Virulent and Vaccine Strains of Streptococcus equi ssp

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Virulent and vaccine strains of Streptococcus equi ssp. zooepidemicus have different influences on phagocytosis and cytokine secretion of macrophages Jie Peng, Zhe Ma, Chengwei Hua, Huixing Lin, Hui Zhang, Chengping Lu, and Hongjie Fan J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00571 • Publication Date (Web): 11 Oct 2016 Downloaded from http://pubs.acs.org on October 12, 2016

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Journal of Proteome Research

Virulent and vaccine strains of Streptococcus equi ssp. zooepidemicus have different influences on phagocytosis and cytokine secretion of macrophages Peng Jie†a, Ma Zhe†*ab, Hua Chengweia, Lin Huixingab, Zhang Huic, Lu Chengpinga and Fan Hongjie*ab

a. College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China

b. Jiangsu Co-innovation Center for Prevention and Control of Important Animal

Infectious Diseases and Zoonoses, Yangzhou 225009, China

c. China Animal Health and Epidemiology Center, Qingdao, China

† These two authors contributed equally to this work

* Corresponding Author, E-mail: [email protected] and [email protected]

Abstract Swine streptococcosis is a significant threat to the Chinese pig industry, and

Streptococcus equi ssp. zooepidemicus (SEZ) is one of the major pathogens. SEZ

ATCC35246 is a classical virulent strain, while SEZ ST171 is a Chinese attenuated 1

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vaccine strain. In this study, we employed stable isotope labeling by amino acids in

cell culture and liquid chromatography/mass spectrometry (LC/MS) to determine the

differential response of macrophages to infection by these two strains. Eighty-seven

upregulated proteins and 135 downregulated proteins were identified. The proteomic

results were verified by real-time PCR for 10 chosen genes and western blotting for

three proteins. All differentially abundant proteins were analyzed for their GO and

KEGG annotations. Certain downregulated proteins were associated with immunity

functions and the upregulated proteins were related to cytomembrane and

cytoskeleton regulation. The phagocytosis rate and cytokine genes transcription in

Raw264.7 cells during SEZ ATCC35246 and ST171 infection were detected to

confirm the bioinformatics results. These results showed that different effects on

macrophage phagocytosis and cytokine expression might explain the different

phenotypes of SEZ ATCC35246 and ST171 infection. This research provided clues to

the mechanisms of host immunity responses to SEZ ST171and SEZ ATCC35246,

which could identify potential therapy and vaccine development targets.

Keywords Streptococcus equi ssp. zooepidemicus, quantitative proteomics,

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macrophage, pathogen, vaccine.

1. Introduction Streptococcus equi ssp. zooepidemicus (SEZ) belongs to Lancefield’s group C of

catalase-negative, coagulase-negative bacteria. It is an opportunistic pathogen of a

wide variety of important domesticated animals, such as horses, pigs, cats and dogs 1,2

. Humans are also suitable hosts for SEZ infection, causing bacteremia and

meningitis. SEZ-infected domestic animals are a significant threat to human health. In

recent years, many SEZ outbreaks in animals and humans have been reported worldwide, including in Spain, South Korea and Germany 3-6. In China, SEZ is an

important swine pathogen that has caused significant economic losses since the

1970s. SEZ caused approximately 300,000 pig deaths within two weeks in Sichuan province in 1975. SEZ strain ATCC35246 was isolated from these dead pigs 7, and was determined as virulent not only to pigs, but also to many other species 8. In 1978,

Liao and his colleagues developed a SEZ avirulent vaccine strain ST171 via 171

generations of artificial passage at 45°C of SEZ virulent strains isolated from

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Guangdong province. ST171 has a great immunoprotective effect: it prevents pigs

from being infected by virulent SEZ strains, with few adverse reactions.

Both pathogenic and attenuated live vaccines invade the mammalian host and

activate the innate immune system initially. As a part of the first line of host innate

immune system defense, macrophages play vital roles in antigen presentation and

elimination. Most pathogens have strategies to fight against macrophage, such as

manipulating surface receptor molecules related to macrophage adhesion and phagocytosis, or directly modulating host signaling pathways 9-11. During invasion,

pathogens always induce certain harmful responses in macrophage that are beneficial for bacterial immune evasion and tissue injury 12, while vaccines induce host

macrophages to limit bacterial replication and improve immunity. Foreign

microorganisms can stimulate macrophage to secrete cytokines that mediate

communication among immune and non-immune cells. Manipulation of cytokines

expression, and the production and release of macrophage cytokines, can have

profound effects on the immune response. Inhibiting cytokines secretion will disturb

the response of macrophage to pathogens, while a sudden and violent inflammatory

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response with a cytokine storm, such as TNF-α and IFN-γ, will impair host tissues seriously, even causing host death 13,14.

