmitted by the faecal-oral route and infects intestinal cells causing gastroenteritis. Rotaviruses are the main cause of severe acute diarrhoea in children less than 5 years of age worldwide [3]. They are responsible for 453,000 deaths worldwide each year, which in most cases (85%) occur in developing countries [3]. The virus particle is composed of six viral proteins (VPs) called VP1, VP2, VP3, VP4, VP6, and VP7. Among these, the glycoprotein VP7 is located on the outer surface of the virus determining the specific G-type of the strain and plays a role in the development of immunity to infection [4].

We have previously described the presence, in active celiac disease (CD), of a subset of antitransglutaminase IgA antibodies that recognizes the viral protein VP-7 and is able to increase intestinal permeability and induce monocyte activation [5]. We then showed that the antirotavirus VP7 antibodies may be even detected before the CD onset and the detection of antitissue transglutaminase (tTG) and antiendomysium antibodies, showing a predictive role [6]. In addition, we observed that these antibodies were able to induce in human T84 intestinal cell line the modulation of genes involved in biological processes that represents typical features of CD [6]. Taken together, our data seem to provide a link between rotavirus infection and CD.

In this paper, we aim at clarifying some aspects of the pathogenesis of NCGS by a gene-array approach. In particular, we plan to verify the possibility of the involvement of an autoimmune mechanism in the disease. In addition, we also aim at investigating a possible involvement of rotavirus infection in the development of NCGS. For this purpose, we compared the global panel of modulated genes in NCGS to the dataset of human T84 intestinal cells treated with antirotavirus VP7 antibodies, described in our previous work [6], and to a dataset of acute phase of rotavirus infection, downloaded from the GEO (Gene Expression Omnibus) database, searching for transcriptional profiles that may be associated to viral infection.

2. Materials and Methods

2.1. Patients

We studied a cohort of 16 patients (6 males and 10 females, mean age: 27.3 years) affected by NCGS, attending the Unit of Autoimmune Diseases and the Immunology Unit and Child Neuropsychiatry Unit at the University Hospital of Verona, Italy.

All the enrolled subjects were recruited after informed consent. Main symptoms were headache, dermatitis, chronic urticaria, muscle and joint pain, bloating, abdominal pain, diarrhoea, alternating bowel movements, and fatigue in a variable combination.

Diagnosis of NCGS was established when all the following criteria were met: (1) exclusion of wheat allergy by clinical history and determination of specific IgE; (2) exclusion of celiac disease by absence of celiac-specific antibodies tissue transglutaminase (tTG), endomysium (EMA), and/or deamidated gliadin peptides (DGP); (3) duodenal biopsy with a histological damage grade 0 to 1, according to Marsh’s classification; (4) significant improvement of symptoms on strict gluten-free diet and relapse of symptoms after gluten reintroduction.

2.2. Detection of Anti-VP7 Peptide Antibodies

The ELISA test for antibody binding to the synthetic peptides has been carried out as already described elsewhere with minor modifications [7]. The synthetic peptides were used at a concentration of 20 μ/mL in PBS to coat polystyrene plates (Immulon 2HB, Thermo). For the detection of antirotavirus VP7 peptide IgA antibodies, only the sera whose OD readings were higher than the mean plus three standard deviations of each serum dilution of the control group were considered positive. OD values higher than 0.140 were considered positive.

2.3. Gene Array

Peripheral blood cells were collected for analysis of gene expression profiles on a gluten-containing diet. PAXgene Blood RNA tubes (PreAnalytiX, Hombrechtikon, Switzerland) were used for blood collection and total RNA was extracted according to the protocol supplied by the manufacturer. Preparation of cRNA hybridization and scanning of arrays for each samples were performed following the manufacturer instructions (Affymetrix, Santa Clara, CA, USA) by Cogentech Affymetrix microarray unit (Campus IFOM IEO, Milan, Italy) using the Human Genome U133A 2.0 GeneChip (Affymetrix). The gene expression profiles were analysed using the GeneSpring software version 12.1 (Agilent Technologies, Santa Clara, CA, USA) that calculated a robust multiarray average of background-adjusted, normalized, and log-transformed intensity values applying the robust multiarray average algorithm (RMA). The normalized data were transformed to the log₂ scale. The unpaired t-test was performed to determine which genes were modulated at a significant level (), and values were corrected for multiple testing by using Bonferroni correction. Finally, statistically significant genes were chosen for final consideration when their expression was at least 1.5-fold different in the test sample versus control sample. Genes that passed both the p value and the FC restriction were submitted to functional and pathway enrichment analysis according to the Gene Ontology (GO) annotations employing the Panther expression analysis tools (http://pantherdb.org/).

2.4. Protein-Protein Interaction (PPI) Network Construction and Network Modular Analysis

All the possible interactions among the protein products of DEGs were analysed with Search Tool for the Retrieval of Interacting Genes (STRING version 1.0; http://string-db.org/) a web-based database that includes experimental as well as predicted interaction information and covers more than 1100 sequenced organisms. Only protein-protein interaction (PPI) pairs that were confirmed by experimental studies were selected, and a score of ≥0.7 for each PPI pair was used to build a PPI network.

Cytoscape software [8] was used to define the topology of the built network, and the Molecular Complex Detection (MCODE) [9] was used to find densely connected region (modules) of the network that could be involved in the modulation of biological processes that are relevant for the disease pathogenesis. To find locally dense regions of a graph, MCODE applies a vertex-weighting scheme based on a clustering coefficient that is a measure of the degree to which nodes in a graph tend to cluster together.

The following settings in MCODE were used: degree cutoff = 2, K-core = 3, and max. depth = 100. Functional enrichment for a given module was assessed quantitatively using the Panther tool.

2.5. Analysis of the Association between DEGs and Human Diseases

We used the software Ingenuity Pathway Analysis (IPA, Ingenuity Systems) to evaluate diseases and disorders that could be statistically significantly associated to gene modulation observed in NCGS samples. The statistical significance of gene-disease associations was calculated in IPA by the Fisher’s exact test ().

2.6. Detection of Soluble Mediators in GS Sera

Serum levels of sCTLA-4, s PD-1, and sgp130/IL6ST were detected before and after gluten-free diet using commercially available ELISA kits according to the manufacturer’s instructions. ELISA kits were purchased from Bender MedSystems (Milano, Italy) (sCTLA-4), from R&D Systems (Minneapolis, United States) (sgp130), and from EMELCA Bioscience (Clinge, Netherlands) (sPD-1).

2.7. FACS Analysis

Cells collected from patients and normal controls were cultured at a concentration of 1∗10⁶ cells/mL in 2 mL tubes containing 1 mL of RPMI 1640 + FCS 10% (Lonza, Basel, CH). Cells were stimulated overnight with Dynabeads Human T-Activator CD3/CD28 (Life Technologies, Carlsbad, CA, USA). The detection of IL-17 production was analysed using the IL-17 Secretion Assay (Miltenyi Biotec, Bergisch Gladbach, D) following the manufacturer’s instruction. Briefly, cells were washed with 2 mL of cold buffer at 300 ×g for 5 minutes at 4°C, and the pellet was resuspended in 90 μL of cold medium. Cells were then incubated with 10 μL of IL-17 Catch Reagent for 5 minutes in ice and cultured in 1 mL of warm medium at 37°C for 45 minutes under slow continuous rotation. Cells were then washed with cold buffer and resuspended in 75 μL of cold buffer; 10 μL of IL-17 Detection Antibody APC, 10 μL of anti-CD3 PerCP (Becton Dickinson, Franklin Lakes, NJ, USA), and 5 μL of anti-CD4 APC-H7 (Becton Dickinson) monoclonal antibodies were added. Incubation was carried out in ice for 10 minutes. Finally, cells were washed and resuspended in an appropriate volume of PBS and acquired on a FACSCanto II cytometer (Becton Dickinson). Analysis was performed with FlowJo 9.3.3 software (Tree Star, Ashland, OR, USA).

2.8. Statistical Analysis

Data obtained from the analysis of the soluble mediators CTLA-4, gp130, and PD-1 and from the detection of antigliadin antibodies were submitted to statistical testing using the Wilcoxon nonparametric statistical hypothesis test for paired samples.

Data obtained from the ELISA test for the detection of antirotavirus VP7 peptide antibodies were submitted to statistical testing using the Mann–Whitney nonparametric test. Statistical analysis was performed using GraphPad Prism Software version 5.00 (GraphPad Software, La Jolla, California, USA, http://www.graphpad.com).

3. Results and Discussion

Many aspects of NCGS are still unknown; in particular, it is still not clear whether the disease is permanent or transient or whether the disease has features of autoimmunity. The pathogenesis of NCGS is also unclear and data obtained so far suggest a prevalent activation of innate immune responses [2].

We aimed at clarifying some aspects of NCGS pathogenesis using a gene array approach which we successfully used in the study of many immune-mediated diseases [6, 10–12].

In order to identify specific gene signatures typically associated with NCGS, we compared the gene expression profiles of 8 PBC samples obtained from individual NCGS patients with 10 PBC samples obtained from healthy age- and sex-matched donors. We observed that the disease has a profound impact on gene expression profiles since a large number of differentially expressed genes (DEGs) (1293, represented by 1521 modulated probe sets) complied with the Bonferroni-corrected value criterion () and the fold change criterion (FC ≥ 1.5) showing robust and statistically significant variation between healthy controls and NCGS samples. In particular, 695 and 598 genes resulted to be up- and downregulated, respectively (Additional Table 1).

DEGs were submitted to functional enrichment analysis according to terms of the Gene Ontology (GO) biological processes (BP) and canonical pathways. The most enriched biological process was “immune system” followed by “intracellular signal transduction” (Table 1). In addition, several enriched terms were related to the immune response gene category, including “leukocyte differentiation,” “leukocyte activation involved in immune response,” “T cell differentiation,” “neutrophil degranulation,” “adaptive immune response,” and “defense response.” Interestingly, we observed an enrichment in “cellular response to organic substance,” “cellular response to endogenous stimulus,” and “viral process.” The BP named “viral process” is defined by the Gene Ontology Consortium as a “multi-organism process in which a virus is a participant and the other participant is the host.” This term is related to the infection of a host cell, the replication of the viral genome, the viral transcription, and the assembly of progeny virus particles.

Table 1: Biological processes and pathways that were enriched in the NCGS dataset.

