Advertisement

BMC Veterinary Research

, 15:195 | Cite as

MicroRNA responses associated with Salmonella enterica serovar typhimurium challenge in peripheral blood: effects of miR-146a and IFN-γ in regulation of fecal bacteria shedding counts in pig

  • Tinghua Huang
  • Xiali Huang
  • Wang Chen
  • Jun Yin
  • Bomei Shi
  • Fangfang Wang
  • Wenzhao Feng
  • Min YaoEmail author
Open Access
Research article
Part of the following topical collections:
  1. Animal Genetics

Abstract

Background

MicroRNAs are involved in a broad range of biological processes and are known to be differentially expressed in response to bacterial pathogens.

Results

The present study identified microRNA responses in porcine peripheral blood after inoculation with the human foodborne pathogen Salmonella enterica serovar Typhimurium strain LT2. We compared the microRNA transcriptomes of the whole blood of pigs (Duroc × Landrace × Yorkshire) at 2-days post inoculation and before Salmonella infection. The analysis identified a total of 29 differentially expressed microRNAs, most of which are implicated in Salmonella infection and immunology signaling pathways. Joint analysis of the microRNA and mRNA transcriptomes identified 24 microRNAs with binding sites that were significantly enriched in 3′ UTR of differentially expressed mRNAs. Of these microRNAs, three were differentially expressed after Salmonella challenge in peripheral blood (ssc-miR-146a-5p, ssc-miR-125a, and ssc-miR-129a-5p). Expression of 23 targets of top-ranked microRNA, ssc-miR-146a-5p, was validated by real-time PCR. The effects of miR-146a, IFN-γ, and IL-6 on the regulation of fecal bacteria shedding counts in pigs were investigated by in vivo study with a Salmonella challenge model.

Conclusions

The results indicated that induction of miR-146a in peripheral blood could significantly increase the fecal bacterial load, whereas IFN-γ had the reverse effect. These microRNAs can be used to identify targets for controlling porcine salmonellosis.

Keywords

Salmonella Swine microRNA Immune response miR-146a IFN-γ 

Abbreviations

CFU

Colony forming units

Dpi

Days post inoculation

FDR

False discovery rate

LPS

Lipopolysaccharide

PBMC

Peripheral blood mononuclear cell

UTR

Untranslated region

Background

MicroRNAs are small, non-coding RNAs that regulate gene expression by target mRNAs at the post-transcriptional level (degradation or translational repression) [1, 2]. Differential expression of microRNAs has been implicated in a number of biological processes, such as cancer, development, growth [3, 4], and especially in the regulation of the host immune system [5, 6]. Subsequent studies suggested the roles of microRNAs in response to infection by pathogens [7, 8]. The Salmonella enterica serovar Typhimurium is a Gram-negative bacterium that causes gastroenteritis in both humans and animals.

In pigs, Salmonella enterica is the cause of asymptomatic carriage status to systemic febrile infection-related death varying with the ages of the animals [9]. Salmonella carrier pigs can transmit bacteria to the pig carcass in the slaughterhouse and contaminate the pork product, thus posing a significant threat to the human health [10]. Establishment of Salmonella carrying status is determined by the virulence of the bacteria as well as the genetic predisposition of the infected individual [11]. Several important Salmonella resistance microRNAs have been identified in animals, including miR-146 and miR-155 [12], let-7 [13], miR-29 [14], miR-128 [15], miR-15 [16], and more remaining to be discovered. These microRNAs are summarized in recent reviews [17, 18, 19]. Previous research on low versus persistent Salmonella shedding pigs at 2-days post inoculation (2 dpi) identified miR-214 and miR-331 as regulators of immune-related genes in peripheral blood [20] and thus demonstrated the important roles of microRNAs in buffering gene expression variation [21].

The miR-146a was found to be coordinately up regulated in immune cells in response to Salmonella infection [12]. It has been reported in mouse that the levels of miR-146a was significantly increased in T helper 1 cells and decreased in T helper 2 cells [22]. Further investigation showed that miR-146a was involved in lymphocytes cell fate determination [22]. The transcription of miR-146a might be regulated by the NF-κB and the STAT family [23]. Recent gene knock-out studies showed an increased percentage of INFγ-producing T-cell subset in the miR-146a-deficient mice [24]. Also, miR-146a was found to be inducible upon stimulation with lipopolysaccharide (LPS) in a NF-κB-dependent manner, and to target the TRAF6 and IRAK1, which encode downstream adapter molecules of Toll like receptors [25].

The scope of the study is to identify the differences in microRNA transcription abundance in peripheral blood samples collected before Salmonella Typhimurium inoculation (day 0) and early infection stage at 2 days post inoculation (2 dpi). A microRNA, miR-146a, was linked to porcine Salmonella shedding count which could help identify novel targets for controlling this human foodborne pathogen in pigs.