During infection, the influence of ATCC35246 and the diminished virulence of

vaccine ST171 to the host immunity system are completely different. However, the

underlying mechanism of this difference remains unknown. In this study, we used

stable isotope labeling by amino acids in cell culture (SILAC) coupled with tandem

mass spectroscopy (LC-MS/MS) to generate a preliminary map of the macrophage

proteome in the presence of ATCC35246 and ST171 infection. Determining the

proteomic changes in macrophages infected with ATCC35246 and ST171 would

provide both a valuable data set, and more importantly, a point of comparison with

better characterized SEZ strains. Analysis of different quantitative proteomics of

macrophages might also help to identify the host immunity responses to ST171, and

the invasive mechanisms used by ATCC3524, providing information for potential

vaccine development targets.

2. Materials & Methods

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2.1 Microorganisms

Streptococcus equi ssp. zooepidemicus (SEZ) ATCC35246 was isolated from a

dead pig in Sichuan province, China, and stored by the American Type Culture

Collection (ATCC, Manassas, VA, USA). ST171 was an artificially attenuated strain

produced by continuous passaging of SEZ virulent strains from Guangdong province

isolated at high temperature for 171 generations. Bacteria were cultured in Todd

Hewitt Broth (THB) (BD Biosciences, San Jose, CA, USA) medium at 37°C.

2.2 Raw264.7 cell culture and SILAC labeling

Raw264.7 macrophage cells were purchased from the ATCC. This cell line was

established from a tumor induced by Abelson murine leukemia virus and isolated from mice. The cells were cultured in heavy isotope (Arg13C6, Lys13C6) or light isotope (Arg12C6, Lys12C6) SILAC DMEM media with 10% FBS (fetal bovine serum;

Pierce, Rockford, IL, USA) at 37°C and 5% CO2. After passaging for five

generations, most proteins in Raw264.7 cells should be labeled by isotopes of L-lysine and L-Arginine 15,16.

2.3 SEZ infection and sample preparation

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SEZ ATCC35246 and ST171 were cultured overnight on THB solid medium at

37°C. A fresh single colony was picked into 5 ml of THB medium and grown to an

OD600 = 0.6 at 37°C with vigorous shaking (180 rpm). The SEZ cultures were

centrifuged at 5,000 × g for 5 min at 4°C and the pellets retained. The bacteria were

washed with PBS three times and resuspended in DMEM media. The heavy

isotope-labeled Raw264.7 cells were infected with ATCC 35246 and the light

isotope-labeled Raw264.7 cells were infected with ST171 at a multiplicity of

infection (MOI) of 1:10, and cultured at 37°C under 5% CO2. After 4 hours, the total

cellular proteins were extracted using M-PER Mammalian Protein Extraction Reagent

(containing a Protease Inhibitor Cocktail) (Thermo, Waltham, MA, USA). Protein

concentrations of heavy and light lysates were measured using a BAC Protein Assay

Kit (Pierce, Rockford, IL, USA) and equal quantities of heavy and light lysates were

mixed. Mixed proteins were separated by SDS-PAGE and the gel was excised into

five slices, two biological replications were taken in this part (Supplementary Figure

1).

2.4 Mass spectrometry analysis

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The gel was cut into 1~2 mm2 slices and sonicated in 100 µl 50% acetonitrile/25

mmol/L ammonium bicarbonate (Sigma, St. Louis, MO, USA) for 10 min for

distaining. The liquid was discarded and 50 µL of 10 mM DTT (dithiothreitol;

Bio-Rad, Hercules, CA, USA) was added and the slices incubated at 56°C for 30 min.

The liquid was discarded and the slices further destained using 100 µl of acetonitrile.

Then, 50 µl of 55 mM iodoacetamide (Bio-Rad, Hercules, CA, USA) was added and

the slices were incubated for 30 min in the dark for alkylation. The slices were

washed with 50% acetonitrile/25 mmol/L ammonium at bicarbonate 5 min and the

liquid discarded. The gel pieces were dried thoroughly using a Vacuum Concentrator

Package (Thermo, Waltham, MA, USA). The dried gel was then digested with 10–15

µL 10 ng/µL trypsin (Promega, Madison, WI, USA) at 4°C for 30 min and at 37°C

overnight. The tryptic peptides were extracted with 100 µl 5% trifluoroacetic acid

(TFA) at 40°C for 1 h and 100 µl 2.5% TFA/50% CAN (Merck, Billerica, MA, USA)

at 30°C for 1 h, mixed with two samples of supernatant, separately, and dried using

the Vacuum Concentrator Package. The dried peptides were resuspended in 20 µl 2%

methyl alcohol and 0.1% formic acid, centrifuged at 12,000 g for 10 min and the

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supernatants collected. The peptides mixtures were separated using the EASY-nLC

1000 System (Nano HPLC) (Thermo, Waltham, MA, USA) on a C18 Acclaim

PepMap100 pre-column and C18 EASY-Spray column. The spray voltage was set to

2.1 kV, the capillary temperature was set to 250°C, the ion source was the

EASY-Spray source and the DP was 100. Full MS with a resolution of 70000 FWHM

was performed, with a full scan AGC target of 1e6, at full scan max. The IT was 60

ms and the scan range was 350-1800 m/z. The dd-MS2 had a resolution of 17500

FWHM, the AGC target was 5e6, the maximum IT was 70ms and the intensity

threshold was 5.00E+3. The fragmentation method was HCD, the NCD was 29% and

the TOP N was 20. The data were acquired using Q-Exactive software (Thermo).

