extfieldsofstudy: Medicine Chemistry

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Powering the ABC multidrug exporter LmrA: How nucleotides embrace the ion-motive force

Researchers study how different forms of metabolic energy are coupled to drug extrusion by an ATP-binding cassette transporter.

This PDF file includes:
. Mascot search results for mass spectrometry data for purified LmrA-WT. Table S2. Mascot search results for mass spectrometry data for purified LmrA-ΔK388. Table S3. Speciation of HEPES at pH 6.5 as a function of the HEPES concentration. Data analysis S1. Determination of E rev values and ion stoichiometry. Data analysis S2. Comparisons of ion transport models.

Data analysis S1. Determination of E rev values and ion stoichiometry.
We consider a thermodynamic reaction cycle involving the coupled transport of n Na sodium ions from the outside to the inside of the phospholipid bilayer, and n Cl chloride ions, n H protons, and n D drug molecules from the inside to the outside, with z Na , z Cl , z H , and z D representing the charge of these ions. At equilibrium, the free energies of the coupled ions define a zero-flux equation relating the reversal potential (E rev ) to the transmembrane ion gradients: (C) Imposition of HEPES + gradients HEPES can exist in the form of monovalent cationic, zwitterionic, and monovalent anionic species, and has two protonatable moieties, a piperazin moiety with pKa 1 of 3, and a sulfonate moiety with pKa 2 of 7.5 (20 C) in the following equilibria: in which H 2 A + , HA and Arefer to the double protonated form (HEPES + , protonated on piperazin and sulfonate moiety), zwitterionic from (HEPES 0 , protonated on piperazin moiety only) and double deprotonated form of HEPES (HEPES -). Using the Henderson-Hasselbalch equation, the speciation at pH 6.5 was calculated: 2.9*10 -7 7.3*10 -7 2.9*10 -6 3.6*10 -6 [HEPES 0 ] (M) 9.1*10 -4 2.3*10 -3 9.1*10 -3 1.1*10 -2 [HEPES -] (M) 9.1*10 -3 2.3*10 -2 9.1*10 -2 1.1*10 -1 With In our previous measurements of the chemical proton gradient-dependent transport of radioactive 36 Clin LmrA-containing proteoliposomes, we directly established that LmrA mediates the symport of Cland proton with the stoichiometry of 1:1 (10).

Therefore, n H = n Cl (Eq. 7)
Our observations for symmetrical NaCl-containing buffer solutions that the ATP-induced ion conductance by LmrA-WT is directly proportional to the imposed membrane voltage (Fig. 2H) demonstrates the transbilayer movement of one positive charge per transport cycle, with the term: In the schematic below, in and out refer to the inside and outside of the membrane in lactococcal cells. The calculated E rev value of 69.5 mV is close to the experimental E rev value of 66.7 ± 6.1 mV (n = 3) (Fig. 2H). The calculated E rev value of 35.6 mV is close to the experimental E rev value of 37.6 ± 1.5 mV (n = 3) (Fig. 2H) Hence, the model correctly predicts E rev in response to simultaneous changes in ion gradients.

Data analysis S2. Comparisons of ion transport models.
(A) In previous work, Velamakanni and co-workers (10) focused on electrogenic proton-chloride symport by LmrA-MD and LmrA, and proposed that this reaction is based on apparent (1H + -1X + -1Cl -) + co-transport in which X + can be Na + or Drug + (Model 1). The transport of 100 µM 36 Cl -[added as NaCl] was measured in proteoliposomes containing transport proteins in an inside-out fashion. In the following calculations, the predicted direction of transport and accumulation of 36 Clfor Model 1 and 2Na + /(1H + -1Drug + -1Cl -) + exchange (Model 2) are compared with the experimental data.

A2. ∆ -dependent accumulation of 36 Cl -
To artificially impose a ∆ , the proteoliposomes were prepared in 20 mM (K)Pipes (pH 7.6) containing 100 mM K-acetate, and diluted 100-fold into 20 mM N-methyl-D-glucosamine (NMG) Pipes (pH 7.6) containing 100 mM (NMG) acetate in the presence of valinomycin to induce a potassium diffusion potential (∆ , interior negative) of -118.2 mV. The proteoliposomes accumulated 36 Clabout 17.4-fold above the equilibration level obtained for empty liposomes. Model 2 predicts a 100-fold accumulation of 36 Clin the lumen of the proteoliposomes.