Results

To identify porcine microRNAs that are differentially expressed in response to Salmonella Typhimurium challenge, microRNA transcriptomes were constructed for blood samples collected from three piglets (randomly selected from the nine pigs described in the Materials and Methods) at day 0 and 2 dpi. A total of 136,491,615 microRNA sequences were obtained. The six samples had an average of 22,748,602 reads per library; the number of reads ranged between 13,997,226 and 30,710,701 reads. The microRNA sequences were mapped to the 457 Sus scrofa microRNA mature sequences deposited in miRBase, and 185 microRNAs that were present across the six samples were identified. For transcriptome profile analysis of microRNA samples collected on day 0 and 2 dpi, a total of 29 differentially expressed microRNAs were identified based on the following criteria: 1) minimum read count of 20; 2) fold change ≥2.0, and 3) FDR ≤ 0.05 (Table 1). A total of 18 microRNAs were significantly downregulated, and five of these, namely, ssc-miR-16, ssc-miR-143-3p, ssc-miR-23b, and ssc-miR-744, were differentially expressed by at least four-fold. A total of 12 microRNAs were significantly upregulated, and five of these, namely, ssc-miR-155-5p ssc-miR-124a, ssc-miR-127, ssc-miR-26a, and ssc-miR-146a-5p, were differentially expressed by at least four-fold (Table 1).
Table 1

Differentially expressed microRNAs in response to Salmonella Typhimurium challenge

Accession No.