2.5 Data bioinformatics analysis

The data of the two replications were combined together for further gene

ontology GO and Kyoto encyclopedia of genes and genomes (KEGG) analysis. The R package “clusterProfiler” was employed for the bioinformatics analysis 17. The

upregulated and down regulated proteins were analyzed, respectively. Briefly, all

proteins were classified into biological process level 2 GO terms by GO classification

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(Counts ≥ 10 and p-value < 0.05). An over-representation test was used here to assess

whether the number of downregulated proteins associated with the GO term

biological process was larger than expected. GO Gene Set Enrichment Analysis (GO

GSEA) aggregated the per protein statistics across proteins within a protein set,

making it possible to detect situations where all proteins in a predefined set change in

a small but coordinated way. KEGG pathway analysis was used to determine the

relationship of differentially abundant proteins and signal pathways. “clusterProfiler”

also helped us to identify the relationship among these GO terms and pathways.

2.6 Quantitative real-time PCR and Western blotting

Quantitative real-time PCR (qPCR) was performed to detect the expression level

of gene encoding differentially abundant proteins and cytokines in Raw 264.7 cells

after infection. The genes representing the 10 downregulated proteins were chosen

according to their functions, such as immunity and signal transduction. Total RNA of

cells was extracted using RNAiso Plus (Takara, Tokyo, Japan), according to the manufacturer’s protocol. cDNA was synthesized using PrimeScript™ 1st strand cDNA

Synthesis Kit (Takara). cDNA was mixed in a 20-µl final volume with 10 µl of

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SYBR® Premix EX Taq II, 0.4 µM of each primer and ROX Reference Dye II

(Takara), and detected using an ABI PRISM 7300 real-time PCR system (Applied

Biosystems by Thermo, Waltham, USA). The sequences of the qPCR primers are

listed in Table 1. To detect the gene expression levels, cell samples were used for

RNA extraction after 4h of infection, while for cytokine detection, the same extraction

was done after 6h infection.

Western blotting was performed to detect the expression levels of differentially

abundant proteins in Raw 264.7 cells after infection. Total protein of the cells was

extracted and the protein concentration was measured using a BAC Protein Assay Kit

(Pierce) and diluted to the same concentration. Proteins were boiled for 5 min in

SDS-PAGE sample buffer. After separating the samples by SDS-PAGE, the proteins

were transferred to 0.22 µm Polyvinylidene difluoride membranes (Roche, Basel,

Switzerland) and blocked in 5% skimmed milk (BD Biosciences, San Jose, CA, USA)

for 2 h at room temperature. Antibodies recognizing STAT3, Pgst2 and Mcl-1

(Abcam, Cambridge, MA, USA) and GAPDH (internal control; CMCTAG, USA)

were used as detection antibodies. After washing with TBST, membranes were

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incubated with HRP Goat anti-Mouse or Rabbit IgG antibodies (ABGENT, San

Diego, CA, USA) for 1 h. Specific bands were visualized using the ECL Western

Blotting Substrate (Pierce).

2.7 Phagocytosis test

A fresh bacterial colony (from SEZ ATCC 35246 and ST171) was inoculated

into 5 ml of THB medium and grown to an OD of 0.6 at 37°C with vigorous shaking

(180 rpm). The bacteria were then washed with PBS three times and resuspended. The Raw264.7 cells in 12 well culture plate (105 cells per well) were infected by SEZ at an

MOI 1:10. The plates were centrifuged at 500 × g for 10 min to allow the bacteria to

contact the surface of cells and were then incubated at 37°C in 5% CO2 for 2 h and 4

h. After washing cells three times with PBS, 1 ml of DMEM medium containing 100

µg/ml gentamicin and 10 µg/ml penicillin G was added to each well, and incubation

was continued for another 2 h to kill the extracellular bacteria. The macrophage cells

were disrupted by 1 ml of sterile ddH2O and plated onto THB agar medium. For

inhibition of endocytosis as control, before infecting by SEZ, cells were treated with

an endocytosis inhibitor cytochalasin D (2µM) (Gene Operation, Wuxi, Jiangsu,

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China) at 37 °C for 1h. The phagocytosis percent was calculated as “(CFU on plate count/CFU in original inoculum)×100%”18.

3. Results 3.1 Protein identification and quantification

All isotope labeled macrophage proteins identified by LC/MS are listed in

Supplementary Table 1. As mentioned above, two replications were processed in this

experiment to obtain more proteins. In the first replication, 1737 proteins were

identified, 127 of which had different abundances between SEZ ATCC35246 and

ST171-treated Raw264.7 cells. 1510 proteins were identified in the second

replication, 114 of which were differentially abundant proteins. Among the total 222

differentially abundant proteins, 87 were upregulated, with a heavy/light ratio ≥ 1.5,

and 135 proteins were downregulated, with heavy/light ratio ≤ 0.67 (Supplementary

Figure 1). All differentially abundant proteins are listed in Supplementary Table 2.