Conclusions
With the imposed electrochemical ion gradients in the proteoliposomes, Model 2 and Model 1 predict Claccumulation in a similar range as the experimental values obtained in the study by Velamakanni and co-workers (10). This conclusion is further supported by the notion that the imposed ∆ (based on a potassium diffusion potential) will have been short-lived due to the export of K + from the lumen of the proteoliposomes via valinomycin (Model 1) and valinomycin and LmrA (Model 2).

(B) Further comparisons of ion transport models
In the electrophysiological experiments in Fig. 2H, the imposition of asymmetric solutions ([NaCl] in /[NaCl] out = 10 mM/150 mM) yielded a measured E rev = 66.7 ± 6.1 mV and calculated E rev = 70.5 mV based on Model 2 (data analyses S1 E1).
In the following section the E rev is calculated using Model 1.

Conclusion
Model 2 has a better predictive value of E rev than Model 1.


extfieldsofstudy: Biology Medicine

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Identification of New Factors Modulating Adhesion Abilities of the Pioneer Commensal Bacterium Streptococcus salivarius

Biofilm formation is crucial for bacterial community development and host colonization by Streptococcus salivarius, a pioneer colonizer and commensal bacterium of the human gastrointestinal tract. This ability to form biofilms depends on bacterial adhesion to host surfaces, and on the intercellular aggregation contributing to biofilm cohesiveness. Many S. salivarius isolates auto-aggregate, an adhesion process mediated by cell surface proteins. To gain an insight into the genetic factors of S. salivarius that dictate host adhesion and biofilm formation, we developed a screening method, based on the differential sedimentation of bacteria in semi-liquid conditions according to their auto-aggregation capacity, which allowed us to identify twelve mutations affecting this auto-aggregation phenotype. Mutations targeted genes encoding (i) extracellular components, including the CshA surface-exposed protein, the extracellular BglB glucan-binding protein, the GtfE, GtfG and GtfH glycosyltransferases and enzymes responsible for synthesis of cell wall polysaccharides (CwpB, CwpK), (ii) proteins responsible for the extracellular localization of proteins, such as structural components of the accessory SecA2Y2 system (Asp1, Asp2, SecA2) and the SrtA sortase, and (iii) the LiaR transcriptional response regulator. These mutations also influenced biofilm architecture, revealing that similar cell-to-cell interactions govern assembly of auto-aggregates and biofilm formation. We found that BglB, CshA, GtfH and LiaR were specifically associated with bacterial auto-aggregation, whereas Asp1, Asp2, CwpB, CwpK, GtfE, GtfG, SecA2 and SrtA also contributed to adhesion to host cells and host-derived components, or to interactions with the human pathogen Fusobacterium nucleatum. Our study demonstrates that our screening method could also be used to identify genes implicated in the bacterial interactions of pathogens or probiotics, for which aggregation is either a virulence trait or an advantageous feature, respectively.