Name

Fold change

FDR

Sequence

MIMAT0007754

ssc-miR-16

−6.88

3.71E-05

UAGCAGCACGUAAAUAUUGGCG

MIMAT0022959

ssc-miR-155-5p

7.43

0.000113

UUAAUGCUAAUUGUGAUAGGGG

MIMAT0013879

ssc-miR-143-3p

−5.96

0.000194

UGAGAUGAAGCACUGUAGCUC

MIMAT0013893

ssc-miR-23b

−4.60

0.000214

AUCACAUUGCCAGGGAUUACCA

MIMAT0013932

ssc-miR-127

5.83

0.000322

UCGGAUCCGUCUGAGCUUGGCU

MIMAT0002135

ssc-miR-26a

5.20

0.000459

UUCAAGUAAUCCAGGAUAGGCU

MIMAT0002156

ssc-miR-124a

5.86

0.000571

UAAGGCACGCGGUGAAUGCCA

MIMAT0002157

ssc-miR-128

−3.98

0.000613

UCACAGUGAACCGGUCUCUUU

MIMAT0007755

ssc-miR-17-5p

−3.01

0.000617

CAAAGUGCUUACAGUGCAGGUAG

MIMAT0013865

ssc-let-7a

−3.90

0.000678

UGAGGUAGUAGGUUGUAUAGUU

MIMAT0002133

ssc-miR-23a

−3.57

0.001286

AUCACAUUGCCAGGGAUUUCC

MIMAT0013916

ssc-miR-34c

−3.58

0.001402

AGGCAGUGUAGUUAGCUGAUUGC

MIMAT0015708

ssc-miR-744

−4.32

0.00213

UGCGGGGCUAGGGCUAACAGCA

MIMAT0022963

ssc-miR-146a-5p

4.36

0.003514

UGAGAACUGAAUUCCAUGGGUU

MIMAT0013867

ssc-let-7 g

3.00

0.004261

UGAGGUAGUAGUUUGUACAGUU

MIMAT0007753

ssc-miR-15a

−3.22

0.004261

UAGCAGCACAUAAUGGUUUGU

MIMAT0013908

ssc-miR-92a

−3.44

0.004261

UAUUGCACUUGUCCCGGCCUGU

MIMAT0002152

ssc-let-7f-5p

−3.19

0.004365

UGAGGUAGUAGAUUGUAUAGUU

MIMAT0025361

ssc-miR-132

3.25

0.00443

UAACAGUCUACAGCCAUGGUCG

MIMAT0002167

ssc-miR-30c-5p

−3.36

0.00443

UGUAAACAUCCUACACUCUCAGC

MIMAT0002153

ssc-let-7i-5p

−2.88

0.0055

UGAGGUAGUAGUUUGUGCU

MIMAT0007757

ssc-miR-34a

−3.30

0.0055

UGGCAGUGUCUUAGCUGGUUGU

MIMAT0013875

ssc-miR-199a-3p

−3.10

0.008427

ACAGUAGUCUGCACAUUGGUUA

MIMAT0002165

ssc-miR-21-5p

2.71

0.010239

UAGCUUAUCAGACUGAUGUUGA

MIMAT0041605

ssc-miR-141

2.57

0.011832

UAACACUGUCUGGUAAAGAUG

MIMAT0025384

ssc-miR-874

−2.56

0.011895

CUGCCCUGGCCCGAGGGACCGAC

MIMAT0013897

ssc-miR-125a

2.65

0.014516

UCCCUGAGACCCUUUAACCUGUG

MIMAT0032108

ssc-miR-129a-5p

−2.71

0.025477

CUUUUUGCGGUCUGGGCUUGC

MIMAT0002155

ssc-miR-107

2.36

0.025666

AGCAGCAUUGUACAGGGCUAUCA

MIMAT0002151

ssc-let-7c

2.80

0.032272

UGAGGUAGUAGGUUGUAUGGUU

The microRNA profiling data were analyzed jointly with the mRNA transcriptome data to identify microRNAs whose binding sites were significantly enriched in the 3′ untranslated region (UTR) of differentially expressed mRNAs. A microRNA-regulator-(target gene) network was defined as a microRNA connected to a regulator, which is in turn connected to a set of target genes. These networks were created using experimentally validated binding sites deposited in the miRTarBase [26] and GREDB (http://www.thua45.cn/geredb/). The inputs are expression profiles of the target genes. The statistical problem was to test whether the microRNA binding sites in targets are enriched in the differentially expressed genes, and they can be formulated in a two-by-two contingency table. Significant enrichment values in the corresponding network and gene expression profile can be deduced using Fisher’s exact test. The mRNA transcriptome profiling data in porcine peripheral blood challenge with Salmonella bacteria was obtained from the NCBI GEO database with accession number GSE118150. Comparisons were made between basal and 2 days of Salmonella challenge. The input files were created with gene expression significantly changed in comparison with FDR ≤0.05 and marked as “diff”. The result indicated that the binding sites of 24 microRNAs were significantly enriched in the “diff” genes (Table 2). In these microRNAs, three were differentially expressed after Salmonella challenge in peripheral blood (ssc-miR-146a-5p, ssc-miR-125a, and ssc-miR-129a-5p, see Table 1 for details).
Table 2

Significantly enriched microRNAs in mRNA transcriptome of peripheral-blood challenged with Salmonella

Regulon

Aa

Bb

Cc

Dd

FDR

ssc-miR-146a-5p

187

620

1081

6808

1.89E-09

ssc-miR-125a

107

644

565

7029

4.11E-07

ssc-let-7e-5p

64

674

315

7204

0.000116

ssc-miR-466n-3p

85

665

502

7110

0.000195

ssc-miR-27a-3p

116

648

742

6983

0.000315

ssc-miR-3081-3p

24

698

78

7344

0.000381

ssc-miR-2139

24

698

77

7345

0.000403

ssc-miR-467 g

98

664

637

7057

0.001425

ssc-miR-669 m-3p

75

668

461

7125

0.001578

ssc-miR-466e-3p

94

665

608

7069

0.001627

ssc-miR-466d-3p

94

665

612

7067

0.001651

ssc-miR-298-5p

106

643

700

6890

0.001840

ssc-miR-297b-3p

94

665

608

7069

0.002827

ssc-miR-466a-3p

94

665

619

7065

0.002945

ssc-miR-466b-3p

55

679

322

7198

0.004303

ssc-miR-466c-3p

55

679

322

7198

0.004303

ssc-miR-466p-3p

55

679

322

7198

0.004303

ssc-miR-20a-5p

110

650

767

6971

0.006205

ssc-miR-5098

27

693

123

7321

0.006687

ssc-miR-181b-5p

109

636

769

6853

0.006835

ssc-miR-129a-5p

143

633

1071

6887

0.007193

ssc-miR-138-5p

48

679

265

7190

0.007297

ssc-miR-125b-5p

114

639

803

6865

0.007862

ssc-miR-19a-3p

83

649

554

6927

0.007940

aNumber of microRNA targets in differential gene list

bNumber of microRNA targets in non-differential gene list

cNumber of non-microRNA targets in differential gene list

dNumber of none-microRNA targets in none-differentially gene list

There are a total of 807 miR-146a-5p target in the uploaded gene list (Ratio = 0.083) and a total of 187 miR-146a-5p target in the differentially expressed gene list (Ratio = 0.147) gives a highly significant p value of 1.89E-09 (Table 2). The top ranked microRNA, miR-146a-5p, can regulate 12 regulators, namely, INFG, IL6, VEGFA, PPARG, NOTCH1, STAT1, NOS2, TRAF6, RELB, WNT5A, HES1, and IRAK1. Among those regulators, INFG, IL6, VEGFA, and PPARG can regulate the largest number of target genes. A total of 64 IFNG targets, 36 IL6 targets, 25 VEGFA targets, and 31 PPARG targets were differentially expressed in peripheral blood after Salmonella challenge (Fig. 1).
Fig. 1