The SILAC ratio distribution for all identified and quantified proteins is shown in

Supplementary Figure 2. Most of heavy/light ratios of the proteins were distributed

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around 1, indicated that Raw264.7 cells were labeled fully, and the heavy and light

labeled cells were mixed equally. The cutoff ratios were selected according to the

distribution of ratios in a control sample of labeled and unlabeled Raw246.7 cells

lysates mixed equally. The standard deviation (S.D.) was 0.28, the cutoff value of heavy/light should be 20.56 and 2-0.56, equal to 1.474 and 0.678, so we chose 1.5 and 0.67 as the cutoff values, was consistent with other studies19,20.

3.2 Gene Ontology analysis and GO term relationship

In this research, our focus was on the macrophage immunity related proteins that

had different abundances between SEZ ATCC35246 and ST171 infections. Level 2

GO analysis showed that there were 10 proteins belonging to immune system process;

however, no immunity related GO terms were found among the upregulated proteins

(Figure 1A). All GO terms and genes were listed in Supplementary Table 3. In a

further analysis, we focused on the immunity related functions of the downregulated

proteins. Besides these immune related proteins, we also chose some concerned

downregulated genes for the GO over-representation test. This approach was used to

identify GO terms and assess whether the number of selected proteins associated with

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a GO term is larger than expected. Figure 1B shows these GO terms. The relationship

of these GO terms and their proteins are shown in Figure 1B, and the details of each

GO term is listed in Table 2. All of these GO terms had connections with the others

and formed a complex network. This network should be related to macro phenomenon

concerning immunity functions. Using GO GSEA, we analyzed profiles of all the

differentially abundant proteins that could be matched with Entrez IDs. Gene sets

related to immunity function and cellular cytoskeleton are listed in Table 3. Figure 1C

shows the relationships of these gene sets, and the results agreed with the GO

over-representation test: most of the downregulated gene sets were associated with

immunity functions. The upregulated gene sets were related to cytomembrane and

cytoskeleton regulation.

3.3 KEGG pathway analysis and pathways interaction network

We employed the KEGG pathway analysis tools to identify relationships among

the differentially abundant proteins and signal pathways, and illuminate their

connections. Upregulated and down regulated proteins were analyzed separately. The

pathways involving upregulated proteins and their connections are shown in Figure

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2A. Most of pathways belonged to metabolism, except the “Bacterial invasion of

epithelial cells pathway”. The downregulated proteins-related pathways caught our

attention, especially the immunity related pathways (Figure 2B). The connections

between proteins and immunity-related pathways are shown in Figure 2C. Three of

the concerned KEGG pathway map to the downregulated proteins shown in

Supplementary Figure 3, including the T cell and B cell receptor signaling pathways

and antibody mediated phagocytosis. All were related to host immunity defense.

3.4 Verification of the expression levels genes encoding downregulated proteins

We chose 10 of the downregulated proteins from the LC/MS results and checked

the expressions of their encoding genes using qPCR. Figure 3A shows that all 10

genes had lower transcript levels in SEZ ATCC35246-infected Raw264.7 cells than

ST171-infected cells. Although some of them did not match the 1.5 times difference

criterion, the tendency was the same as the LC/MS results. In addition, western

blotting was used to detect the protein abundances of three selected proteins. The

results showed that the expression levels of Ptgs2, Mcl1 and STAT3 in SEZ

ATCC35246-infected Raw264.7 cells were lower than in ST171-infected cells (Figure

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3B), which agreed with the proteomics and transcription results.

3.5 Raw264.7 cells phagocytosis rate and cytokine genes transcription in response to

SEZ ATCC35246 and ST171 infection.

Compared with SEZ ATCC35246, Raw264.7 cells find it easier to ingest ST171.

The plate counting results are shown in Figure 4A, which suggested that

SEZ ATCC35246 has significantly higher phagocytosis resistance than ST171.

qPCR detected four kinds of cytokines. The transcription levels of IL-1α, IL-1β

and IL-6 in Raw264.7 cells infected with SEZ ATCC35246 were significantly lower

than in cells infected with ST171. However, the transcript level of TNF-α showed no

difference. The result is shown in Figure 4B, and indicated that SEZ ST171 infection

might cause a stronger inflammatory response by inducing the increased expressions

of certain cytokines. However, the cytokine storm-related cytokine TNF-α did not

increase significantly.

4. Discussion Swine streptococcosis is prone to outbreaks in the Chinese pig industry,

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especially when pigs have been infected by immunosuppressive viruses, such as

porcine reproductive and respiratory syndrome virus (PPRSV) and porcine circovirus 2 (PCV2) 21-23. SEZ may cause swine streptococcosis, and vaccines have been

developed to protect pigs against SEZ infection, including ST171, which is an

artificially attenuated strain that with a good protection rate. This ST171 vaccine

strain has been used in China widely for many years. In this study, we employed the

SILAC and LC/MS technology identify the differential proteomic response of

macrophages to infection with the SEZ virulent strain ATCC35246 and the vaccine

strain ST171. We also tried to explain how SEZ ATCC35246 affects macrophages and why ST171 lost its virulence. We believed that using the TripleTOFTM 5600, cutting

five slices of gels and using two replications, we would obtain sufficient protein

information. Ultimately, the LC/MS provided more than 2700 isotope labeled proteins

and among them, 222 proteins were identified as differentially abundant proteins.