Biological processes
value

Immune system process
6.3 × 10⁻²⁶

Intracellular signal transduction
4.6 × 10⁻¹⁶

Cellular response to organic substance
1.5 × 10⁻¹³

Cell surface receptor signaling pathway
8.2 × 10⁻¹⁰

Leukocyte differentiation
6.3 × 10⁻⁹

Viral process
7.7 × 10⁻⁹

Leukocyte activation involved in immune response
8.0 × 10⁻⁸

Apoptotic process
2.2 × 10⁻⁶

Cellular response to endogenous stimulus
3.0 × 10⁻⁶

T cell differentiation
5.6 × 10⁻⁵

Neutrophil degranulation
5.6 × 10⁻⁵

Adaptive immune response
6.5 × 10⁻⁵

Defense response
6.8 × 10⁻⁵

Pathways
value

Inflammation mediated by chemokine and cytokine signaling pathway
2.1 × 10⁻⁷

Apoptosis signaling pathway
1.6 × 10⁻⁴

Angiogenesis
4.1 × 10⁻⁴

T cell activation
5.3 × 10⁻⁴

B cell activation
5.7 × 10⁻⁴

Integrin signaling pathway
7.8 × 10⁻⁴

EGF receptor signaling pathway
4.0 × 10⁻³

Toll like receptor signaling pathway
4.6 × 10⁻³

PI3 kinase pathway
7.6 × 10⁻³

Interleukin signaling pathway
8.1 × 10⁻³

JAK/STAT signaling pathway
1.6 × 10⁻²

Bonferroni corrected.

Pathway enrichment analysis showed that the most enriched signaling pathways were “inflammation mediated by chemokine and cytokine,” “apoptosis,” and “angiogenesis,” followed by “T cell activation” and “B cell activation” (Table 1). Other enriched pathways were: “integrin signaling,” “EGF receptor signaling,” “Toll-like receptor signaling,” “PI3 kinase,” “interleukin signaling,” and JAK/STAT signaling. Since the majority of the top-enriched functional classes and pathways were related to the immune system, we selected, within the entire data set, all modulated genes associated to the “Immune response” GO term to better characterize the immunological processes that are involved in NCGS pathogenesis. Although both innate and adaptive immunity play a crucial role in the development of CD, NCGS has been mainly associated with activation of the innate immune response [2].

It is therefore surprising to notice that both transcripts involved in the innate immune response as well as genes of the adaptive immune response were well represented in our dataset (Table 2).

Table 2: Genes modulated in NCGS patients that are involved in immune response and molecular signalings.

Probe set ID
value
Gene symbol
Gene title
FC
Representative public ID

T cell activation

203809_s_at
<0.001
AKT2
v-akt murine thymoma viral oncogene homolog 2
2.36
NM_001626

211861_x_at
<0.001
CD28
CD28 molecule
2.75
AF222343

205456_at
<0.001
CD3E
CD3e molecule, epsilon (CD3-TCR complex)
2.67
NM_000733

206804_at
<0.001
CD3G
CD3g molecule, gamma (CD3-TCR complex)
2.13
NM_000073

211027_s_at
<0.001
IKBKB
Inhibitor of kappa light polyp. gene enhancer in B cells, kinase β
2.69
NM_001190720

213281_at
0.007
JUN
jun proto-oncogene
3.07
NM_002228.3

204890_s_at
<0.001
LCK
Lymphocyte-specific protein tyrosine kinase
2.05
U07236

213490_s_at
<0.001
MAP2K2
Mitogen-activated protein kinase kinase 2
1.83
NM_030662

214786_at
0.013
MAP3K1
Mitogen-activated protein kinase kinase kinase 1
1.52
NM_005921

210671_x_at
<0.001
MAPK8
Mitogen-activated protein kinase 8
2.33
NM_001278548

211230_s_at
<0.001
PIK3CD
Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic sub. Δ
1.88
U57843

212249_at
0.002
PIK3R1
Phosphoinositide-3-kinase, regulatory subunit 1 (alpha)
2.76
NM_181523

216551_x_at
0.001
PLCG1
Phospholipase C, gamma 1
1.53
NM_002660

208640_at
<0.001
RAC1
Rho family, small GTP-binding protein Rac1
−1.92
NM_006908

207419_s_at
<0.001
RAC2
Rho family, small GTP-binding protein Rac2
2.31
NM_002872

217576_x_at
0.002
SOS2
Son of sevenless homolog 2
1.90
NM_006939

216042_at
<0.001
TNFRSF25
Tumor necrosis factor receptor superfamily, member 25
2.40
NM_148965

221331_x_at
<0.001
CTLA4
Cytotoxic T-lymphocyte-associated protein 4
2.26
NM_005214

206569_at
<0.001
IL24
Interleukin 24
2.84
NM_006850

203828_s_at
0.003
IL32
Interleukin 32
2.10
NM_004221

B cell mediated immune response

211027_s_at
<0.001
IKBKB
Inhibitor of kappa light polyp. gene enhancer in B cells, kinase β
2.69
NM_001190720

213281_at
0.007
JUN
jun proto-oncogene
3.07
NM_002228.3

202626_s_at
0.004
LYN
v-yes-1 Yamaguchi sarcoma viral related oncogene homolog
−2.22
AI356412

213490_s_at
<0.001
MAP2K2
Mitogen-activated protein kinase kinase 2
1.83
NM_030662

210671_x_at
<0.001
MAPK8
Mitogen-activated protein kinase 8
2.33
NM_001278548

211230_s_at
<0.001
PIK3CD
Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic sub. Δ
1.88
U57843

32540_at
<0.001
PPP3CC
Protein phosphatase 3, catalytic subunit, gamma isozyme
2.00
NM_001243975

208640_at
<0.001
RAC1
Rho family, small GTP-binding protein Rac1
−1.92
NM_006908

207419_s_at
<0.001
RAC2
Family, small GTP-binding protein Rac2
2.31
NM_002872

217576_x_at
0.002
SOS2
Son of sevenless homolog 2
1.90
NM_006939

207540_s_at
0.008
SYK
Spleen tyrosine kinase
−1.92
NM_003177

207224_s_at
0.003
SIGLEC7
Sialic acid-binding Ig-like lectin 7
−2.21
NM_016543

206150_at
0.004
CD27
CD27 molecule
1.96
NM_001242

214447_at
0.005
ETS1
v-ets erythroblastosis virus E26 oncogene homolog 1
2.25
NM_005238

201328_at
0.012
ETS2
v-ets erythroblastosis virus E26 oncogene homolog 2
−1.83
NM_005239

212420_at
0.001
ELF1
E74-like factor 1 (ets domain transcription factor)
1.94
KJ896761

211825_s_at
<0.001
FLI1
Friend leukemia virus integration 1
2.28
AF327066

215967_s_at
<0.001
LY9
Lymphocyte antigen 9
2.08
NM_002348

210690_at
0.001
KLRC4
Killer cell lectin-like receptor subfamily C, member 4
2.23
U96845

204116_at
<0.001
IL2RG
Interleukin 2 receptor, gamma
1.84
NM_000206

217489_s_at
<0.001
IL6R
Interleukin 6 receptor
1.79
S72848

204863_s_at
<0.001
IL6ST
Interleukin 6 signal transducer (gp130, oncostatin M receptor)
4.52
NM_002184

206966_s_at
<0.001
KLF12
Kruppel-like factor 12
1.83
AH010423

219878_s_at
<0.001
KLF13
Kruppel-like factor 13
1.89
NM_015995

219386_s_at
<0.001
SLAMF8
SLAM family member 8
−2.27
NM_020125

210405_x_at
0.003
TNFRSF10B
Tumor necrosis factor receptor superfamily, member 10b
1.50
NM_003842

219386_s_at
<0.001
SLAMF8
SLAM family member 8
−2.27
NM_020125

210405_x_at
0.003
TNFRSF10B
Tumor necrosis factor receptor superfamily, member 10b
1.50
NM_003842

203508_at
0.005
TNFRSF1B
Tumor necrosis factor receptor superfamily, member 1B
−2.06
NM_001066

216042_at
<0.001
TNFRSF25
Tumor necrosis factor receptor superfamily, member 25
2.40
NM_148965

218856_at
0.008
TNFRSF21
Tumor necrosis factor receptor superfamily, member 21
−1.68
NM_014452

206181_at
0.003
SLAMF1
Signaling lymphocytic activation molecule family member 1
1.65
NM_003037

210796_x_at
<0.001
SIGLEC6
Sialic acid-binding Ig-like lectin 6
1.58
D86359

211192_s_at
<0.001
CD84
CD84 molecule
2.55
AF054818

220132_s_at
<0.001
CLEC2D
C-type lectin domain family 2, member D
3.18
NM_013269

204773_at
0.005
IL11RA
Interleukin 11 receptor, alpha
1.56
AY532110

210850_s_at
<0.001
ELK1
ELK1, member of ETS oncogene family
1.60
AF000672

209894_at
0.002
LEPR
Leptin receptor
−2.10
U50748

203005_at
0.006
LTBR
Lymphotoxin beta receptor (TNFR superfamily, member 3)
−1.92
NM_002342

NK cell activation

220132_s_at
<0.001
CLEC2D
C-type lectin domain family 2, member D
3.18
NM_013269

203233_at
0.014
IL4R
Interleukin 4 receptor
1.50
NM_000418

210152_at
0.007
LILRB4
Leukocyte immunoglobulin-like receptor, subfamily B, member 4
−1.83
NM_001278426

210784_x_at
0.012
LILRA6
Leukocyte immunoglobulin-like receptor, subfamily A, member 6
−1.74
NM_024318

211405_x_at
0.003
IFNA17
Interferon, alpha 17
1.59
NM_021268

210660_at
0.008
LILRA1
Leukocyte immunoglobulin-like receptor, subfamily A, member 1
−2.81
NM_001278319

207857_at
0.016
LILRA2
Leukocyte immunoglobulin-like receptor, subfamily A, member 2
−2.35
NM_006866

210690_at
0.001
KLRC4
Killer cell lectin-like receptor subfamily C, member 4
2.23
U96845

206881_s_at
0.013
LILRA3
Leukocyte immunoglobulin-like receptor, subfamily A, member 3
−2.96
NM_006865