The miR-146a regulation network. The microRNA is plotted at the center of the graph (red). The gene regulators regulated by the microRNA are plotted around the microRNA (blue) with edges weighted by the number of target genes. The target genes are plotted as black nodes and labeled by the official gene symbol

To determine if the in vivo expression patterns of the 23 miR-146a target genes could be modeled by varying LPS levels, we performed real-time PCR after in vitro treatment in peripheral blood mononuclear cell (PBMCs) with three different doses of LPS (1 ng/ml, 10 ng/ml, and, 100 ng/ml), and miR-146a overexpression or knockdown. Samples were collected at 4 h post stimulation. A total of 21 genes were induced in response to LPS treatment, as RNA levels for these genes in the non-stimulated control were different from at least one dose of LPS stimulation. A total of 10 genes were down-regulated in response to overexpression with miR-146a, and 19 genes were up-regulated in response to knockdown with miR-146a. We used hierarchical clustering analysis to determine whether the LPS stimulation response pattern of the combined miR-146a target genes was similar to the patterns detected in miR-146a overexpression or knockdown, and if any similarity depended on the dosage of LPS used. Hierarchical clustering analysis of the average mRNA levels of the 23 miR-146a target genes indicated that the expression patterns of samples with miR-146a knockdown clustered with the LPS stimulation group (Fig. 2). When the LPS dose was increased, the expression pattern became less similar with miR-146a knockdown, and the most similar pattern seen upon LPS stimulation was at the 10 ng/ml dose. As expected, the expression pattern of miR-146a overexpression alone was unique and was neither similar to the LPS stimulation nor the miR-146 knockdown. The greatest similarity seen was in the samples before any treatment.
Fig. 2

 Hierarchical clustering of the gene expression data in PBMCs measured by real-time PCR. Cells were treated in vitro with three different doses of LPS (1 ng/ml, 10 ng/ml, and 100 ng/ml) and miR-146 overexpression or knockdown. Color codes of yellow, black, and blue represent expression levels of high, average, and low, respectively, across the treatments shown

To evaluate the potential effects of miR-146a in Salmonella infected pigs, measurements of Salmonella shedding counts were taken in Salmonella challenged pigs that had received either mock nanovector or miR-146a nanovector by jugular-vein injection (10 and 50 μg), twice in 1 day. Meanwhile, to show the involvement of the regulator gene, pigs were also treated with IFN-γ and IL-6 recombinant protein by jugular-vein injection (10 μg). The experiment was performed in a three (0, 10, and 50 μg) by three (Control, IFN-γ, and IL-6) factor completely randomized design with five Salmonella-free animals in each group. SAS GLM analysis showed an effect on Salmonella fecal shedding count for miR-146a and IFN-γ treatment (p < 0.06) but not for IL-6. A significant increase in Salmonella shedding counts was observed when pigs were treated with miR-146a nanovectors compared with the mock nanovector (Fig. 3). The shedding counts of the 50 μg miR-146a nanovector treatment group were 5.14 and 2.15 fold higher than the mock nanovector and 10 μg miR-146a nanovector treatment group, respectively (p < 0.05). There was no significant difference between the mock and 10 μg miR-146a nanovector treatment group. These results indicate that the promotive effects on shedding counts were caused by induction of the miR-146a nanovector. Notably, the Salmonella shedding count of IFN-γ treatment group was significantly lower than the control group (p < 0.05), whereas the IL-6 treatment group was not significantly different.
Fig. 3

Effects of miR-146a, IFN-γ and IL-6 on Salmonella shedding counts in pigs. Salmonella shedding counts of a three (0 μg: black square, 10 μg: red cycle, and 50 μg: blue triangle) by three (Control, IFN-γ and IL-6) factor, completely randomized design with five animals in each group