INTRODUCTION
Streptococcus salivarius is one of the early colonizers of oral mucosa surfaces in neonates and is a commensal inhabitant of the oral cavity and digestive tract of healthy adults. S. salivarius is thought to exert a range of biological activities related to host health, in particular through its impact on the stability of microbiota composition, and its interaction with the host (Delorme et al., 2015). Despite the role of S. salivarius in both oral and digestive tract ecology, the factors that allow this bacterium to become established and then maintained in the host environment have not yet been the subject of extensive molecular and genetic analyses.
Bacterial adhesion is an initial and critical step in the oral colonization process. Adhesion processes may include attachment of the bacterial cell to host cells, to components of the extracellular matrix (ECM), to the salivary pellicle on teeth and to soluble factors, as well as to bacterial cells of the same strain (autoaggregation) or genetically distinct species (co-aggregation). The ability of S. salivarius to colonize and maintain at multiple niches throughout the lifespan of its host suggests that this bacterium has evolved various adhesive strategies (Delorme et al., 2015). Numerous studies have described the remarkable capability of S. salivarius to bind to a wide range of biological surfaces, including buccal, hypopharyngeal, bronchial and cervicovaginal epithelial cell lines (Handley et al., 1987;Cosseau et al., 2008;Guglielmetti et al., 2010;Burton et al., 2013), various proteins in human saliva and of the ECM (Kilian and Nyvad, 1990;Schenkels et al., 1993;Couvigny et al., 2017) and to MUC2, a glycoprotein found in saliva and on mucosal surfaces of the ileum and colon (Kilian and Nyvad, 1990;Schenkels et al., 1993;McGuckin et al., 2011). S. salivarius is also able to form auto-aggregates (Couvigny et al., 2017) and to co-aggregate with numerous oral microorganisms such as the early colonizers Veillonella McBride, 1980, 1981;Handley et al., 1987) and Prevotella species (Levesque et al., 2003), the intermediate colonizers Fusobacterium nucleatum (Weerkamp and McBride, 1980;Levesque et al., 2003) and Candida albicans (Nikawa et al., 2001;Levesque et al., 2003), and the late colonizers Tannerella forsythia (Shimotahira et al., 2013) and Porphyromonas gingivalis (Levesque et al., 2003).
Our knowledge of the factors mediating adhesion of S. salivarius to biological surfaces remains limited. It has been shown that cell wall-associated fimbriae and fibrils are involved in this adhesion process. Indeed, fimbriae are responsible for adhesion to cervicovaginal epithelial cells and co-aggregation with Prevotella intermedia (Levesque et al., 2003;Burton et al., 2013), whereas fibrils mediate co-aggregation with Veillonella species and adhesion to various host surfaces (Weerkamp and McBride, 1981;Weerkamp and Jacobs, 1982). However, the genes encoding the constituent proteins of these structures remain to be identified. Recently, we showed that SrpB and SrpC, two adhesins belonging to the SRR (Serine-rich repeat) glycoprotein family, mediate auto-aggregation and adhesion to a wide range of epithelial cells and ECM proteins (Couvigny et al., 2017). SrpB/C proteins are glycosylated by the GtfE/F glycosyltransferases and are secreted through a dedicated transport system (i.e., the accessory SecA2/Y2 system) to the cell surface where they form fibril-like structures. Genes involved in SRR transport are located in the conserved secA2/Y2 genomic cluster, encoding the SecA2 motor protein, the SecY2 membrane translocation complex, the Asp1/2/3/4/5 chaperones and the GtfA/B/C/D glycosyltransferases. Components of the SecA2/Y2 system and GtfE/F are required for adhesion of S. salivarius to ECM and to epithelial cells and for auto-aggregation through their activities on SrpB/C (Couvigny et al., 2017).
The objective of this study was to identify novel factors that influence the ability of S. salivarius to form biofilms and host-cell interactions. Measurements of auto-aggregation are widely used as a method for evaluating the adhesion capacity of bacteria. We developed a strategy, based on immobilization of cell clusters in semi-liquid agar medium, to select mutants with increased and decreased auto-aggregation phenotypes. We then identified the genes involved and evaluated the ability of the auto-aggregation mutants to bind various host surfaces, to co-aggregate with F. nucleatum and to form biofilms.

Bacterial Strains, Growth Conditions, and DNA Manipulation
Bacterial strains used in this study are listed in Table 1. S. salivarius strains were grown as described previously (Couvigny et al., 2015b). F. nucleatum DSM 20482 was grown in brain heart infusion broth supplemented with 0.25% L-glutamic acid. Erythromycin (5 µg/ml) or kanamycin (1000 µg/ml) were added to the medium as required. PCRs were performed using Phusion R high-fidelity DNA polymerase (New England Biolabs, Ipswich, MA, United States). The primers were purchased from Eurofins MWG Operon (Germany) and are listed in Supplementary Table S1. When necessary, PCR products and DNA restriction fragments were purified using QIAquick kits (Qiagen). Plasmids were purified using the QIAprep Miniprep kit (Qiagen).

Isolation of Mutants with Altered Sedimentation Properties
Mutants with altered auto-aggregation properties were isolated using Todd Hewitt broth with 0.5% yeast extract with a low (0.03%) concentration of agar (Mercier et al., 2000(Mercier et al., , 2002Llull et al., 2005). In these conditions, the extent of free displacement of bacteria in the environment depends on the viscosity of the semi-liquid (SL) medium.

Genome-Wide Mutagenesis and Characterization of Transposon Targets
Mutagenesis with pGhost9::ISS1 was performed essentially as described previously (Guedon et al., 2001). Briefly, cells containing pGhost9::ISS1 were grown overnight at 30 • C in M17 medium in the presence of erythromycin. Stationaryphase cultures were diluted 1:1000 in fresh M17 broth without erythromycin, incubated for 150 min at 30 • C and then at 38 • C for 150 min. Samples were then diluted and plated at 38 • C. Clones that grew on M17 medium containing erythromycin were selected as transposon mutants. Approximately 10,000 mutants were pooled and the mixture was deposited on the surface of 20 flasks containing SL medium and erythromycin. After 140 h of growth, 40 samples at the ends of the roots were removed and streaked out on agar plates to isolate single colonies of auto-aggregation mutants. The pGhost9::ISS1 insertion site was identified by cloning and sequencing of the chromosomal junctions as described previously (Couvigny et al., 2015b).