We analyzed these differentially abundant proteins by GO and KEGG analysis.

The GO analysis results for downregulated proteins identified many immune GO

terms, especially the endocytosis GO term and some other cytokine, inflammatory

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and immune response-related GO terms. Macrophages are professional phagocytes

with exceptionally high endocytic activity. Many kinds of bacteria have evolved

strategies to escape the bactericidal mechanisms associated with phagocytosis. In a

previous study, the antiphagocytosis ability of SEZ was found to be connected with

alternative complement pathways; however, SEZ antiphagocytosis should involve other mechanisms 24. Picalm (Phosphatidylinositol-binding clathrin assembly protein)

and Snx9 (Sorting nexin 9) both function to recruit clathrin and adapter protein

complex 2 (AP2) to cell membranes at the sites of coated-pit formation and clathrin-vesicle assembly 25,26. Other proteins, such as Apobr (Apolipoprotein B receptor), are participate directly or indirectly in macrophage phagocytosis 27. One

reason of the differentiation of virulence between SEZ ATCC35246 and ST171 might

be their different effects on macrophage phagocytosis. SEZ ATCC35246 has many antiphagocytosis strategies 24,28, while ST171 might have lost some of these functions

and could be ingested more easily by macrophages. The characteristics of these two

bacteria might affect their infection processes: SEZ ATCC35246 avoided the host

immune defense and caused the disease, while SEZ ST171 was ingested by

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macrophages and presented to lymphocytes as antigens, providing immune protection

to the host.

In the GO GSEA results, some of the upregulated genes sets were related to

cytomembrane and cytoskeleton regulation. Macrophages can adjust their cellular

membrane and cytoskeleton, making the macrophage stable and efficient, and bacteria

might cause the cellular membrane to lose its integrity or disturb the cytoskeleton to facilitate bacterial infection 29. Upregulated proteins Rab10, Rhog and Rap2a belong

to small G protein superfamily that is involved in regulation of actin dynamics and the

maintenance of cell membrane and cytoskeleton. Basal small G protein activity is

required for homeostatic functions in physiological conditions; however, sustained

over-activation of small G proteins or the spatiotemporal deregulation of small G

proteins activity has pathological consequences for cells, including apoptosis and loos of integrity 30-32. In addition, upregulated proteins Tbcd, Sptbn1 and Txlna are related

to the cellular cytoskeleton. Thus, we considered that the SEZ ATCC35246 might

escape phagocytosis by influencing the cell membrane and cytoskeleton of macrophages, decreasing their immunity functions 33,34.

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Among the upregulated proteins, the KEGG pathways were related to

metabolism, except the “Bacterial invasion of epithelial cells pathway”. Many

pathogenic bacteria can invade phagocytic cells and colonize them intracellularly, then becoming disseminated to other cells 35. Streptococcus might express proteins on

their surfaces that interact with cellular receptors, initiating signal cascades that result in the close apposition of the cellular membrane around the entering bacteria 36. SEZ

ATCC35246 might also infect hosts by this method. The downregulated proteins

formed a network, including “T/B cell receptor signaling pathways”, “Fc gamma

R-mediated phagocytosis” and “Natural killer cell mediated cytotoxicity”. These

pathways are linked directly to immunity functions. Inhibited host immune defense

might be an important reason for the high virulence of SEZ ATCC35246.

There were some notably differentially abundant proteins. CD44 and Irg1

(Immune responsive gene 1) were downregulated during SEZ ATCC35246 infection. CD44 is a cellular surface protein 37, and is a receptor of hyaluronic acid, which is the

basis of SEZ and Group A streptococcus (GAS) capsules. Downregulation of CD44 would decrease the adherence ability of host cells to the bacteria 38. If macrophages

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lost their ability to adhere to SEZ, this would decrease their ability to phagocytose the

bacteria. Irg1 is highly expressed in mammalian macrophages during inflammation,

its downregulation in macrophages resulted in significantly reduced antimicrobial activity during bacterial infections 39,40 and this might be beneficial to SEZ

ATCC35246 infection. Meanwhile, the abundance of these two proteins in SEZ

ST171-infected macrophages allowed this vaccine strains to be treated like a common

antigen by macrophages.

The expression trend of Ptgs2 was interesting. Ptgs2 is responsible for the

production of inflammatory prostaglandins, which are immunosuppressive factors 41,42

. In the SEZ ATCC35246-infected macrophages, Ptgs2 was present in low levels

compared with the control and SEZ ST171-treated macrophages. This reduction in

immunosuppression might be necessary for macrophages to fight against SEZ

infection. By contrast, SEZ ST171, as a vaccine strain, as handled more easily by the

macrophages; therefore, in these macrophage, the Ptgs2 expression level was not as

low as in SEZ ATCC35246 infected macrophage, but a little lower than the control.