Discussion

Several microRNAs have been reported to be differentially regulated following Salmonella infection. For example, miR-146 and miR-155 were demonstrated to be coordinately upregulated in immune cells [12]. Expression of the microRNA let-7 is downregulated in macrophage cells in response to Salmonella infection [13]. MiR-128 expression is upregulated in intestinal epithelial cells [15], while miR-15 is downregulated via inhibition by the transcription factor E2F1 [16]. In the presented study, both miR-146 and miR-155 were upregulated (4.36- and 7.43-fold, respectively) following Salmonella infection. On the other hand, let-7 s (let-7a, −g, −f, and -i) expression levels were downregulated, with fold changes ranging from 2.88 to − 3.9, and miR-15 was downregulated by 3.44-fold. The only exception is that, contrary to previously reported findings, miR-128 was downregulated by 3.9-fold. In our previous study, we showed that miR-143 expression was significantly downregulated in persistent shedding animals compared to low shedding animals after Salmonella infection. In addition, miR-26 expression was induced 10.5-fold in persistent shedding animals and was upregulated by 14-fold at 2 dpi [27]. Consistent with previous findings, our results showed that miR-143 expression was downregulated by 5.96-fold, while miR-26 expression was upregulated by 5.2-fold. Other microRNAs, such as miR-124, did not show evidence of direct involvement with Salmonella bacterial infection but have been predicted to play roles in targeting immune signaling pathways. MiR-124 expression is upregulated by 5.86-fold at 2 dpi. IQGAP2 was a predicted target of miR-124 and is downregulated by fivefold in persistent shedding pigs after Salmonella inoculation [28, 29]. IQGAP2 is associated with the CDC42 protein, which is known to regulate the MAPK signaling pathway. The observed upregulation of miR-124 expression and downregulation of IQGAP2 expression indicated that the CDC42-MAPK pathway is highly possibly inhibited after Salmonella infection. The microRNA responses to Salmonella challenge provide interesting hypotheses on the mechanisms underlying Salmonella infection in pigs.

The miR-146a was found to be a NF-κB-dependent gene and is responsible for regulated innate immune genes such as TRAF6 and IRAK1 [25]. Previous studies have shown that upregulation of miR-146a significantly inhibits LPS-induced IFN-γ expression in mouse splenic lymphocytes [30]. Also, miR-146a is upregulated during retinal pigment epithelium aging in mice and represses IL-6 expression in RPE cells [31]. IFN-γ was found to promote rapid acidification of phagolysosomes within infected macrophages and this low pH within the phagolysosome improves reactive nitrogen species production and leads to elimination of the pathogen [32]. The roles of IFN-γ in Salmonella infection have been reviewed by Gunjan et al. [33]. In brief, IFN-γ has been proved critical in mediating intestinal immunity to Salmonella typhimurium [34, 35]; IFN-γ also facilitated internalization and promoted early killing of Salmonella without employing oxidative burst [36]; furthermore, IFN-γ depleted intracellular iron levels in Salmonella typhimurium infected macrophages and exerted it’s anti-microbial effect [37]. In this study, the effects of miR-146a, IFN-γ, and, IL-6 on Salmonella fecal shedding counts in pig were investigated. Although the detailed mechanism of miR-146a regulation of IFN-γ and IL-6, and the establishment of different shedding status, remains unknown, it is clear that an appropriate innate immune response is required to defend an organism against Salmonella infection. However, if attenuated at post-transcription level, the response can be insufficient, causing the pathological manifestations of increased bacterial load and prolonged shedding status.

Conclusions

The present study identified a total of 29 differentially expressed microRNAs responses in porcine peripheral blood after inoculation with the human foodborne pathogen Salmonella enterica serovar Typhimurium. Joint analysis of the microRNA and mRNA transcriptomes using binding sites enrichment analysis identified three microRNA candidates (ssc-miR-146a-5p, ssc-miR-125a, and ssc-miR-129a-5p) which were both differentially expressed after Salmonella challenge in peripheral blood and shown over-represented binding sites in differentially expressed mRNA list. In vivo study with a Salmonella challenge model indicated that induction of miR-146a in peripheral blood could significantly increase the fecal bacterial load by regulating IFN-γ targets.

Methods

Nine crossbred (Duroc × Landrace × Yorkshire), conventionally raised, mixed gender piglets from three sows were weaned at 14 days of age and housed in an animal room under controlled and standardized conditions (animals were obtained from Da Bei Nong group, China). Animals were provided free access to standard food and water at 7 days before the experiment (average weight 6.83 ± 1.24kg, tested for Salmonella free twice). Salmonella Typhimurium cells (Salmonella enterica serovar Typhimurium strain LT2, ATCC 700720) were cultured in Luria broth and M9 minimal medium as previously described [27]. Piglets with Salmonella-negative fecal were challenged with Salmonella Typhimurium as previously described [28, 29]. Briefly, piglets were intranasally administered with 1 × 109 colony forming units (CFUs) of Salmonella Typhimurium. Blood samples were obtained from the jugular vein of each animal before Salmonella inoculation (day 0) and 2 dpi following a previously described method [28, 29]. MicroRNA transcriptomes were sequenced for blood samples collected from three randomly selected piglets. After the experiment, the animals were combine treated with gentamicin and enrofloxacin for 7 days and tested for Salmonella free, then feeding in the isolation house for 30 days, and finally transferred to growing to finish farm.