210313_at
0.003
LILRA4
Leukocyte immunoglobulin-like receptor, subfamily A, member 4
−1.83
NM_012276

215838_at
0.012
LILRA5
Leukocyte immunoglobulin-like receptor, subfamily A, member 5
−3.15
NM_181985

210146_x_at
0.004
LILRB2
Leukocyte immunoglobulin-like receptor, subfamily B, member 2
−3.41
AF004231

208982_at
0.008
PECAM1
Platelet/endothelial cell adhesion molecule 1
−1.73
M37780

203828_s_at
0.003
IL32
Interleukin 32
2.10
NM_004221

Macrophage activation

210405_x_at
0.003
TNFRSF10B
Tumor necrosis factor receptor superfamily, member 10b
1.50
NM_003842

221900_at
0.003
COL8A2
Collagen, type VIII, alpha 2
−1.62
NM_005202

205819_at
<0.001
MARCO
Macrophage receptor with collagenous structure
−3.01
NM_006770

208602_x_at
<0.001
CD6
CD6 molecule
3.67
NM_006725

207540_s_at
0.008
SYK
Spleen tyrosine kinase
−1.92
NM_003177

203508_at
0.005
TNFRSF1B
Tumor necrosis factor receptor superfamily, member 1B
−2.06
NM_001066

204438_at
0.012
MRC1
Mannose receptor, C type 1
−2.04
NM_002438

202269_x_at
0.006
GBP1
Guanylate binding protein 1, interferon-inducible
−1.77
NM_002053

208982_at
0.008
PECAM1
Platelet/endothelial cell adhesion molecule 1
−1.73
M37780

Complement activation

205500_at
0.006
C5
Complement component 5
−1.60
NM_001735

206244_at
0.003
CR1
Complement component (3b/4b) receptor 1 (Knops blood group)
−1.79
NM_000573

Response to gamma interferon

205831_at
0.001
CD2
CD2 molecule
2.00
NM_001767

205468_s_at
0.015
IRF5
Interferon regulatory factor 5
1.52
NM_032643

219386_s_at
<0.001
SLAMF8
SLAM family member 8
−2.27
NM_020125

211192_s_at
<0.001
CD84
CD84 molecule
2.55
AF054818

202269_x_at
0.006
GBP1
Guanylate-binding protein 1, interferon-inducible
−1.77
NM_002053

202621_at
<0.001
IRF3
Interferon regulatory factor 3
1.67
NM_001571

33148_at
<0.001
ZFR
Zinc finger RNA binding protein
2.19
NM_016107

206181_at
0.003
SLAMF1
Signaling lymphocytic activation molecule family member 1
1.65
NM_003037

215967_s_at
<0.001
LY9
Lymphocyte antigen 9
2.08
NM_002348

201461_s_at
0.011
MAPKAPK2
Mitogen-activated protein kinase-activated protein kinase 2
1.85
NM_004759

216450_x_at
<0.001
HSP90B1
Heat shock protein 90 kDa beta (Grp94), member 1
3.76
AK025862

214370_at
<0.001
S100A8
S100 calcium-binding protein A8
3.65
AW238654

Antigen processing and presentation

206050_s_at
0.010
RNH1
Ribonuclease/angiogenin inhibitor 1
−1.53
NM_002939

204770_at
<0.001
TAP2
Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)
1.86
NM_000544

TH17 related genes

203233_at
0.014
IL4R
Interleukin 4 receptor
1.50
NM_000418

204116_at
<0.001
IL2RG
Interleukin 2 receptor, gamma
1.84
NM_000206

204863_s_at
<0.001
IL6ST
Interleukin 6 signal transducer (gp130, oncostatin M receptor)
4.52
NM_002184

205067_at
0.015
IL1B
Interleukin 1, beta
1.52
NM_000576

205798_at
0.011
IL7R
Interleukin 7 receptor
1.55
NM_002185

201332_s_at
0.013
STAT6
Signal transducer and activator of transcription 6
1.54
AH006951

205026_at
<0.001
STAT5B
Signal transducer and activator of transcription 5B
1.60
NM_012448

206360_s_at
<0.001
SOCS3
Suppressor of cytokine signaling 3
1.83
NM_003955

209774_x_at
0.015
CXCL2
Chemokine (C-X-C motif) ligand 2
1.53
M57731

Type I interferon signaling

211405_x_at
0.003
IFNA17
Interferon, alpha 17
1.59
NM_021268

205468_s_at
0.015
IRF5
Interferon regulatory factor 5
1.52
NM_032643

202621_at
<0.001
IRF3
Interferon regulatory factor 3
1.67
NM_001571

217199_s_at
<0.001
STAT2
Signal transducer and activator of transcription 2, 113 kDa
1.59
S81491

M97935_5_at
<0.001
STAT1
Signal transducer and activator of transcription 1, 91 kDa
2.73
NM_007315

210370_s_at
<0.001
LY9
Lymphocyte antigen 9
2.05
NM_002348

In this regard, 14 genes involved in NK activity were modulated in NCGS samples (i.e., LILRA1, LILRA2, CLEC2D, and KLRC4). Moreover, several genes involved in macrophage activation were modulated in NCGS including TNFRSF10B, the ligand of the death receptors TRAIL that play important roles in set up both innate and adaptive immune responses against pathogens [13], and the scavenger receptors MRC1/CD206 [14] and MARCO, a member of the class A scavenger receptor family strongly upregulated in MΦ by various microbial stimuli in a TLR-dependent manner [15].

Noteworthy, 38 genes prevalently related to B cell activity (i.e., IL2RG, IL6R, KLF12, and CD27) were also modulated, indicating an important role for this cell subset in NCGS, 20 genes involved in T cell activation were upregulated in NCGS samples (i.e., CD28, CD3E, CD3G, and CTLA-4). Remarkably, Th17-lymphocyte-related genes and transcripts that can modulate Th17 cell development and functions were overexpressed including IL4R, IL2RG, IL6ST, IL1B, IL7R, STAT6, STAT5B, SOCS3, and CXCL2.

DEGs indicate therefore active involvement of both arms of the adaptive immune response (i.e., T and B cells response) and a prevalent upregulation of several Th17-related genes in the T cell response category. It is well known that Th17 cells play an important role in autoimmunity and have been implicated in the pathogenesis of psoriasis and in the amplification of inflammation in rheumatoid synovitis and in lupus nephritis [16–18].

In the NCGS dataset, 6 type I interferon inducible genes (IFIG) were upregulated (IFNA17, IRF5, IRF3, STAT2, STAT1, and LY9), thus indicating the presence of an IFN type I signature, typically associated with autoimmune disease such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Crohn’s disease, and Sjogren syndrome [19–25].

In this respect, it is well known that Th17 cells and related cytokines are crucial in promoting autoimmunity, in particular, when they act in synergy with type I IFN-driven inflammation. In the presence of IFN type I signature, CCR6⁺ memory T-helper cells producing IL-17A, IL-17F, IL-21, and/or IL-22 are increased in SLE, [26] indicating that, in the pathogenesis of systemic autoimmune diseases, IFN type I signature coacts with Th17 cells and related cytokines.

In order to further confirm our gene expression data on overexpression of IFIG and Th17 pathways, we analysed the presence of IL-17-producing CD4+ T cells and found a significantly () increased percentage of these cells in PBMC of patients with NCGS compared with normal subjects (Figure 1).

Figure 1: Flow cytometric analysis of CD4+T cells releasing IL-17 in patients with NCGS. Panel displays the mean percentage of CD4+T cells releasing IL-17 of 10 healthy donors and 8 NCGS patients. PBMCs were stimulated overnight with anti-CD3/-CD28-coated beads. value calculated with the Mann–Whitney nonparametric statistical test was 0.0159.

The analysis of genes modulated in gluten sensitivity was paralleled by the detection of some of the corresponding soluble mediators in the sera of NCGS patients. We analysed selected molecules that are widely recognized to be associated to an autoimmune response, including sCTLA-4, sPD-1, and sgp130/IL6ST. Figure 2 shows the concentration of these molecules in the sera of NCGS patients before and after gluten-free diet. The serum levels of all the molecules tested were significantly higher in NCGS before GFD than after GFD.

(a)

(b)

(c)

Figure 2: Serum levels of selected soluble mediators in NCGS patients and in normal subject sera. The histograms represent the mean of the results obtained in 20 healthy donors and in 16 NCGS patients. values calculated with the Wilcoxon nonparametric statistical test for paired samples were: for sCTLA-4, for sPD-1, and for sgp130.

In order to gain further insights into the molecular mechanisms relevant in NCGS pathogenesis, we constructed a protein-protein interaction (PPI) network starting from all the 1293 DEGs. The resulted PPI network contained 853 nodes and 3512 edges (Figure 3). By performing a modular analysis of the constructed PPI network, we were able to identify clusters of the most densely interconnected nodes (modules) and to extrapolate 15 main modules of genes displaying the highest degree of connection. Figure 4 shows a graphical representation of such modules, where the nodes represent proteins and the edges indicate their relations.

Figure 3: Protein-protein interaction (PPI) network of DEGs in NCGS patients.

Figure 4: Modules originated from the network analysis of DEGs in NCGS patients.

All modules were submitted to enrichment analysis to find enriched GO biological processes and pathways.

Among the 15 modules in particular, five (module 1, 3, 7, 10, and 14) showed a prevalent enrichment in BP and pathways associated to the activation of T cells. Similarly, “B cell activation” pathways were significantly enriched in modules 1, 9, 10, and 14. Interestingly, in modules 3, 10, and 11, we observed an enrichment in the JAK–STAT signaling pathway, which is highly relevant to human autoimmunity [27] and plays a role in the intestinal mucosal immune homeostasis as well as in intestinal epithelial repair and regeneration [28]. We also observed that module 11 contained several genes involved in Th-17 cell functions (i.e., IL2RG, IL4R, IL6ST, IL7R, SOCS3, STAT5B, and STAT6) and several IFIG, including IFNA17, STAT1, and STAT2. Other IFIG genes were ascribed to module 9 which also shows an enrichment in BPs associated to type I interferon signaling, including positive regulation of type I interferon production, positive regulation of interferon-beta production, and type I interferon biosynthetic process (Table 3).

Table 3: Biological processes and pathways enriched in the 15 modules.