Among the upregulated genes, Tbcd, Rhog and Rab10 were related to cytoskeleton

22


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dynamic stability, suggesting that the chaos of macrophage cytoskeleton adjustment would lead to decreased phagocytosis 43.

To verify the bioinformatics results, we detected the phagocytosis rate and

cytokine genes transcription responses to SEZ ATCC35246 and ST171 infection in

the Raw264.7 cells. Raw264.7 found it harder to ingest SEZ ATCC35246 and induced

less cytokine expression, which was in accordance with the GO analysis results. The

phagocytosis rate was higher for SEZ ST171, leading to its presentation to

lymphocytes as an antigen, causing Raw264.7 to express higher levels of cytokines

and providing immune protection to the host.

In conclusion, using SILAC coupled to LC-MS/MS and bioinformatics analysis,

the differential proteomics of SEZ ATCC35246 and ST171 infected Raw264.7 cells

were analyzed. Further experiments were designed to confirm the bioinformatics

results. According to these results, the virulent strain caused cytoskeleton related

genes upregulation might influence the macrophage phagocytosis efficiency, on the

other hand, many cytokines related genes went down reduced the immune response of

macrophage. The different influences on macrophage phagocytosis and cytokine

23



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expression might be important reasons for different infection phenotypes of SEZ

ATCC35246 and ST171.

5. Acknowledgements This work was supported by the National Natural Science Foundation of China

(31302093, 31172319, 31272581); National Key Research and Development Program

(2016YFD0501607); the Ph.D. Program of the Foundation of the Ministry of

Education of China (20130097120024); the Key Project of Independent Innovation of

the Fundamental Research Fund for the Central Universities of Nanjing Agricultural

University (Y0201600144, KYZ201630); and the Priority Academic Program

Development of Jiangsu Higher Education Institutions (PAPD).

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Captions Figure 1. GO analysis of differentially abundant proteins. (A) Level 2 GO

classification. Proteins were classified into level 2 different biological process GO

terms. The x-axis indicates the proteins number of each GO term. (B) The GO

over-representation test of immunity related downregulated proteins. In the bar graph,

the x-axis represents the protein number of the selected proteins; a p-value < 0.01

indicated that the association of these proteins with the corresponding GO term is

28


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larger than expected. (C) GO Gene Set Enrichment Analysis (GO GSEA) results and

their networks. Gene sets in the green areas had positive enrichment score and were

termed the upregulated gene sets, while those in the red areas had negative

enrichment scores and were termed downregulated gene sets. The color of each circle

represents the p-value: as the red fades, the significance decreases.

Figure 2. The KEGG pathway analysis. These results showed the relationships of

the differentially abundant proteins to signal pathways, and illuminated their

connections. (A) The pathways of upregulated proteins and their connections; (B) The

pathways of downregulated genes and their connections. In A and B, the color of each

circle represents the p-value: as the red fades, the significance decreases. (C) The

network of proteins and immunity related pathways are shown in in the blue area.

Figure 3. Verification the downregulated expression genes after 4h of infection.

(A) Genes representing 10 of the downregulated proteins were chosen to verify the

LC/MS results using qPCR. The gene transcription level in SEZ ST171 treated cells

29



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was regarded as 100% and was represented by the whole column, then the gene

transcription level of SEZ ATCC35246 treated cells was compared with it and shown

in blue column; (B) Ptgs2, Mcl1 and STAT3 were chosen to verifying the protein

expression levels. β-actin or GAPDH were used as control proteins. The grayscale of

the reactive protein bands was measured by software and a ratio = grayscale of target

protein/Grayscale of control protein was produced. 1: Negative control; 2: SEZ

ATCC35246 treated cells; 3: SEZ ST171 treated cells.

Figure 4. Raw264.7 cells phagocytosis rate and cytokine genes transcription in

response to SEZ ATCC35246 and ST171 infection. (A) The phagocytic rate of

Raw264.7 cells to SEZ ATCC35246 and ST171 at 2 h and 4 h, cytochalasin D treated

cells were used as control; (B) The transcription levels of IL-1α, IL-1β and IL-6 genes

in Raw264.7 cells infected with SEZ ATCC35246 and ST171 at 6h. (“***” represents

significant different compared to SEZ ATCC35246 treated group, p-value＜0.001)

Table 1. qPCR primers used in this study.

30


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Table 2. Over-represented Go terms from the test results.

Table 3. GO sets from the GO GSEA analysis.

Supplementary Table 1. Information for all proteins identified by LC/MS.

Supplementary Table 2. Information for differentially abundant proteins.

Supplementary Table 3. Details of the GO classification results.

Supplementary Figure 1. Diagram of Raw264.7 cells isotope labeling, SEZ infection

and proteomics analysis by LC/MS.

Supplementary Figure 2. Ratio distribution of the identified proteins.

Supplementary Figure 3. Illustration of the KEGG pathways involving three

downregulated proteins.