Blood samples were immediately preserved in PAXgene blood RNA tubes containing the RNA stabilization reagent. Total RNA was isolated using the PAXgene Blood microRNA Kit according to the manufacturer’s instructions (Qiagen, USA). RNA quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA), and RNA quantity was assessed using a Nanodrop spectrophotometer (Thermo Scientific, USA). Libraries were constructed using the Illumina TruSeq Small RNA Sample Prep Kit and were sequenced on an Illumina HiSeq2000 sequencer (50 bp and single-read) (Illumina Inc., USA) [38]. Raw data were submitted to the NCBI GEO database under the accession NO. GSE120266. Sequence reads with lengths ranging from 18 to 26 nucleotides were extracted. Identical sequences were counted, and microRNAs were identified by aligning the unique sequences with those of known mature microRNA sequences downloaded from miRBase release 22. Porcine sequences with no mismatches with known microRNAs were regarded as real porcine microRNAs and were assigned the same accession number as those in miRBase. Data normalization and differential microRNA expression analysis were conducted using the limma R package [39]. In brief, a design matrix was created which includes separate coefficients for control (day 0) and 2 dpi blood samples, and then extract the difference as a contrast. The differentially expressed miRNAs were controlled as false discovery rate (FDR) ≤ 0.05 and fold change ≥1.5 or ≤ 0.67.

The miR-146a was amplified from genomic DNA sequence using Pfu polymerase and inserted into the XhoI restriction endonuclease site in the pMSCV-puro expression vector (Clontech Laboratories) [40]. The sequence of the expression vector was confirmed by sequencing. The lipid-based nanovector for systemic miR-146a delivery was prepared using the method described previously [40]. A liposomal-based nanovector was chosen on the basis of the proved safety and efficacy of such constructions and the enhanced circulation stability in blood afforded by the presence of polyethylene glycol [41]. The recombinant porcine IFN-γ and IL-6 proteins were purchased from R&D systems (985-PI-050 and 686-PI-025). The in vivo study was performed in 45 crossbred (Duroc × Landrace × Yorkshire), conventionally raised, mixed-gender piglets at a population age of 21 days. The animals were inoculated with Salmonella and then given different kinds of treatment at the beginning of the experiment, and the Salmonella shedding count in feces was measured at 2 days post inoculation use Uthe’s method described previously [28]. The data were analyzed using SAS 9.4 GLM procedure.

Notes

Acknowledgements

Not applicable

Authors’ contributions

TH designed the study, performed the experiments and data analysis, and wrote the manuscript. MY performed bioinformatics analysis and edited the manuscript. BS, XH, FW and WF helped conduct the pig infection experiments, and TH performed the RNA extraction. MY, WC and JY participated in the interpretation and discussion of results, and provided critical comments during the drafting of the manuscript. All authors have read and approved the final version of the manuscript.

Funding

This project was funded by the National Natural Science Foundation of China (NSFC Grant No. 31402055), College Students’ Innovation and Entrepreneurship Training Program of Yangtze University (Grant No. 2018057), the Science and Technology Research Project of Department of Education of Hubei Province (Grant No. Q20171305), the Yangtze Youth Talents Fund (Grant No. 2015cqr12), the Yangtze Youth Fund (Grant No. 2015cqn39), and the Scientific Research Starting Foundation for Returned Overseas Chinese Scholars of the Ministry of Education of China. The funding bodies played none role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Ethics approval and consent to participate

Experimental procedures involving animals were performed in compliance with the recommended guidelines described in the Guide for the Care and Use of Laboratory Animals and were approved by the Animal Care and Use Committee of Hubei Province (China, YZU-2018-0031).