Biological processes
value
Pathways
value

M0

Exocytosis
<0.001
None

Secretion by cell
<0.001

Secretion
<0.001

Vesicle-mediated transport
0.0018

Single-organism transport
0.0220

Single-organism localization
0.0308

M1

T cell receptor signaling pathway
<0.001
T cell activation
<0.001

Transmembrane receptor protein tyrosine kinase signaling pathway
<0.001
B cell activation
0.0012

T cell costimulation
<0.001
Cadherin signaling pathway
0.0056

Viral process
<0.001
Integrin signaling pathway
0.0081

Fc-gamma receptor signaling pathway involved in phagocytosis
<0.001

Peptidyl-tyrosine modification
0.0016

Adaptive immune response
0.0017

Positive regulation of antigen receptor-mediated signaling pathway
0.0029

Positive regulation of alpha-beta T cell proliferation
0.0038

Phosphatidylinositol phosphorylation
0.0060

Phosphatidylinositol-mediated signaling
0.0162

Positive regulation of calcium-mediated signaling
0.0192

T cell selection
0.0244

Leukocyte migration
0.0303

Interleukin-2-mediated signaling pathway
0.0324

MAPK cascade
0.0371

Positive regulation of immune effector process
0.0466

Positive regulation of defense response
0.0485

M2

mRNA export from nucleus
<0.001
None

Spliceosomal complex assembly
<0.001

Termination of RNA polymerase II transcription
<0.001

Regulation of mRNA splicing, via spliceosome
<0.001

Positive regulation of RNA splicing
<0.001

mRNA 3′-end processing
<0.001

Regulation of gene silencing by miRNA
<0.001

tRNA export from nucleus
0.0010

Viral gene expression
0.0054

Intracellular transport of virus
0.0078

Protein sumoylation
0.0294

Regulation of cellular response to heat
0.0310

Fibroblast growth factor receptor signaling pathway
0.0414

M3

Positive regulation of T cell activation
0.0035
T cell activation
<0.001

Interleukin-2-mediated signaling pathway
0.0065
Interleukin signaling pathway
<0.001

Interleukin-4-mediated signaling pathway
0.0065
PDGF signaling pathway
0.0010

Protein phosphorylation
0.0349
Integrin signaling pathway
0.0017

JAK/STAT signaling pathway
0.0057

Hypoxia response via HIF activation
0.0110

Insulin/IGF pathway-protein kinase B signaling cascade
0.0136

p53 pathway feedback loops 2
0.0176

PI3 kinase pathway
0.0182

VEGF signaling pathway
0.0238

Endothelin signaling pathway
0.0284

p53 pathway
0.0290

M4

Pospholipase C-activating G-protein-coupled receptor signaling pathway
<0.001
Heterotrimeric G-protein signal. pathway-Gq α and Go α med. pathway
<0.001

G-protein coupled acetylcholine receptor signaling pathway
<0.001

Activation of phospholipase C activity
<0.001
PI3 kinase pathway
<0.001

Positive regulation of cytosolic calcium ion concentration
<0.001
Endothelin signaling pathway
0.0013

Adenylate cyclase-modulating G-protein-coupled receptor signaling pathway
0.0048
Wnt signaling pathway
0.0015

M5

Translational initiation
<0.001
None

Nuclear-transcribed mRNA catabolic process, nonsense mediated decay
<0.001

SRP-dependent cotranslational protein targeting to membrane
<0.001

rRNA processing
<0.001

Ribosomal small subunit assembly
0.0083

M6

Regulation of small GTPase-mediated signal transduction
<0.001
None

Positive regulation of GTPase activity
<0.001

Small GTPase-mediated signal transduction
<0.001

Actin cytoskeleton organization
0.0108

M7

T cell costimulation
<0.001
T cell activation
<0.001

Phosphatidylinositol-mediated signaling
<0.001
Integrin signaling pathway
0.0041

T cell receptor signaling pathway
<0.001

Phosphatidylinositol phosphorylation
<0.001

Transmembrane receptor protein tyrosine kinase signaling pathway
0.0016

Peptidyl-tyrosine autophosphorylation
0.0033

Viral process
0.0035

Fc receptor signaling pathway
0.0050

Regulation of apoptotic process
0.0055

Leukocyte differentiation
0.0122

Leukocyte migration
0.0232

Lymphocyte activation
0.0237

B cell receptor signaling pathway
0.0256

Positive regulation of defense response
0.0340

M8

Response to unfolded protein
<0.001
None

Response to topologically incorrect protein
<0.001

Chaperone-mediated protein complex assembly
<0.001

Protein folding
<0.001

Protein transmembrane transport
<0.001

Response to stress
<0.001

M9

Activation of innate immune response
<0.001
Toll-like receptor signaling pathway
<0.001

Positive regulation of innate immune response
<0.001
Ras pathway
<0.001

Toll-like receptor signaling pathway
<0.001
Apoptosis signaling pathway
<0.001

Fc-epsilon receptor signaling pathway
0.0020
T cell activation
<0.001

MAPK cascade
0.0026
p38 MAPK pathway
<0.001

Positive regulation of type I interferon production
0.0029
Oxidative stress response
<0.001

Positive regulation of cytokine production
0.0035
Angiogenesis
<0.001

TRIF-dependent toll-like receptor signaling pathway
0.0136
B cell activation
<0.001

Positive regulation of interferon-beta production
0.0202
FGF signaling pathway
<0.001

Response to lipopolysaccharide
0.0268
EGF receptor signaling pathway
<0.001

Type I interferon biosynthetic process
0.0419
Integrin signaling pathway
0.0024

Inflammation mediated by chemokine and cytokine signaling pathway
0.0079

Interleukin signaling pathway
0.0104

M10

T cell receptor signaling pathway
<0.001
T cell activation
<0.001

T cell costimulation
<0.001
EGF receptor signaling pathway
<0.001

Fc-epsilon receptor signaling pathway
<0.001
Integrin signaling pathway
<0.001

phosphatidylinositol phosphorylation
<0.001
p53 pathway feedback loops 2
<0.001

Peptidyl-tyrosine autophosphorylation
<0.001
VEGF signaling pathway
<0.001

Fc-gamma receptor signaling pathway involved in phagocytosis
<0.001
B cell activation
<0.001

Leukocyte migration
<0.001
Ras pathway
<0.001

Growth hormone receptor signaling pathway
<0.001
Angiogenesis
<0.001

Regulation of defense response to virus
<0.001
Insulin/IGF pathway-protein kinase B signaling cascade
<0.001

Innate immune response
<0.001

Positive regulation of MAP kinase activity
<0.001
Inflammation mediated by chemokine and cytokine signaling pathway
<0.001

T cell differentiation
<0.001

Regulation of apoptotic process
<0.001
PI3 kinase pathway
<0.001

JAK–STAT cascade
0.0011
p53 pathway
<0.001

Positive regulation of immune effector process
0.0031
Interferon-gamma signaling pathway
<0.001

MAPK cascade
0.0056
FGF signaling pathway
<0.001

Adaptive immune response
0.0088
Endothelin signaling pathway
0.0101

B cell receptor signaling pathway
0.0121
JAK/STAT signaling pathway
0.0176

Phosphatidylinositol 3-kinase signaling
0.0214

Stimulatory C-type lectin receptor signaling pathway
0.0363

Innate immune response activ. cell surface receptor signal. pathway
0.0387

M11

Cellular response to cytokine stimulus
<0.001
JAK/STAT signaling pathway
<0.001

JAK–STAT cascade involved in growth hormone signaling pathway
<0.001
Interleukin signaling pathway
<0.001

Positive regulation of cytokine production
<0.001
PDGF signaling pathway
<0.001

Response to interleukin-2
<0.001
Interferon-gamma signaling pathway
<0.001

Positive regulation of T cell differentiation
<0.001
EGF receptor signaling pathway
<0.001

Positive regulation of tyrosine phosphorylation of STAT protein
<0.001
Integrin signaling pathway
<0.001

Regulation of interferon-gamma-mediated signaling pathway
<0.001
Inflammation mediated by chemokine and cytokine signaling pathway
<0.001

MAPK cascade
<0.001

Adaptive immune response
<0.001
p53 pathway feedback loops 2
0.0025

Innate immune response
0.0014
PI3 kinase pathway
0.0027

Positive regulation of T cell proliferation
0.0022
VEGF signaling pathway
0.0045

Positive regulation of inflammatory response
0.0025
B cell activation
0.0045

Antigen receptor-mediated signaling pathway
0.0072
Ras pathway
0.0050

T cell differentiation
0.0085
T cell activation
0.0078

Inflammatory response
0.0194
Cadherin signaling pathway
0.0201

Positive regulation of antigen receptor-mediated signaling pathway
0.0227

Transcription factor import into nucleus
0.0313

T cell costimulation
0.0396

M12

Inflammation mediated by chemokine and cytokine signaling pathway
<0.001

G-protein-coupled receptor signaling pathway
<0.001

Chemokine-mediated signaling pathway
<0.001
Heterotrimeric G-protein signaling pathway-Gi alpha and Gs alpha-mediated pathway
0.0473

Positive regulation of cytosolic calcium ion concentration
<0.001

Inflammatory response
<0.001

Cell chemotaxis
<0.001

Positive regulation of neutrophil chemotaxis
0.0136

Response to lipopolysaccharide
0.0268

M13

Adherens junction assembly
<0.001
Integrin signaling pathway
<0.001

Phosphatidylinositol phosphorylation
0.0015
Cadherin signaling pathway
<0.001

Vesicle-mediated transport
0.0026

Positive regulation of protein localization to nucleus
0.0043

Actin cytoskeleton organization
0.0105

Cell differentiation
0.0308

M14

Positive regulation of T cell activation
0.0035
T cell activation
<0.001

Interleukin-2-mediated signaling pathway
0.0065
Integrin signaling pathway
<0.001

Interleukin-4-mediated signaling pathway
0.0065
Angiogenesis
0.0032

Regulation of immune response
0.0430
Toll like receptor signaling pathway
0.0269

VEGF signaling pathway
0.0387

B cell activation
0.0387

Ras pathway
0.0431

Loss of the intestinal barrier integrity is a typical feature of CD and represents an important mechanism of autoimmunization through the passage of antigens across the intestinal epithelium [29]. However, Sapone et al. [29] have shown that NCGS patients have normal intestinal permeability when compared to CD patients, as assessed by the lactulose-mannitol test.