31



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For TOC only

32


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Figure 1. GO analysis of differentially abundant proteins. (A) Level 2 GO classification. Proteins were classified into level 2 different biological process GO terms. The x-axis indicates the proteins number of each GO term. (B) The GO over-representation test of immunity related downregulated proteins. In the bar graph, the x-axis represents the protein number of the selected proteins; a p-value < 0.01 indicated that the association of these proteins with the corresponding GO term is larger than expected. (C) GO Gene Set Enrichment Analysis (GO GSEA) results and their networks. Gene sets in the green areas had positive enrichment score and were termed the upregulated gene sets, while those in the red areas had negative enrichment scores and were termed downregulated gene sets. The color of each circle represents the pvalue: as the red fades, the significance decreases. 82x129mm (300 x 300 DPI)



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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 2. The KEGG pathway analysis. These results showed the relationships of the differentially abundant proteins to signal pathways, and illuminated their connections. (A) The pathways of upregulated proteins and their connections; (B) The pathways of downregulated genes and their connections. In A and B, the color of each circle represents the p-value: as the red fades, the significance decreases. (C) The network of proteins and immunity related pathways are shown in in the blue area. 177x144mm (300 x 300 DPI)


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Figure 3. Verification the downregulated expression genes after 4h of infection. (A) Genes representing 10 of the downregulated proteins were chosen to verify the LC/MS results using qPCR. The gene transcription level in SEZ ST171 treated cells was regarded as 100% and was represented by the whole column, then the gene transcription level of SEZ ATCC35246 treated cells was compared with it and shown in blue column; (B) Ptgs2, Mcl1 and STAT3 were chosen to verifying the protein expression levels. β-actin or GAPDH were used as control proteins. The grayscale of the reactive protein bands was measured by software and a ratio = grayscale of target protein/Grayscale of control protein was produced. 1: Negative control; 2: SEZ ATCC35246 treated cells; 3: SEZ ST171 treated cells. 82x153mm (300 x 300 DPI)



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Figure 4. Raw264.7 cells phagocytosis rate and cytokine genes transcription in response to SEZ ATCC35246 and ST171 infection. (A) The phagocytic rate of Raw264.7 cells to SEZ ATCC35246 and ST171 at 2 h and 4 h, cytochalasin D treated cells were used as control; (B) The transcription levels of IL-1α, IL-1β and IL-6 genes in Raw264.7 cells infected with SEZ ATCC35246 and ST171 at 6h. (“***” represents significant different compared to SEZ ATCC35246 treated group, p-value＜0.001) 82x155mm (300 x 300 DPI)


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Table 1. The sequence of primers for RT-PCR Genes