Whole-Genome Sequencing of Spontaneous Auto-aggregation Mutant Strains
Streptococcus salivarius spontaneous mutants JIM9443 and JIM9447 were sequenced using the Illumina HiSeq 2000 system at I2BC 1 , with around 7 million paired-end 100 baselong reads. Strains JIM9442, JIM9445, JIM9446, and JIM9448 were sequenced using the SOliD technology 4 platform at MetaGenoPoliS 2 , with around 5 million single 50 base-long reads. SOLiD reads containing adapter and/or barcode fragments with a mean quality value <20 and low-quality reads (with 3 or more "N") were discarded. Clean reads were mapped to the reference JIM8777 strain using Bowtie (Langmead et al., 2009) and variations were detected using the Tablet software (Milne et al., 2013). The Illumina and SOLiD sample sequences are available in the SRA database (project ID PRJEB23560).

Construction of Deletion Strains by Natural Transformation
Mutant derivatives of strain JIM8777 were constructed by exchanging the coding sequence of a target gene (sequence between the start and stop codon) for an erythromycin or kanamycin resistance cassette devoid of a transcriptional regulatory region (e.g., promoter or terminator), as described previously (Couvigny et al., 2015b). After peptide-induced transformation, integration of the antibiotic cassette at the appropriate location was verified by PCR (Supplementary  Table S1).

Adhesion Assay
Adhesion of S. salivarius strains to HT-29 human epithelial cell lines (colon adenocarcinoma; ATCC HTB-38) and MUC2 proteins (M2378, Sigma) was performed as described previously (Couvigny et al., 2017). Experiments were repeated at least three times, each in duplicate.

Electron Microscopy
Transmission and scanning electron microscopy (TEM and SEM, respectively) experiments were conducted as described previously (Couvigny et al., 2017). Images were acquired and analyzed at the MIMA2 microscopy and imaging platform 3 .

Auto-aggregation and Co-aggregation Assays
The ability of bacterial cells to auto-aggregate and co-aggregate was assessed by a spectrophotometric procedure (Handley et al., 1987). Stationary-phase cells from overnight cultures were centrifuged, washed twice with PBS and resuspended in aggregation buffer to an optical density at 600 nm (OD 600 ) of 0.5 ± 0.05 (Burton et al., 2013). To determine percentage auto-aggregation, bacterial suspensions were placed in a glass test tube, vortexed, and the suspensions immediately transferred to a cuvette at room temperature (20 ± 1 • C). The OD 600 of the bacterial suspensions was monitored at various time points and the percentage auto-aggregation was expressed as follows: 1 − OD t OD t0 × 100, where OD t represents the OD 600 of the suspension at time t (4, 20, or 24 h) and OD t0 the OD 600 at time zero. To evaluate the impact on auto-aggregation of various pre-treatments of bacteria, cell suspensions prepared in aggregation buffer as described above were treated with EDTA (2 mM, instantaneous), Ca 2+ (CaCl2, 2 mM, instantaneous), proteinase K (0.1 M, 2 h, 37 • C), trypsin (0.1 M, 2 h, 37 • C) and by heat (85 • C, 10 min). Proteinase K-and trypsin-treated cells were harvested by centrifugation and the pellets washed twice in aggregation buffer before the auto-aggregation assay. To determine percentage co-aggregation, suspensions of S. salivarius strains were combined with an equal volume of a test strain (F. nucleatum) or aggregation buffer (as a control) and incubated at room temperature, without agitation, for at least 6 h. The percentage co-aggregation was calculated using the following equation: 100 × [OD 600 (S. salivarius control + F. nucleatum control)/2 − OD 600 (S. salivarius + F. nucleatum)]/OD 600 (S. salivarius control + F. nucleatum control)/2, where OD 600 (S. salivarius control + F. nucleatum control) represents the OD 600 of control tubes containing only the S. salivarius or F. nucleatum strain at time t and OD 600 (S. salivarius + F. nucleatum) represents the OD 600 of the mixture of both suspensions at time t.
Experiments were repeated at least three times, each in duplicate.