Indeed, in module 13, in which the most enriched BP was “adherent junction assembly,” we observed a reduced expression of molecules involved in cell adhesion including CDH1 (epithelial cadherin), CTNNA1, VCL, and CTTN, a molecule expressed on the apical surface of the polarized epithelium. In the same module, we also observed underexpression of Rac1, a critical regulator of intestinal epithelial barrier functions [30] and EGF, known to protect intestinal barrier integrity by stabilizing the microtubule cytoskeleton [31] and upregulation of FYN and PIK3R1, both involved in the signaling pathway by which IFNγ increases intestinal permeability [32].

The gene expression data would therefore indicate deregulation of adherent junctions and altered intestinal permeability also in NCGS, which seems to be in contrast with the data of Sapone et al. Nevertheless, it is important to point out that the lactulose-mannitol test may not be sensitive enough to detect mild alterations of the intestinal barrier function in patients with NCGS.

In module 12, the most enriched pathway was “inflammation mediated by chemokine and cytokine signaling”; this pathway was also enriched in modules 9, 10, and 11, which is consistent with inflammatory/autoimmune origin of NCGS.

Moreover, modules 1, 2, 7, and 10 were enriched in BPs related to viral infection including “viral process,” “viral gene expression,” “intracellular transport of virus,” and “regulation of defense response to virus.”

In addition, we observed that modules 10 and 11 showed enrichments in the gamma interferon pathways typically associated to the innate response to viruses [33].

Therefore, to further clarify the relationship between viral infections and NCGS, we searched in the IPA software database to find all diseases that are most likely to be statistically significantly associated to the genes modulated in the NCGS dataset. We found that, in the resulting list of most significantly associated diseases, “Infectious diseases” ranked first and, among these, “Viral infection” showed the best statistical p value (Figure 5(a)). Moreover, we could find a cluster of 134 DEGs that, in our NCGS dataset, showed a modulation that was consistent with a process of viral infection (Figure 5(b)). Based on these data, we aimed at investigating whether rotavirus, known to be linked to CD, [5, 6, 34] could also play a role in NCGS.

(a)

(b)

(c)

Figure 5: (a) List of diseases which are most likely to be statistically significantly associated and compatible with the transcriptional profile observed in NCGS. (b) DEGs in NCGS showing a modulation consistent with a viral infection process. (c) Detection of antibodies directed against the rotavirus VP7 peptide in the sera of patients with NCGS. Each circle represents a measurement for one patient, and the dashed horizontal line indicates the threshold for positivity (O.D. 0.140). The statistical value was calculated with the Mann–Whitney test ().

In the second part of our study, we made a comparison between the dataset obtained from our previous analysis of intestinal human T84 cells treated with anti-VP7 antibodies (that we indicate in this paper as “T84 dataset”) and genes modulated in NCGS. We found that 529 genes modulated in NCGS (accounting for the 41% of genes modulated in this dataset) were also modulated in treated T84 cells. Interestingly, several DEGs that were shared by the two datasets are involved in BP that may be related to the pathogenesis of celiac disease, including apoptosis, inflammatory and immune response, cell proliferation, cell differentiation, cell junctions, matrix metalloproteases, receptors and signal transducers, cytoskeleton components, ion transport and exchange, and EGF receptor pathway. Table 4 shows a selection of genes ascribed to the abovementioned functional classes. As a whole in NCGS dataset, the modulation of genes ascribed to the abovementioned categories indicated an upregulation of apoptotic genes accompanied by a downregulation of genes involved in cell differentiation and an increased transcription of proliferative genes. All these observation are in agreement with what we described on human T84 cells treated with antirotavirus Vp7 peptide antibodies and are related to the typical features of celiac disease. Indeed in CD, an increased apoptosis is the main cause of villous atrophy that is also sustained by a dysregulation of cell differentiation [35]. Moreover, it has been observed that the increase of intestinal cell proliferation leads to crypt hyperplasia seen in celiac disease [35]. Other aspects of CD previously observed in our T84 treated cells, that are paralleled by the gene modulated observed in NCGS, are the upregulation of members of the epidermal growth factor receptor (EGFR) signaling pathway and the concomitant downregulation of cell adhesion molecules beside a deregulation of ion transport. Noteworthy, the activation of EGFR signaling has been already observed in CD [36], and dysfunction of cell adhesion and transport are typical features of epithelial cells from active CD [37].

Table 4: Selection of DEGs in NCGS that are also modulated in human T84 cells after stimulation with anti-VP7 rotavirus peptide antibodies.

Gene symbol
Accession number
Gene title
FC NCGS PBCs
FC T84 treated cells

Apoptosis

SOCS3
NM_003955
Suppressor of cytokine signaling 3
1.83
2.75

ANXA6
NM_001155
Annexin A6
1.57
2.72

SOS2
NM_006939
Son of sevenless homolog 2 (Drosophila)
1.90
1.75

DEDD
AF064605
Death effector domain containing
1.78
1.47

Immune response

IFNA17
NM_021268
Interferon, alpha 17
1.59
1.56

IL6R
S72848
Interleukin 6 receptor
1.79
2.76

IRF5
NM_03264335
Interferon regulatory factor 5
1.52
1.52

CD84
AF054818
CD84 molecule
2.55
3.40

Inflammatory response

IL1B
NM_000576
Interleukin 1, beta
1.52
1.80

IL24
NM_006850
Interleukin 24
2.84
2.19

IL2RA
K03122
Interleukin 2 receptor, alpha
1.86
1.48

S100A8
AW238654
S100 calcium-binding protein A8
3.65
1.86

Cell proliferation

FGFR2
NM_022975
Fibroblast growth factor receptor 2
1.56
2.89

RAC2
NM_002872
Ras-related C3 botulinum toxin substrate 2
2.31
1.53

CDK2
AB012305
Cyclin-dependent kinase 2
1.63
1.78

DLG1
AL121981
Discs, large homolog 1 (Drosophila)
1.57
1.74

Cell differentiation

GAS7
BC006454
Growth arrest-specific 7
−2.03
−1.90

SRD5A1
NM_001047
Steroid-5-alpha-reductase, alpha polypeptide 1
−2.53
−1.54

VAMP5
NM_006634
Vesicle-associated membrane protein 5
−1.71
−1.58

ZAK
NM_016653
Sterile alpha motif and leucine zipper containing kinase AZK
−2.02
−1.71

Cell–cell junctions

VCL
NM_014000
Vinculin
−1.68
−1.56

CTNND1
NM_001331
Catenin (cadherin-associated protein), delta 1
−2.33
−1.75

CTNNA1
NM_001903
Catenin (cadherin-associated protein), alpha 1, 102 kDa
−2.49
−1.57

COL8A2
NM_005202
Collagen, type VIII, alpha 2
−1.62
−1.64

Metalloproteases

ADAM8
AI814527
ADAM metallopeptidase domain 8
1.94
1.57

ADAM9
NM_003816
ADAM metallopeptidase domain 9
2.81
1.48

ADAM17
AI797833
ADAM metallopeptidase domain 17
1.51
1.56

Receptors and signal transduction

IL2RA
K03122
Interleukin 2 receptor, alpha
1.86
1.48

IRF5
NM_03264335
Interferon regulatory factor 5
1.52
1.52

IL6R
S72848
Interleukin 6 receptor
1.79
2.76

Cytoskeleton

FGD6
NM_018351
FYVE, RhoGEF, and pH domain containing 6
−2.40
−1.48

ABLIM3
NM_014945
Actin-binding LIM protein family, member 3
1.86
1.49

PFN2
NM_002628
Profilin 2
1.51
1.47

Ion transport

SLC24A1
AF026132
Solute carrier family 24 (Na/K/Ca exchanger), member 1
1.59
1.95

SLC30A1
AI972416
Solute carrier family 30 (zinc transporter), member 1
1.94
1.55

SLC4A4
AF069510
Solute carrier family 4, NaHCO₃ cotransporter, member 4
1.52
1.92

EGFR signaling pathway

AKT2
NM_001626
v-akt murine thymoma viral oncogene homolog 2
2.36
2.19

PIK3R1
NM_181523
Phosphoinositide-3-kinase, regulatory subunit 1 (alpha)
2.76
1.54

PTPN12
S69182
Protein tyrosine phosphatase, nonreceptor type 12
2.27
1.50

In this regard, it is worthwhile mentioning that patients with NCGS have normal to mildly inflamed mucosa (Marsh 0-1), while partial or subtotal villous atrophy and crypt hyperplasia are hallmarks of CD. Nevertheless, we cannot exclude that some NCGS patients, especially those positive for HLA-DQ2 and/or DQ8, may switch to classical CD in the course of the follow-up.

Since a large part of DEGs in the NCGS paralleled the modulation of genes seen in human T84 cells treated with antirotavirus Vp7 peptide antibodies, we next aimed at identifying the presence of such antibodies in sera of NCGS patients. We therefore tested in ELISA assay the sera from 16 NCGS patients and 20 healthy subjects for the detection of antirotavirus VP7 peptide antibodies. We found that these antibodies were present in 6 out of 16 (37%) NCGS patients while were not detected in the sera of healthy subjects. Figure 5(c) shows that the levels of such antibodies are significantly different in the two set of tested samples (). The detection of these antibodies in NCGS patients may be relevant to the pathogenesis of the NCGS given their ability to modulate sets of genes in intestinal epithelial cells as we previously demonstrated [6].

Taken together, the modulation of highly connected genes associated to the viral infection process and the presence of anti-VP7 antibodies in the sera of some NCGS patients may suggest that a link also exists between immune response to rotavirus infection and NCGS.

In this perspective, since anti-VP7 rotavirus antibodies are often present before the onset of CD, preceding the detection of celiac specific autoantibodies, [6] it is tempting to speculate that NCGS patients with CD genetic predisposition (DQ2/DQ8) and presence of anti-VP7 antibodies may develop CD in the course of the follow-up.

Therefore, to further clarify the relationship between rotavirus infection and NCGS, we decided to perform an integrative bioinformatics analysis using the dataset GSE50628 downloaded from GEO (Gene Expression Omnibus) database (http://www.ncbi.nlm.nih.gov/geo/) that included samples of peripheral blood cells from patients affected by acute rotavirus infection (named in the paper “Rotavirus infection dataset”). This dataset was analysed to detect significantly modulated genes (Additional Table 2), and a comprehensive GO analysis was carried out on all datasets including NCGS, Rotavirus infection, and T84 datasets that we analysed in our previous work [6].