Primers

STAT3-F

TTTACCACGAAAGTCAGGTTGC

STAT3-R

TGCCCAGAATGTTAAATTTCCG

Mcl1-F

GGCCTTCCTCACTCCTGACTTCC

Mcl1-R

CTCCGCAGGCCAAACATGGTC

Ptgs2-F

TGTGCGACATACTCAAGCAG

Ptgs2-R

TGTTGCACGTAGTCTTCGAT

IL1β-F

TCTGAAGCAGCTATGGCAAC

IL1β-R

TTCATCTTTTGGGGTCCGTCA

SHIP1-F

TCTTCCCAAGCTAAAGCCCAT

SHIP1-R

AGCCTTCACCATAGGACTCGT

Hmox1-F

AGGTACACATCCAAGCCGAGA

Hmox1-R

AGCCATCACCAGCTTAAAGCC

CnA-F

GATCTCCTGCCAACACTCGCTA

CnA-R

GTGCCTACATTCATGGTTTCCG

Arrb1-F

AGCGAGACTCCAGTAGACACCA

Arrb1-R

TCCTTGTCATCCTTCATGCCTT

LSC-F

ACTTGACTCACCTACGGCAGA

LSC-R

TTGTCTTTGGTCACTCGCCAC

Cd44-F

ATCCTCGTCACGTCCAACACC

Cd44-R

TAGCGAGTACCATCACGGTT

IL1α-qF

TGACCTGCAGTCCATAACCC

IL1α-qR

TGACAAACTTCTGCCTGACGAG

IL1β-qF

TTTGAAGTTGACGGACCCCAA

IL1β-qR

ACAGCCACAATGAGTGATACTGC

IL4-qF

CAAACGTCCTCACAGCAACGA

IL4-qR

TGCAGCTCCATGAGAACACT

IL6-qF

AAGAAAGACAAAGCCAGAGTCCT

IL6-qR

TCTGTGACTCCAGCTTATCTGT

IL12α-qF

CCTGCACTGCTGAAGACATCG

IL12α-qR

TAGCCAGGCAACTCTCGTTC

IL12β-qF

TTTGTTCGAATCCAGCGCAAG

IL12β-qR

CAGACATTCCCGCCTTTGCAT

TNFα-qF

GCCTCTTCTCATTCCTGCTTGTG

TNFα-qR

GGAGGCCATTTGGGAACTTC



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Page 40 of 41

Table 2. Details of GO over-representation test results. GO Term ID

Description

pvalue

geneID

Count

GO:0030888

regulation of B cell proliferation

0.003

Inpp5d/Nfatc2/Ptprc

3

GO:0050871

positive regulation of B cell activation

0.004

Inpp5d/Nfatc2/Ptprc

3

GO:0006897

endocytosis

0.006

Arrb1/Ehd1/Il1b/Apobr/Picalm/Necap2/Snx9

7

GO:0002821

positive regulation of adaptive immune response

0.008

Cd44/H2-K1/Ptprc

3

GO:0050776

regulation of immune response

0.008

Cd44/H2-K1/Hmox1/Inpp5d/Irg1/Nfatc2/Ptprc

7

GO:0001818

negative regulation of cytokine production

0.008

Arrb1/Hmox1/Inpp5d/Irg1

4

GO:0006954

inflammatory response

0.009

Cd44/Hmox1/Il1b/Irg1/Ptgs2/Stat3/Hnrnpa0

7

GO:0032635

interleukin-6 production

0.011

Arrb1/Il1b/Inpp5d

3

GO:0002684

positive regulation of immune system process

0.011

Cd44/H2-K1/Hmox1/Il1b/Inpp5d/Irg1/Nfatc2/Ptprc

8

GO:0001816

cytokine production

0.011

Arrb1/Hmox1/Il1b/Inpp5d/Irg1/Nfatc2/Ptgs2

7

GO:0042100

B cell proliferation

0.013

Inpp5d/Nfatc2/Ptprc

3

GO:0050864

regulation of B cell activation

0.014

Inpp5d/Nfatc2/Ptprc

3

GO:0002526

acute inflammatory response

0.014

Il1b/Ptgs2/Stat3

3

GO:0002819

regulation of adaptive immune response

0.021

Cd44/H2-K1/Ptprc

3

GO:0001817

regulation of cytokine production

0.021

Arrb1/Hmox1/Il1b/Inpp5d/Irg1/Ptgs2

6

GO:0002683

negative regulation of immune system process

0.024

Cd44/Hmox1/Inpp5d/Irg1/Ptprc

5

GO:0034097

response to cytokine

0.028

Il1b/Irg1/Mcl1/Ptgs2/Ptprc/Stat3

6

GO:1903039

positive regulation of leukocyte cell-cell adhesion

0.028

Cd44/Il1b/Ptprc

3

GO:0002699

positive regulation of immune effector process

0.034

H2-K1/Hmox1/Ptprc

3

GO:0002250

adaptive immune response

0.034

Cd44/H2-K1/Inpp5d/Ptprc

4

GO:0002703

regulation of leukocyte mediated immunity

0.034

H2-K1/Hmox1/Ptprc

3

GO:0050778

positive regulation of immune response

0.034

Cd44/H2-K1/Irg1/Nfatc2/Ptprc

5

GO:0002443

leukocyte mediated immunity

0.036

H2-K1/Hmox1/Inpp5d/Ptprc

4

GO:0042098

T cell proliferation

0.041

Il1b/Ptprc/Dock8

3


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Table 3. GO sets of GO GSEA analysis. ID

Description

Set Size Enrichment Score pvalue

GO:0006904

vesicle docking involved in exocytosis

2

0.815

0.040

GO:0090002

establishment of protein localization to plasma membrane

3

0.779

0.020

GO:0030041

actin filament polymerization

3

0.739

0.020

GO:0030832

regulation of actin filament length

3

0.739

0.020

GO:0030833

regulation of actin filament polymerization

3

0.739

0.020

GO:0008064

regulation of actin polymerization or depolymerization

3

0.739

0.020

GO:0008154

actin polymerization or depolymerization

4

0.655

0.030

GO:0007009

plasma membrane organization

7

0.637

0.010

GO:0032956

regulation of actin cytoskeleton organization

4

0.598

0.030

GO:0032970

regulation of actin filament-based process

4

0.598

0.030

GO:0002262

myeloid cell homeostasis

5

0.574

0.020

GO:0030029

actin filament-based process

8

0.569

0.020

GO:0030036

actin cytoskeleton organization

8

0.569

0.020

GO:0007015

actin filament organization

5

0.559

0.040

GO:0002520

immune system development

10

0.359

0.040

GO:0061024

membrane organization

15

0.350

0.020

GO:0007010

cytoskeleton organization

14

0.322

0.030

GO:0002376

immune system process

20

0.265

0.030

GO:0007165

signal transduction

36

0.211

0.050

GO:0006954

inflammatory response

7

-0.567

0.020

GO:0050777

negative regulation of immune response

4

-0.585

0.050

GO:0002237

response to molecule of bacterial origin

4

-0.596

0.050

GO:0032496

response to lipopolysaccharide

4

-0.596

0.050

GO:0009617

response to bacterium

5

-0.605

0.040

GO:0042035

regulation of cytokine biosynthetic process

2

-0.832

0.030

GO:0042089

cytokine biosynthetic process

2

-0.832

0.030

GO:0042107

cytokine metabolic process

2

-0.832

0.030

GO:0042226

interleukin-6 biosynthetic process

2

-0.832

0.030

GO:0045408

regulation of interleukin-6 biosynthetic process

2

-0.832

0.030

GO:0070487

monocyte aggregation

2

-0.867

0.040

GO:0014904

myotube cell development

2

-0.954

0.010


Virulent and Vaccine Strains of Streptococcus equi ssp

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