Biofilm Formation
Biofilms were grown in microscope grade µclear R base 96well plates (Greiner Bio-one, France) and analyzed by confocal laser scanning microscopy (CLSM) as described previously (Couvigny et al., 2015b). Briefly, surface-associated bacteria were fluorescently labeled with 5 µM Syto9 (Invitrogen, France) and analyzed under a Leica SP2 CLSM at the MIMA2 platform. Cells labeled with Syto9 were exposed to an argon laser beam, set to 20% of its maximal power, at 488 nm. The resulting fluorescence was collected in the 500-550 nm range with a photomultiplier. Images (512 pixels × 515 pixels) were recorded through a water immersion long distance objective (NA = 0.8) at micrometer intervals through the thickness of the biofilm. Three independent experiments were performed for each strain. Three-dimensional projections of the biofilm structures were reconstructed using the Easy 3D function of the IMARIS 7.0 software (Bitplane, Switzerland). Extraction of quantitative biofilm geometric descriptors from the CSLM series was performed with the PHLIP Matlab-based image analysis toolbox (Mueller et al., 2006).

Statistical Analysis
Variables are expressed as the means and standard deviations of results obtained from three (auto-aggregation, coaggregation, and adhesion assays) or two (CLSM analysis) independent experiments. Comparisons between groups of variables were analyzed using the student's t-test. GraphPad Prism version 6 (GraphPad software Inc., La Jolla, CA, United States) was used for statistical analyses. A P-value < 0.05 was considered to be statistically significant.

Auto-aggregation of S. salivarius
Twenty-eight representative strains of S. salivarius were screened for auto-aggregation ( Figure 1A and Table 1). Most of the S. salivarius isolates tested under our culture conditions formed aggregates, although their auto-aggregation ability was found to be highly strain specific (ranging from 9 to 56%). We chose one of the most strongly aggregating strains, JIM8777, for further study. The chromosome sequence of JIM8777 has already been determined .
To characterize the environmental factors influencing JIM8777 auto-aggregation and to clarify the nature of the interactions, we evaluated the impact of various treatments on auto-aggregation levels. Auto-aggregation was most efficient at pH 8 ( Figure 1B). We also found that addition of Ca 2+ led to an increase in auto-aggregation, whereas heat-treatment, addition of EDTA, proteinase K or trypsin led to a reduction in auto-aggregation (Figure 1C), suggesting that proteins are involved in these cell-to-cell interactions.
Use of Semi-liquid Medium to Isolate S. salivarius JIM8777 Mutants with Altered Auto-aggregation Phenotypes To identify JIM8777 genes involved in auto-aggregation, we first isolated spontaneous mutants, which exhibited increased or decreased auto-aggregation phenotype. For this, we used a strategy initially developed for selection of mutants with alterations in their ability to form chains. This method is based on differential sedimentation of bacteria in semi-liquid (SL) agar medium according to chain-forming capacity (Mercier et al., 2000(Mercier et al., , 2002Chapot-Chartier et al., 2010). In these conditions viscosity of SL medium restricts free displacements of bacteria and sedimentation represents the main means of displacement for bacteria which do not have properties of autonomous motility. When colony of non-chain-forming bacteria initiates from one cell, simultaneous growth and sedimentation results in streaked colony (Llull et al., 2005). Contrariwise, chain-forming bacteria in these conditions are trapped in extracellular agar matrix and do not sediment, consequently forming round colonies. It is possible to observe and to select mutants escaping such immobilization as they form faster sedimenting "roots" from round colonies (Mercier et al., 2002).
We hypothesized that similarly to chain-forming bacteria, the auto-aggregates of JIM8777 strain will counteract sedimentation, resulting in formation of round colony. In keeping with this reasoning, we found that the auto-aggregates of the JIM8777 strain became immobilized in the SL medium (Figure 2A,  and selection of spontaneous (Middle) and IS-induced (Right) mutants. Black arrow indicates round colonies formed by the auto-aggregating wild-type cells in SL conditions. Red and blue arrows indicate faster-sedimenting and slower-sedimenting mutants appearing as hanging down roots and as caps from round colonies, respectively. (B,C) Auto-aggregation abilities of the wild-type (JIM8777) and two spontaneous sedimenting mutants in liquid medium. Faster-(red, JIM9445) and slower-sedimenting (blue, JIM9443) mutants in SL agar medium correspond to non-and hyper-auto-aggregation mutants in liquid medium, respectively. left panel, black arrow). After 140 h of growth, we observed the appearance of "roots" and "caps" from these round colonies, presumably formed by faster-and slower-sedimenting spontaneous mutants, respectively (Figure 2A, left and middle panels, red and blue arrows). Samples of the roots and of the caps were then removed and streaked out on agar plates to isolate single colonies of SL medium sedimenting mutants. Analysis of bacterial growth in liquid medium confirmed that selected mutants exhibited altered auto-aggregation phenotypes (representative images of the two groups of mutants are presented in Figures 2B,C). Compared to the parental strain JIM8777, the slower-sedimenting mutants (such as JIM9443) showed a hyper-auto-aggregating phenotype in liquid medium, whereas the faster-sedimenting mutants (including JIM9445) showed diminished auto-aggregation capacity (Figures 2B,C).
We then performed a similar experiment in which bacterial cells containing the whole genome-wide random insertion library of S. salivarius (obtained after pGhost9::ISS1 mutagenesis) was inoculated on the top of a flask with SL medium. After 140 h, the faster-sedimenting "roots" appeared and were collected for further analysis (Figure 2A, right panel).
This experiment led to the isolation of 40 additional fastersedimenting mutants.