Consent for publication

Not applicable

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. 1.
    Treiber T, Treiber N, Meister G. Regulation of microRNA biogenesis and function. Thromb Haemost. 2012;107(4):605–10.CrossRefGoogle Scholar
  2. 2.
    Roberts TC. The microRNA machinery. Adv Exp Med Biol. 2015;887:15–30.CrossRefGoogle Scholar
  3. 3.
    Acunzo M, Romano G, Wernicke D, Croce CM. MicroRNA and cancer--a brief overview. Adv Biol Regul. 2015;57:1–9.CrossRefGoogle Scholar
  4. 4.
    Kovanda A, Rezen T, Rogelj B. MicroRNA in skeletal muscle development, growth, atrophy, and disease. Wiley Interdiscip Rev RNA. 2014;5(4):509–25.CrossRefGoogle Scholar
  5. 5.
    Jernas M, Malmestrom C, Axelsson M, Nookaew I, Wadenvik H, Lycke J, Olsson B. MicroRNA regulate immune pathways in T-cells in multiple sclerosis (MS). BMC Immunol. 2013;14:32.CrossRefGoogle Scholar
  6. 6.
    Jernas M, Nookaew I, Wadenvik H, Olsson B. MicroRNA regulate immunological pathways in T-cells in immune thrombocytopenia (ITP). Blood. 2013;121(11):2095–8.CrossRefGoogle Scholar
  7. 7.
    Staedel C, Darfeuille F. MicroRNAs and bacterial infection. Cell Microbiol. 2013;15(9):1496–507.CrossRefGoogle Scholar
  8. 8.
    Eulalio A, Schulte L, Vogel J. The mammalian microRNA response to bacterial infections. RNA Biol. 2012;9(6):742–50.CrossRefGoogle Scholar
  9. 9.
    Gopinath S, Carden S, Monack D. Shedding light on Salmonella carriers. Trends Microbiol. 2012;20(7):320–7.CrossRefGoogle Scholar
  10. 10.
    Bearson SM, Allen HK, Bearson BL, Looft T, Brunelle BW, Kich JD, Tuggle CK, Bayles DO, Alt D, Levine UY, et al. Profiling the gastrointestinal microbiota in response to Salmonella: low versus high Salmonella shedding in the natural porcine host. Infect Genet Evol. 2013;16:330–40.CrossRefGoogle Scholar
  11. 11.
    Chiou CS, Wei HL, Mu JJ, Liao YS, Liang SY, Liao CH, Tsao CS, Wang SC. Salmonella enterica serovar Typhi variants in long-term carriers. J Clin Microbiol. 2013;51(2):669–72.CrossRefGoogle Scholar
  12. 12.
    Schulte LN, Westermann AJ, Vogel J. Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing. Nucleic Acids Res. 2013;41(1):542–53.CrossRefGoogle Scholar
  13. 13.
    Schulte LN, Eulalio A, Mollenkopf HJ, Reinhardt R, Vogel J. Analysis of the host microRNA response to Salmonella uncovers the control of major cytokines by the let-7 family. EMBO J. 2011;30(10):1977–89.CrossRefGoogle Scholar
  14. 14.
    Hoeke L, Sharbati J, Pawar K, Keller A, Einspanier R, Sharbati S. Intestinal Salmonella typhimurium infection leads to miR-29a induced caveolin 2 regulation. PLoS One. 2013;8(6):e67300.CrossRefGoogle Scholar
  15. 15.
    Zhang T, Yu J, Zhang Y, Li L, Chen Y, Li D, Liu F, Zhang CY, Gu H, Zen K. Salmonella enterica serovar enteritidis modulates intestinal epithelial miR-128 levels to decrease macrophage recruitment via macrophage colony-stimulating factor. J Infect Dis. 2014;209(12):2000–11.CrossRefGoogle Scholar
  16. 16.
    Maudet C, Mano M, Sunkavalli U, Sharan M, Giacca M, Förstner KU, Eulalio A. Functional high-throughput screening identifies the miR-15 microRNA family as cellular restriction factors for Salmonella infection. Nat Commun. 2014;5:4718.  https://doi.org/10.1038/ncomms5718.CrossRefPubMedGoogle Scholar
  17. 17.
    Zhou X, Li X, Wu M. miRNAs reshape immunity and inflammatory responses in bacterial infection. Signal Transduct Target Ther. 2018;3:14.CrossRefGoogle Scholar
  18. 18.
    Das K, Garnica O, Dhandayuthapani S. Modulation of host miRNAs by intracellular bacterial pathogens. Front Cell Infect Microbiol. 2016;6:79.CrossRefGoogle Scholar
  19. 19.
    Keck J, Gupta R, Christenson LK, Arulanandam BP. MicroRNA mediated regulation of immunity against gram-negative bacteria. Int Rev Immunol. 2017;36(5):287–99.CrossRefGoogle Scholar
  20. 20.
    Bao H, Kommadath A, Liang G, Sun X, Arantes AS, Tuggle CK, Bearson SM, Plastow GS, Stothard P, Guan l L. Genome-wide whole blood microRNAome and transcriptome analyses reveal miRNA-mRNA regulated host response to foodborne pathogen Salmonella infection in swine. Sci Rep. 2015;5:12620.CrossRefGoogle Scholar
  21. 21.
    Bao H, Kommadath A, Plastow GS, Tuggle CK, Guan le L, Stothard P. MicroRNA buffering and altered variance of gene expression in response to Salmonella infection. PLoS One. 2014;9(4):e94352.CrossRefGoogle Scholar