We then searched on the four datasets for the presence of genes associated to GO terms containing the words “virus” and/or “viral” and we found in all datasets a great number of such terms to which modulated genes were connected/related.

The searched terms explored a wide range of biological processes associated to viral infection from the entry of virus in the host cell, viral transcription and gene expression, modulation of host physiology by virus to cellular response to virus.

All the GO terms retrieved in the three datasets are listed in Additional Table 3.

Table 5 shows selected genes modulated in the three datasets that are ascribed to the most representative GO terms, including viral transcription, viral gene expression, response to virus, viral genome replication, and viral life cycle.

Table 5: Genes modulated in the three datasets playing a role in selected GO BPs related to the viral infection process.

Gene symbol
Gene title
FC

NCGS dataset

Viral transcription/gene expression

        RANBP2
RAN-binding protein 2
1.98

        RPL27A
Ribosomal protein L27a
3.22

        RPL37A
Ribosomal protein L37a
2.94

        RPLP2
Ribosomal protein, large, P2
2.15

        RPS10
Ribosomal protein S10
2.66

        RPS11
Ribosomal protein S11
2.49

        TPR
Translocated promoter region, nuclear basket protein
4.40

Response to virus

        RELA
v-rel reticuloendotheliosis viral oncogene homolog A (avian)
1.54

        IKBKB
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase beta
2.69

        IRF5
Interferon regulatory factor 5
1.52

        IFNA17
Interferon, alpha 17
1.59

        DDX3X
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked
3.22

        STAT2
Signal transducer and activator of transcription 2, 113 kDa
1.59

        STAT1
Signal transducer and activator of transcription 1, 91 kDa
2.73

        IRF3
Interferon regulatory factor 3
1.67

        DDX17
DEAD (Asp-Glu-Ala-Asp) box helicase 17
4.75

Viral life cycle

        TPR
Translocated promoter region, nuclear basket protein
4.40

        ATG16L1
Autophagy-related 16-like 1 (S. cerevisiae)
1.87

        HSP90AB1
Heat shock protein 90 kDa alpha (cytosolic), class B member 1
1.87

        RANBP2
RAN-binding protein 2
1.98

        DPP4
Dipeptidyl-peptidase 4
1.61

        DDX6
DEAD (Asp-Glu-Ala-Asp) box helicase 6
4.70

        HTATSF1
HIV-1 Tat specific factor 1
2.23

        SLAMF1
Signaling lymphocytic activation molecule family member 1
1.65

        T84 dataset

Viral transcription/gene expression

        RPL27A
Ribosomal protein L27a
1.68

        RPS2
Ribosomal protein S2
1.99

        RPS6
Ribosomal protein S6
1.51

Response to virus

        IFIH1
Interferon induced with helicase C domain 1
1.52

        IFNA7
Interferon, alpha 7
1.53

        IFIT3
Interferon-induced protein with tetratricopeptide repeats 3
1.46

        IFNA4
Interferon, alpha 4
1.73

        IFI44
Interferon-induced protein 44
1.46

        IFNGR1
Interferon gamma receptor 1
1.67

        IFNA17
Interferon, alpha 17
1.56

Viral life cycle

        CTBP1
C-terminal-binding protein 1
1.58

        ADRBK1
Adrenergic, beta, receptor kinase 1
1.46

        HCRP1
Hepatocellular carcinoma-related HCRP1
1.61

        C9Orf28
Chromosome 9 open reading frame 28
1.56

Rotavirus infection dataset

Viral transcription/gene expression

        NUP58
Nucleoporin 58 kDa
6.38

        RPS16
Ribosomal protein S16
2.10

        DENR
Density-regulated protein
2.11

Response to virus

        XPR1
Xenotropic and polytropic retrovirus receptor 1
1.72

        CNOT7
CCR4-NOT transcription complex subunit 7
3.54

        CD40
CD40 molecule, TNF receptor superfamily member 5
2.72

        ITCH
Itchy E3 ubiquitin protein ligase
2.26

        ARF1
ADP-ribosylation factor 1
1.91

        BCL2L11
BCL2-like 11 (apoptosis facilitator)
3.21

        BCL2L1
BCL2-like 1
3.37

        IKBKE
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase ɛ
1.50

        DDX17
DEAD (Asp-Glu-Ala-Asp) box helicase 17
2.13

Viral life cycle

        NUP153
Nucleoporin 153 kDa
2.01

        VPS37A
Vacuolar protein sorting 37 homolog A (S. cerevisiae)
1.90

        XPR1
Xenotropic and polytropic retrovirus receptor 1
1.72

        UBB
Ubiquitin B
1.75

        ITCH
Itchy E3 ubiquitin protein ligase
2.26

        NUP58
Nucleoporin 58 kDa
6.38

        TNFRSF4
Tumor necrosis factor receptor superfamily, member 4
1.94

        SCARB2
Scavenger receptor class B, member 2
1.96

Moreover, the GO analysis of the abovementioned datasets was complemented by searching for transcripts involved in immune response.

In the “T84 dataset,” we found upregulation for the T cell costimulatory molecule ICOSLG, the transcriptional regulator that is crucial for the development and inhibitory function of regulatory T cells, [38] interleukin-6 that is pivotal for the development of Th-17 cells [39], and FCGR2B that is involved in the phagocytosis of immune complexes and in modulation of antibody production by B cells [40] (Table 6).

Table 6: Selection of genes modulated in human T84 cells after stimulation with anti-VP7 rotavirus peptide antibodies, involved in immune response and in molecular signaling related to the viral infection process.

Gene symbol
Gene title
FC

Immune response

CCR2
Chemokine (C-C motif) receptor 2
−1.48

CXCL1
Chemokine (C-X-C motif) ligand 1
1.81

CXCL13
Chemokine (C-X-C motif) ligand 13
−5.52

GATA3
GATA-binding protein 3
−6.62

TROVE2
TROVE domain family, member 2
−1.64

ICOSLG
Inducible T cell costimulator ligand
2.51

FCGR1A
Fc fragment of IgG, high affinity Ia, receptor (CD64)
2.00

FOXP3
Forkhead box P3
1.49

ULBP1
UL16-binding protein 1
−1.77

ITGA4
Integrin, alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 receptor)
1.48

CXCL9
Chemokine (C-X-C motif) ligand 9
1.59

CSF3
Colony-stimulating factor 3 (granulocyte)
1.46

IL6
Interleukin 6 (interferon, beta 2)
1.51

CD84
CD84 molecule
3.40

FCGR2B
Fc fragment of IgG, low affinity IIb, receptor (CD32)
1.77

LAT2
Linker for activation of T cells family, member 2
1.85

C7
Complement component 7
3.11

CCR1
Chemokine (C-C motif) receptor 1
3.27

CCR3
Chemokine (C-C motif) receptor 3
2.80

CFP
Complement factor properdin
2.92

IL24
Interleukin 24
2.19

IL8
Interleukin 8
1.86

CXCL10
Chemokine (C-X-C motif) ligand 10
1.82

IL1F7
Interleukin 1 family, member 7 (zeta)
−2.26

IKBKB
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase beta
−2.25

CCL11
Chemokine (C-C motif) ligand 11
1.96

Type I interferon signaling

Cellular response to interferon alpha

    FCAR
Fc fragment of IgA, receptor for
2.15

Type I interferon signaling

    IFNA16
Interferon, alpha 16
1.58

    STAT1
Signal transducer and activator of transcription 1, 91 kDa
−1.46

    IFNA17
Interferon, alpha 17
1.56

    YY1
YY1 transcription factor
−2.24

    IFNA4
Interferon, alpha 4
1.73

    IRF8
Interferon regulatory factor 8 interferon regulatory factor 8
−1.68

    IFNA5
Interferon, alpha 5
−2.85

    IRF2
Interferon regulatory factor 2
1.58

    IFNA8
Interferon, alpha 8
2.23

    IRF5
Interferon regulatory factor 5
1.52

    IFI6
Interferon, alpha-inducible protein 6
1.56

    IFNA6
Interferon, alpha 6
2.08

Positive regulation of interferon alpha production

    IRF5
Interferon regulatory factor 5
1.52

Positive regulation of interferon beta production

    DDX3X
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked
−1.49

    IRF5
Interferon regulatory factor 5
1.52

Negative regulation of interferon beta production

    LILRB1
Leukocyte immunoglobulin-like receptor, subfamily B, member 1
−1.60

Positive regulation of Type I interferon production

    IFI16
Interferon, gamma-inducible protein 16
−1.68

    CREBBP
CREB-binding protein (Rubinstein-Taybi syndrome)
1.51

Negative regulation of Type I interferon production

    CYLD
Cylindromatosis (turban tumor syndrome)
−3.04

Gamma interferon signaling

Cellular response to Interferon Gamma signaling

    FCAR
Fc fragment of IgA, receptor for
2.15

    MRC1
Mannose receptor, C type 1
2.52

    SYNCRIP
Synaptotagmin-binding, cytoplasmic RNA-interacting protein
−1.69

    CCL8
chemokine (C-C motif) ligand 8
1.63

Interferon gamma signaling

    STAT1
Signal transducer and activator of transcription 1, 91 kDa
−1.46

    MID1
Midline 1 (Opitz/BBB syndrome)
−1.99

    HLA-DRB4
Major histocompatibility complex, class II, DR beta 4
2.39

    SDK1
Sidekick homolog 1 (chicken)
1.61

    IFNGR1
Interferon gamma receptor 1 interferon gamma receptor 1
1.67

Negative regulation of gamma interferon production

    LILRB1
Leukocyte immunoglobulin-like receptor, subfamily B, member 1
−1.60

    CD244
CD244 molecule, natural killer cell receptor 2B4
−1.69

    IL10
Interleukin 10
−3.56

Positive regulation of gamma interferon production

    FOXP3
Forkhead box P3
1.49

    IL1B
Interleukin 1, beta
1.80

Toll-like receptor signaling

TANK
TRAF family member-associated NFKB activator
−1.91

CHUK
Conserved helix-loop-helix ubiquitous kinase
−1.72

ELK1
ELK1, member of ETS oncogene family
3.70

MAP3K8
Mitogen-activated protein kinase kinase kinase 8
−2.16

TLR6
Toll-like receptor 6
2.43

TLR1
Toll-like receptor 1
1.57

TLR7
Toll-like receptor 7
−1.64

MAP3K7
Mitogen-activated protein kinase kinase kinase 7
−1.89

LY96
Lymphocyte antigen 96
−1.81

NFKB2
Nuclear factor of kappa light polypeptide gene enhancer in B cells 2 (p49/p100)
1.54