Identification of Auto-aggregation Factors of S. salivarius JIM8777
The gene mutations potentially associated with the altered sedimentation and auto-aggregation phenotypes were mapped by whole genome sequencing of 6 spontaneous mutants and cloning and sequencing of the chromosomal DNA fragments flanking the insertion site of ISS1 from 11 insertion mutants. Altogether, 12 genes were identified from the two independent experiments ( Figure 2D and Table 2). Two different spontaneous mutations were identified in the asp1 gene, as well as two different ISS1 insertions in the genes cshA, secA2, gtfG and gtfH. Our independent selection of several mutations in the same gene confirms the importance of these genes in the auto-aggregation phenotype.
To confirm the phenotypes of the bacterial mutants and the correlation between sedimentation in SL conditions and auto-aggregation in liquid medium, we constructed a series of mutants in the JIM8777 background carrying deletions of the genes in which we identified the point mutations associated with altered sedimentation (Table 1). We found that deletion of the asp1, asp2, bglB, cshA, gtfE, gtfG, gtfH, liaR, secA2, and srtA genes, point mutations in which induced a faster-sedimenting phenotype in SL conditions, led to a decreased auto-aggregation phenotype in liquid medium ( Figure 2E). As cwpB and cwpK, two genes coding for enzymes predicted to synthetize cell wall polysaccharides (WPS), are located in the same operon ( Figure 2D), we chose to include only one of these genes (cwpK) for further analyses and confirmed that the cwpK mutant showed an increased auto-aggregation phenotype ( Figure 2E). Altogether, our results confirmed that deletion of the genes identified as having an impact on bacterial sedimentation also affected the auto-aggregation properties of S. salivarius. This confirms that our method, based on differential sedimentation in SL conditions, provided an efficient tool for the specific isolation of mutants with altered auto-aggregation properties.

Capacity of JIM8777 Derivatives to Adhere to Host Surfaces and to Co-aggregate with Fusobacterium nucleatum
We investigated the ability of the auto-aggregation mutants to adhere to the human colonic epithelial cell line HT-29 (Figure 2F), to the mucin 2 (MUC2) glycoprotein ( Figure 2G) and to the human pathogen F. nucleatum (co-aggregation) ( Figure 2H). In comparison to the wild-type JIM8777, 5 out of 10 of the deletion mutants with altered auto-aggregation capacity ( asp1, asp2, gtfE, secA2, and srtA) displayed a significant (P < 0.005) reduction in adhesion to both HT-29 and MUC2 (Figures 2F,G). Moreover, the cwpK (increased autoaggregation) and srtA (decreased auto-aggregation) deletion strains showed a drastic reduction (>90%; P < 0.0001) in co-aggregation with F. nucleatum compared to the wild-type strain ( Figure 2H). The co-aggregation ability of the gtfG strain was also altered but to a lesser extent (50% reduction). Similar results were obtained with the spontaneous mutants asp1 spont1 (JIM9445), asp2 spont (JIM9447), cwpB spont (JIM9442) and cwpK spont (JIM9443) (data not shown).