  22. 22.
    Monticelli S, Ansel KM, Xiao C, Socci ND, Krichevsky AM, Thai TH, Rajewsky N, Marks DS, Sander C, Rajewsky K, et al. MicroRNA profiling of the murine hematopoietic system. Genome Biol. 2005;6(8):R71.CrossRefGoogle Scholar
  23. 23.
    Murphy KM, Reiner SL. The lineage decisions of helper T cells. Nat Rev Immunol. 2002;2(12):933–44.CrossRefGoogle Scholar
  24. 24.
    Lu LF, Boldin MP, Chaudhry A, Lin LL, Taganov KD, Hanada T, Yoshimura A, Baltimore D, Rudensky AY. Function of miR-146a in controlling Treg cell-mediated regulation of Th1 responses. Cell. 2010;142(6):914–29.CrossRefGoogle Scholar
  25. 25.
    Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci U S A. 2006;103(33):12481–6.CrossRefGoogle Scholar
  26. 26.
    Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46(D1):D296–302.CrossRefGoogle Scholar
  27. 27.
    Yao M, Gao W, Tao H, Yang J, Liu G, Huang T. Regulation signature of miR-143 and miR-26 in porcine Salmonella infection identified by binding site enrichment analysis. Mol Gen Genomics. 2016;291(2):789–99.CrossRefGoogle Scholar
  28. 28.
    Uthe JJ, Wang Y, Qu L, Nettleton D, Tuggle CK, Bearson SM. Correlating blood immune parameters and a CCT7 genetic variant with the shedding of Salmonella enterica serovar typhimurium in swine. Vet Microbiol. 2009;135(3–4):384–8.CrossRefGoogle Scholar
  29. 29.
    Huang T, Huang X, Yao M. miR-143 inhibits intracellular salmonella growth by targeting ATP6V1A in macrophage cells in pig. Res Vet Sci. 2018;117:138–43.CrossRefGoogle Scholar
  30. 30.
    Dai R, Phillips RA, Zhang Y, Khan D, Crasta O, Ahmed SA. Suppression of LPS-induced interferon-gamma and nitric oxide in splenic lymphocytes by select estrogen-regulated microRNAs: a novel mechanism of immune modulation. Blood. 2008;112(12):4591–7.CrossRefGoogle Scholar
  31. 31.
    Hao Y, Zhou Q, Ma J, Zhao Y, Wang S. miR-146a is upregulated during retinal pigment epithelium (RPE)/choroid aging in mice and represses IL-6 and VEGF-A expression in RPE cells. J Clin Exp Ophthalmol. 2016;7(3):562.Google Scholar
  32. 32.
    Flannagan RS, Cosio G, Grinstein S. Antimicrobial mechanisms of phagocytes and bacterial evasion strategies. Nat Rev Microbiol. 2009;7(5):355–66.CrossRefGoogle Scholar
  33. 33.
    Kak G, Raza M, Tiwari BK. Interferon-gamma (IFN-gamma): exploring its implications in infectious diseases. Biomol Concepts. 2018;9(1):64–79.CrossRefGoogle Scholar
  34. 34.
    Bao S, Beagley KW, France MP, Shen J, Husband AJ. Interferon-gamma plays a critical role in intestinal immunity against Salmonella typhimurium infection. Immunology. 2000;99(3):464–72.CrossRefGoogle Scholar
  35. 35.
    Muotiala A, Makela PH. The role of IFN-gamma in murine Salmonella typhimurium infection. Microb Pathog. 1990;8(2):135–41.CrossRefGoogle Scholar
  36. 36.
    Gordon MA, Jack DL, Dockrell DH, Lee ME, Read RC. Gamma interferon enhances internalization and early nonoxidative killing of Salmonella enterica serovar typhimurium by human macrophages and modifies cytokine responses. Infect Immun. 2005;73(6):3445–52.CrossRefGoogle Scholar
  37. 37.
    Brown DE, Nick HJ, McCoy MW, Moreland SM, Stepanek AM, Benik R, O'Connell KE, Pilonieta MC, Nagy TA, Detweiler CS. Increased ferroportin-1 expression and rapid splenic iron loss occur with anemia caused by Salmonella enterica Serovar typhimurium infection in mice. Infect Immun. 2015;83(6):2290–9.CrossRefGoogle Scholar
  38. 38.
    Eminaga S, Christodoulou DC, Vigneault F, Church GM, Seidman JG. Quantification of microRNA expression with next-generation sequencing. Curr Protoc Mol Biol. 2013;Chapter 4:Unit 4.17.PubMedGoogle Scholar
  39. 39.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.CrossRefGoogle Scholar
  40. 40.
    Pramanik D, Campbell NR, Karikari C, Chivukula R, Kent OA, Mendell JT, Maitra A. Restitution of tumor suppressor microRNAs using a systemic nanovector inhibits pancreatic cancer growth in mice. Mol Cancer Ther. 2011;10(8):1470–80.CrossRefGoogle Scholar
  41. 41.
    Karmali PP, Chaudhuri A. Cationic liposomes as non-viral carriers of gene medicines: resolved issues, open questions, and future promises. Med Res Rev. 2007;27(5):696–722.CrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.College of Animal ScienceYangtze UniversityJingzhouChina

Personalised recommendations