REL
v-rel reticuloendotheliosis viral oncogene homolog (avian)
−1.82

PTGS2
Prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)
1.76

TNFAIP3
Tumor necrosis factor, alpha-induced protein 3
1.73

MAP2K3
Mitogen-activated protein kinase kinase 3
1.59

IKBKB
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase beta
−2.25

TLR3
Toll-like receptor 3
−2.03

IFNB1
Interferon, beta 1, fibroblast
−1.84

IRAK3
Interleukin-1 receptor-associated kinase 3
1.70

TLR4
Toll-like receptor 4
1.46

IKBKE
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase epsilon
2.06

MAP2K2
Mitogen-activated protein kinase kinase 2
1.98

TLR2
Toll-like receptor 2
−2.30

In the “Rotavirus infection” dataset, we found upregulation for molecules that are crucial in the immune response including the BLK gene, involved in transmitting signals through surface immunoglobulins, supporting the pro-B to pre-B transition, [41] MR1/MAIT playing a role in the development of the mucosal-associated invariant T cells (MAIT), [42] TNFRSF4 involved in T cell proliferation [43], and HCST/DAP10 playing a role in triggering cytotoxicity against both stressed and infected by virus target cells [44] (Table 7).

Table 7: Selection of genes modulated in PBCs in course of the acute phase of rotavirus infection, involved in immune response and in molecular signaling related to the viral infection process.

Gene symbol
Gene title
FC

Immune response

ADGRE3
Adhesion G protein-coupled receptor E3;ADGRE3;ortholog
−3.35

ADIPOQ
Adiponectin, C1Q and collagen domain containing
−1.60

BLK
BLK proto-oncogene, Src family tyrosine kinase
1.72

BRAF
B-Raf proto-oncogene, serine/threonine kinase
1.57

BTK
Bruton agammaglobulinemia tyrosine kinase
−1.59

C1QTNF9
C1q and tumor necrosis factor related protein 9
−1.69

CD109
CD109 molecule
−1.84

CD79B
CD79b molecule, immunoglobulin-associated beta
−1.67

CLEC7A
C-type lectin domain family 7, member A
−1.62

CMIP
c-Maf inducing protein
−3.50

CSF2RA
Colony-stimulating factor 2 receptor, alpha, low-affinity (granulocyte-macrophage)
−2.30

CXCL2
Chemokine (C-X-C motif) ligand 2
−3.76

CXCL8
Chemokine (C-X-C motif) ligand 8
−8.65

FCER1A
Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide
−5.64

HCST
Hematopoietic cell signal transducer
1.85

IL18BP
Interleukin 18 binding protein
1.56

JAG1
Jagged 1
−2.06

KLRB1
Killer cell lectin-like receptor subfamily B, member 1
−5.99

MAP3K11
Mitogen-activated protein kinase kinase kinase 11
1.55

MASP1
Mannan-binding lectin serine peptidase 1
−1.50

MR1
Major histocompatibility complex, class I-related
8.43

PLEKHN1
Pleckstrin homology domain containing, family N member 1
−1.99

PPP2R2C
Protein phosphatase 2, regulatory subunit B, gamma
−1.84

PPP3CA
Protein phosphatase 3, catalytic subunit, alpha isozyme
−1.89

PSME3
Proteasome activator subunit 3
1.91

PVR
Poliovirus receptor
−1.59

STAT5B
Signal transducer and activator of transcription 5B
−2.06

TNFRSF10C
Tumor necrosis factor receptor superfamily, member 10c decoy without an intracellular domain
−1.75

TNFRSF4
Tumor necrosis factor receptor superfamily, member 4
1.94

Type I interferon signaling

Positive regulation of Type I interferon production

    EP300
E1A-binding protein p300
−1.56

    POLR3G
Polymerase (RNA) III (DNA directed) polypeptide G (32kD)
−1.98

    CREBBP
CREB-binding protein
−1.83

    LRRFIP1
Leucine rich repeat (in FLII) interacting protein 1
−2.14

    SOCS1
Suppressor of cytokine signaling 1
2.32

Negative regulation of Type I interferon production

    UBB
Ubiquitin B
1.75

    ITCH
Itchy E3 ubiquitin protein ligase
2.26

    TAX1BP1
Tax1 (human T cell leukemia virus type I) binding protein 1
−4.01

Negative regulation of Type I interferon pathway

    PTPN2
Protein tyrosine phosphatase, nonreceptor type 2
2.10

Positive regulation of interferon beta production

    ZBTB20
Zinc finger and BTB domain containing 20
4.27

Negative regulation of interferon Beta production

    PTPRS
Protein tyrosine phosphatase, receptor type, S
−2.00

    CACTIN
Cactin, spliceosome C complex subunit
−2.53

Cellular response to interferon alpha

    TPR
Translocated promoter region, nuclear basket protein
−2.55

Negative regulation of interferon alpha production

    PTPRS
Protein tyrosine phosphatase, receptor type, S
−2.00

Type I interferon signaling pathway

    JAK1
Janus kinase 1
1.79

    IFI27
Interferon, alpha-inducible protein 27
75.26

    IFI27L2
Interferon, alpha-inducible protein 27-like 2
−1.59

    IKBKE
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase epsilon
1.50

    TPR
Translocated promoter region, nuclear basket protein
−2.55

Positive regulation of Type I interferon pathway

    MME
Membrane metallo-endopeptidase
−5.54

Gamma interferon signaling

JAK1
Janus kinase 1
1.79

HLADQB1
Major histocompatibility complex, class II, DQ beta 1
−36.43

HLADQA1
Major histocompatibility complex, class II, DQ alpha 1
−37.95

PIAS3
Protein inhibitor of activated STAT 3
2.11

HLADRB1
Major histocompatibility complex, class II, DR beta 1
−10.91

MAPK8
Mitogen-activated protein kinase 8
−2.28

MAPK1
Mitogen-activated protein kinase 1
−2.23

Regulation of interferon gamma signaling pathway

    PTPN2
Protein tyrosine phosphatase, nonreceptor type 2
2.10

Positive regulation of interferon gamma production

    PDE4B
Phosphodiesterase 4B, cAMP-specific
−1.77

    ZFPM1
Zinc finger protein, FOG family member 1
1.75

Negative regulation of interferon gamma production

    HLADRB1
Major histocompatibility complex, class II, DR beta 1
−10.91

    RARA
Retinoic acid receptor, alpha
−7.60

    FOXP3
Forkhead box P3
−2.14

Cellular response to interferon gamma

    SLC26A6
Solute carrier family 26 (anion exchanger), member 6
1.95

    DAPK3
Death-associated protein kinase 3
−1.62

    CD40
CD40 molecule, TNF receptor superfamily member 5
2.72

    MEFV
Mediterranean fever
−4.83

    SNCA
Synuclein alpha
8.62

    MRC1
Mannose receptor, C type 1
−1.93

Toll-like receptors signaling pathway

TANK
TRAF family member-associated NFKB activator
−1.91

NFKBIA
Nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor, alpha
−1.76

MAPK8
Mitogen-activated protein kinase 8
−2.28

IKBKE
Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase epsilon
1.50

MAPK1
Mitogen-activated protein kinase 1
−2.23

JUN
jun proto-oncogene
−1.91

Interestingly, in all the datasets, we found the presence of modulated genes involved in the type I interferon signaling, that is central in autoimmunity, and in molecular pathways involved in the immune response to viral infection, including the Toll-like receptors, and the type I and gamma interferon pathways (see Tables 2, 6, and 7).

Taken together, our data seem to indicate that NCGS has features of autoimmunity and that an immune response to rotavirus may play a role in some cases of NCGS.

4. Conclusions

NCGS is an emerging new clinical entity lacking specific diagnostic biomarkers which has been reported to occur in 6–10% of the population. Interestingly, up to 50% of these patients carry HLA-DQ2 and/or HLA-DQ8 genes. NCGS patients may complain gastrointestinal (e.g., diarrhoea/constipation, abdominal pain, bloating) and/or extraintestinal symptoms (“foggy mind,” headache, dermatitis, etc.) which recede with GFD. The pathogenesis of NCGS is still unclear and the data, so far obtained, suggest a predominant activation of the innate immune responses.

Our data indicate a concomitant involvement of the adaptive immune system and suggest that NCGS may have an autoimmune origin. This is based both on gene expression data (i.e., TH17-IFNA I signatures) and on the presence of TH17 cells and of serological markers of autoimmunity in NCGS. Our results also indicate a possible involvement of rotavirus infection in the pathogenesis of NCGS, similarly to what we have previously shown in CD.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Authors’ Contributions

Antonio Puccetti, Daniele Saverino, Roberta Opri, Claudio Lunardi, and Marzia Dolcino equally contributed to this paper.

Supplementary Materials

Supplementary 1. Additional Table 1: genes modulated in NGCS samples versus healthy controls.

Supplementary 2. Additional Table 2: genes modulated in the “Rotavirus infection” dataset.

Supplementary 3. Additional Table 3: GO terms containing the words “virus” and “viral” to which are associated genes modulated in the three datasets.

References

U. Volta and R. De Giorgio, “New understanding of gluten sensitivity,” Nature Reviews Gastroenterology & Hepatology, vol. 9, no. 5, pp. 295–299, 2012.

G. Caio, G. Riegler, M. Patturelli, A. Facchiano, L. de Magistris, and A. Sapone, “Pathophysiology of non-celiac gluten sensitivity: where are we now?” Minerva Gastroenterologica e Dietologica, vol. 63, no. 1, pp. 16–21, 2017.

P. H. Dennehy, “Rotavirus infection: a disease of the past?” Infectious Disease Clinics of North America, vol. 29, no. 4, pp. 617–635, 2015.

J. B. Pesavento, S. E. Crawford, M. K. Estes, and B. V. Prasad, “Rotavirus proteins: structure and assembly,” Current Topics in Microbiology and Immunology, vol. 309, pp. 189–219, 2006.