Role of JIM8777 Auto-aggregation Factors in Biofilm Development
Cell aggregation and biofilm formation are interconnected bacterial properties (Sanchez et al., 2010;Couvigny et al., 2017). We evaluated whether the auto-aggregation factors we had identified also contributed to biofilm formation by analyzing biofilms formed by JIM8777 derivatives and comparing them with those of the wild-type strain by confocal microscopy. After 5 h of sessile growth, all mutants were able to form biofilms under the conditions employed. JIM8777 formed a relatively dense and filamentous biofilm that covered the  , maximum coverage (%) and maximum height (µm) were normalized against values obtained for JIM8777 and measured for each strain in nine different CLSM image stacks from two independent experiments. Student's t-test was used for statistical analysis ( * * * * P < 0.0001, * * * P < 0.0005, * * P < 0.005, * P < 0.05).
entire available surface (Figure 3). Biofilms formed by the mutants differed in their architecture from JIM8777 (P < 0.05 compared to wild-type for at least one geometric descriptor of the biofilm structure). With the exception of strain gtfG, all non-auto-aggregative strains (asp1 spont1 , asp1, asp2, bglB, gtfE, gtfH, srtA) developed compact and uniform biofilms. Hyper-auto-aggregative mutants (cwpB spont , cwpK spont and cwpK) formed excessively filamentous biofilms compared to wild-type. This result indicates that the S. salivarius factors implicated in cell-to-cell interactions in planktonic conditions also influence the morphology and spatial organization of bacterial biofilm communities.

Evaluation of the Cell Surface
Morphology of the cwpK and asp1

Mutants by Electron Microscopy
To provide insight into the role played by the products of the cwpK and asp1 genes in auto-aggregation, we evaluated the cell surface morphology of the corresponding mutants by scanning and transmission electron microscopy (SEM and TEM, respectively). We observed by SEM that the JIM8777 cells exhibited a rough and irregular surface that was partially covered by fibril-like structures ( Figure 4A and Supplementary Figure S1). The cwpK mutant displayed a smooth surface with fibril-like structures, whereas the asp1 mutant exhibited a rough surface without fibrils. Furthermore, the cwpK asp1 mutant exhibited an additive phenotype, with both a smooth and bald surface. These findings suggest that surface roughness is linked to WPS biosynthesis involving CwpK, whereas Asp1 may be responsible for the presence of the extracellular fibrils. TEM of osmium tetroxide-stained cells of the wild-type strain revealed the presence of an electron dense layer outside the cell wall, potentially corresponding to WPS (Figure 4B). The overall morphology of JIM8777 and asp1 was similar, however, the electron-dense layer was completely absent in micrographs of the cwpK and cwpK asp1 strains. A similar result was obtained with the spontaneous cwpK spont (JIM9443) mutant, confirming that the point mutation resulted in loss of function (Supplementary Figure S2).
We have shown previously that the SecA2/Y2 transported glycoproteins form fibril-like structures that can be observed outside of the cell wall of ruthenium red-stained cells (Couvigny et al., 2017). Accordingly, here we found that the fibrillar layer (mainly composed by SRR glycoproteins) was greatly diminished in the asp1 and cwpK asp1 mutants (Figure 4C), as well as in the asp1 spont1 and srtA strains (Supplementary Figure S2). Altogether, these results indicate that CwpB and CwpK are involved in WPS production and show that Asp1 and SrtA play a role in fibril detection, and therefore in the SRR glycoprotein synthesis pathway. It should also be noted that the surface glycoprotein layer appeared denser in the cwpK mutant background (i.e., cwpK and cwpK asp1), suggesting that WPS may hamper glycoprotein formation, exposure, or detection.