G. Zanoni, R. Navone, C. Lunardi et al., “In celiac disease, a subset of autoantibodies against transglutaminase binds toll-like receptor 4 and induces activation of monocytes,” PLoS Medicine, vol. 3, no. 9, article e358, 2006.

M. Dolcino, G. Zanoni, C. Bason et al., “A subset of anti-rotavirus antibodies directed against the viral protein vp7 predicts the onset of celiac disease and induces typical features of the disease in the intestinal epithelial cell line t84,” Immunologic Research, vol. 56, no. 2-3, pp. 465–476, 2013.

C. Lunardi, C. Bason, R. Navone et al., “Systemic sclerosis immunoglobulin g autoantibodies bind the human cytomegalovirus late protein ul94 and induce apoptosis in human endothelial cells,” Nature Medicine, vol. 6, no. 10, pp. 1183–1186, 2000.

M. S. Cline, M. Smoot, E. Cerami et al., “Integration of biological networks and gene expression data using cytoscape,” Nature Protocols, vol. 2, no. 10, pp. 2366–2382, 2007.

G. D. Bader and C. W. Hogue, “An automated method for finding molecular complexes in large protein interaction networks,” BMC Bioinformatics, vol. 4, no. 1, p. 2, 2003.

M. Dolcino, A. Ottria, A. Barbieri et al., “Gene expression profiling in peripheral blood cells and synovial membranes of patients with psoriatic arthritis,” PLoS One, vol. 10, no. 6, article e0128262, 2015.

M. Dolcino, G. Patuzzo, A. Barbieri et al., “Gene expression profiling in peripheral blood mononuclear cells of patients with common variable immunodeficiency: modulation of adaptive immune response following intravenous immunoglobulin therapy,” PLoS One, vol. 9, no. 5, article e97571, 2014.

C. Lunardi, M. Dolcino, D. Peterlana et al., “Antibodies against human cytomegalovirus in the pathogenesis of systemic sclerosis: a gene array approach,” PLoS Medicine, vol. 3, no. 1, article e2, 2005.

J. Zhou, H. K. Law, C. Y. Cheung, I. H. Ng, J. S. Peiris, and Y. L. Lau, “Functional tumor necrosis factor-related apoptosis-inducing ligand production by avian influenza virus-infected macrophages,” The Journal of Infectious Diseases, vol. 193, no. 7, pp. 945–953, 2006.

L. D. Heftdal, K. Stengaard-Pedersen, L. M. Ornbjerg et al., “Soluble cd206 plasma levels in rheumatoid arthritis reflect decrease in disease activity,” Scandinavian Journal of Clinical and Laboratory Investigation, vol. 77, no. 5, pp. 385–389, 2017.

T. Areschoug and S. Gordon, “Scavenger receptors: role in innate immunity and microbial pathogenesis,” Cellular Microbiology, vol. 11, no. 8, pp. 1160–1169, 2009.

N. Rother and J. van der Vlag, “Disturbed T cell signaling and altered Th17 and regulatory T cell subsets in the pathogenesis of systemic lupus erythematosus,” Frontiers in Immunology, vol. 6, p. 610, 2015.

E. Lubberts, “The IL-23–IL-17 axis in inflammatory arthritis,” Nature Reviews Rheumatology, vol. 11, no. 10, p. 562, 2015.

M. Diani, G. Altomare, and E. Reali, “T helper cell subsets in clinical manifestations of psoriasis,” Journal of Immunology Research, vol. 2016, Article ID 7692024, 7 pages, 2016.

R. A. Gordon, G. Grigoriev, A. Lee, G. D. Kalliolias, and L. B. Ivashkiv, “The interferon signature and Stat1 expression in rheumatoid arthritis synovial fluid macrophages are induced by tumor necrosis factor α and counter-regulated by the synovial fluid microenvironment,” Arthritis & Rheumatism, vol. 64, no. 10, pp. 3119–3128, 2012.

A. Nzeusseu Toukap, C. Galant, I. Theate et al., “Identification of distinct gene expression profiles in the synovium of patients with systemic lupus erythematosus,” Arthritis & Rheumatism, vol. 56, no. 5, pp. 1579–1588, 2007.

R. M. Thurlings, M. Boumans, J. Tekstra et al., “Relationship between the type I interferon signature and the response to rituximab in rheumatoid arthritis patients,” Arthritis & Rheumatism, vol. 62, no. 12, pp. 3607–3614, 2010.

H. G. Raterman, S. Vosslamber, S. de Ridder et al., “The interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients,” Arthritis Research & Therapy, vol. 14, no. 2, p. R95, 2012.

F. Moschella, G. F. Torelli, M. Valentini et al., “Cyclophosphamide induces a type I interferon–associated sterile inflammatory response signature in cancer patients' blood cells: implications for cancer chemoimmunotherapy,” Clinical Cancer Research, vol. 19, no. 15, pp. 4249–4261, 2013.

N. I. Maria, Z. Brkic, M. Waris et al., “MxA as a clinically applicable biomarker for identifying systemic interferon type I in primary Sjögren’s syndrome,” Annals of the Rheumatic Diseases, vol. 73, no. 6, pp. 1052–1059, 2014.

G. Caignard, M. Lucas-Hourani, K. P. Dhondt et al., “The V protein of Tioman virus is incapable of blocking type I interferon signaling in human cells,” PLoS One, vol. 8, no. 1, article e53881, 2013.

Z. Brkic, O. B. Corneth, M. A. Versnel, and E. Lubberts, “Response to ‘T-helper 17 cell cytokines and interferon type I: partners in crime in systemic lupus erythematosus?’ - authors’ reply,” Arthritis Research & Therapy, vol. 16, no. 3, p. 410, 2014.

J. J. O'Shea and R. Plenge, “JAK and Stat signaling molecules in immunoregulation and immune-mediated disease,” Immunity, vol. 36, no. 4, pp. 542–550, 2012.

A. F. Heneghan, J. F. Pierre, and K. A. Kudsk, “JAK-STAT intestinal mucosal immunology,” JAK-STAT, vol. 2, no. 4, article e25530, 2013.

A. Sapone, K. M. Lammers, V. Casolaro et al., “Divergence of gut permeability and mucosal immune gene expression in two gluten-associated conditions: celiac disease and gluten sensitivity,” BMC Medicine, vol. 9, no. 1, p. 23, 2011.

N. Schlegel, M. Meir, V. Spindler, C. T. Germer, and J. Waschke, “Differential role of Rho GTPases in intestinal epithelial barrier regulation in vitro,” Journal of Cellular Physiology, vol. 226, no. 5, pp. 1196–1203, 2011.

A. Banan, J. Z. Fields, Y. Zhang, and A. Keshavarzian, “Phospholipase c-γ inhibition prevents egf protection of intestinal cytoskeleton and barrier against oxidants,” American Journal of Physiology Gastrointestinal and Liver Physiology, vol. 281, no. 2, pp. G412–G423, 2001.

D. Smyth, V. Phan, A. Wang, and D. M. McKay, “Interferon-γ-induced increases in intestinal epithelial macromolecular permeability requires the Src kinase Fyn,” Laboratory Investigation, vol. 91, no. 5, pp. 764–777, 2011.

R. Shtrichman and C. E. Samuel, “The role of gamma interferon in antimicrobial immunity,” Current Opinion in Microbiology, vol. 4, no. 3, pp. 251–259, 2001.

L. C. Stene, M. C. Honeyman, E. J. Hoffenberg et al., “Rotavirus infection frequency and risk of celiac disease autoimmunity in early childhood: a longitudinal study,” The American Journal of Gastroenterology, vol. 101, no. 10, pp. 2333–2340, 2006.

B. Diosdado, E. van Oort, and C. Wijmenga, ““Coelionomics”: towards understanding the molecular pathology of coeliac disease,” Clinical Chemistry and Laboratory Medicine, vol. 43, no. 7, pp. 685–695, 2005.

K. Juuti-Uusitalo, M. Maki, H. Kainulainen, J. Isola, and K. Kaukinen, “Gluten affects epithelial differentiation-associated genes in small intestinal mucosa of coeliac patients,” Clinical & Experimental Immunology, vol. 150, no. 2, pp. 294–305, 2007.

S. Bracken, G. Byrne, J. Kelly, J. Jackson, and C. Feighery, “Altered gene expression in highly purified enterocytes from patients with active coeliac disease,” BMC Genomics, vol. 9, no. 1, p. 377, 2008.

S. Sakaguchi, M. Miyara, C. M. Costantino, and D. A. Hafler, “Foxp3⁺ regulatory T cells in the human immune system,” Nature Reviews Immunology, vol. 10, no. 7, pp. 490–500, 2010.

C. Zhang, X. Zhang, and X. H. Chen, “Inhibition of the interleukin-6 signaling pathway: a strategy to induce immune tolerance,” Clinical Reviews in Allergy & Immunology, vol. 47, no. 2, pp. 163–173, 2014.

M. R. Clatworthy, “B-cell regulation and its application to transplantation,” Transplant International, vol. 27, no. 2, pp. 117–128, 2014.

R. C. Hsueh and R. H. Scheuermann, “Tyrosine kinase activation in the decision between growth, differentiation, and death responses initiated from the B cell antigen receptor,” Advances in Immunology, vol. 75, pp. 283–316, 2000.

C. R. Shaler, J. Choi, P. T. Rudak et al., “MAIT cells launch a rapid, robust and distinct hyperinflammatory response to bacterial superantigens and quickly acquire an anergic phenotype that impedes their cognate antimicrobial function: defining a novel mechanism of superantigen-induced immunopathology and immunosuppression,” PLoS Biology, vol. 15, no. 6, article e2001930, 2017.

T. H. Schreiber, D. Wolf, M. Bodero, L. Gonzalez, and E. R. Podack, “T cell costimulation by TNFR superfamily (TNFRSF)4 and TNFRSF25 in the context of vaccination,” The Journal of Immunology, vol. 189, no. 7, pp. 3311–3318, 2012.

I. Kavazović, M. Lenartić, V. Jelenčić et al., “NKG2D stimulation of CD8⁺ T cells during priming promotes their capacity to produce cytokines in response to viral infection in mice,” European Journal of Immunology, vol. 47, no. 7, pp. 1123–1135, 2017.