DISCUSSION
In this study, we show that auto-aggregation is a property of many isolates of the pioneer colonizer and commensal bacterium S. salivarius. Auto-aggregation is influenced by a variety of environmental factors and involves bacterial proteins (Figure 1). To identify the proteins determining cell-to-cell interactions of a highly auto-aggregating S. salivarius strain (JIM8777), we developed a screening strategy based on immobilization of cell clusters in SL medium that allowed us to select for mutants with altered auto-aggregation. Twelve proteins involved in different aspects of auto-aggregation in S. salivarius were identified. It has been shown previously that the surfaceexposed adhesin, SrpB, mediates auto-aggregation in S. salivarius JIM8777 (Couvigny et al., 2017). Our screen led us to identify mutations in several genes coding for components of the maturation and secretion pathway of SrpB that had a negative impact on the bacterial auto-aggregation phenotype: the SecA2 motor protein, the Asp1 and Asp2 chaperones, the GtfE glycosyltransferase and the SrtA sortase. We also demonstrated that the SrtA sortase is directly involved in SRR glycoprotein surface exposure. Redundancy of mutations in the SrpB pathway confirmed the importance of this pathway in S. salivarius autoaggregation.
New determinants of S. salivarius auto-aggregation were also identified by our screening method in SL medium: CshA, BglB, CwpB, CwpK, GtfG, GtfH and LiaR. Homologs of these genes in other streptococcal species have already been reported to contribute to auto-aggregation. The Streptococcus gordonii adhesin CshA exhibits auto-aggregative properties (McNab et al., 1999) and the BglB homolog in Streptococcus mutans contributes to auto-aggregation by linking bacterial cells and extracellular molecules of glucan (Shah and Russell, 2004;Lynch et al., 2007).
Two enzymes (CwpB and CwpK), which are required for WPS biosynthesis in S. salivarius JIM8777, also participate in auto-aggregation. However, in contrast to the other genes identified, mutations in cwpB and cwpK caused an increase in auto-aggregation. A similar increase in auto-aggregation has already been associated with mutations in the WPS locus of Streptococcus mitis (Rukke et al., 2012). It is possible that WPS in JIM8777 blocks surface accessibility to S. salivarius adhesins, as demonstrated for WPS in Streptococcus pneumoniae (Sanchez et al., 2011). Interestingly, we found that the surface of S. salivarius cwp mutants displayed a much denser external fibrillar layer than the wild-type strain JIM8777, also suggesting that WPS could interfere with surface exposure of auto-aggregation promoting cell surface factors, such as SrpB.
We also identified two additional S. salivarius autoaggregation factors: the GtfG and GtfH glycosyltransferases. Glycosyltransferases participate in the maturation and/or synthesis of various extracellular compounds, including glucans, cell wall polysaccharides, lipoteichoic acid, peptidoglycan and glycoproteins. Therefore, mutations in genes encoding the GtfG and GtfH glycosyltransferases may affect some of these compounds, leading to consequences for the auto-aggregation capacity of these mutants.
The ability of bacterial cells to form auto-aggregates is considered as an advantageous feature under certain environmental conditions, however, excessively large cell clusters may increase the probability of their clearance (Koop et al., 1989). This suggests that bacteria have finely tuned regulatory mechanisms to ensure optimal levels of auto-aggregation. In this study, we provide evidence that auto-aggregate formation is both positively and negatively modulated by various environmental factors and several bacterial components, indicating that their formation is a dynamic and controlled process. LiaR may be part of this regulatory network: we found mutations in the gene encoding this protein led to decreased auto-aggregation. LiaR homologs are implicated in the cell wall stress response and control expression of several surface proteins (Suntharalingam et al., 2009;Klinzing et al., 2013), which may have an effect on auto-aggregation. Our results indicate that auto-aggregation is a multifactorial process in S. salivarius but further investigations are required for a more comprehensive assessment of how these factors act and interplay.
Auto-aggregation measurements are widely used as an indirect method for evaluating the adhesion capacity of bacteria, including potential probiotics (Saeed and Heczko, 2007;Walter et al., 2008;Xu et al., 2009;Piwat et al., 2015). In line with this strategy, we identified mutations in genes that also affect adhesion of S. salivarius to host surfaces, such as MUC2 and HT-29 cells (i.e., asp1, asp2, gtfE, secA2, and srtA), and co-aggregation with the human pathogen F. nucleatum (i.e., cwpK, gtfG, srtA). As mentioned above, these mutations impact the SRR glycoprotein synthesis pathway. SRR glycoproteins, which are important for colonization and persistence in mice, also mediate adhesion of S. salivarius to host cells and hostderived molecules, such as MUC2 (Couvigny et al., 2017). In addition to their effect on SRR glycoproteins, GtfE glycosylates numerous other extracellular proteins in S. salivarius (Couvigny et al., 2017) and the genome of S. salivarius JIM8777 encodes 23 potential SrtA substrates (i.e., LPTXG-containing proteins) (Delorme et al., 2015). The activity of SecA2, Asp1 and Asp2 is strictly dedicated to SRR glycoprotein synthesis, whereas SrtA and GtfE may have a much broader role and additional proteins, other than those transported by the SecA2/Y2 system, could be involved in the adhesive phenotypes observed in this study.
We also identified mutations affecting co-aggregation with F. nucleatum, a multifaceted bacterium that plays a pivotal role in oral biofilm community development (Kolenbrander and London, 1993) and is associated with periodontal disease and colon cancer (Han, 2015). It has been shown that coaggregation results predominantly from the specific interaction of a cell-surface protein of a given/specific bacterium with a polysaccharide receptor expressed on the surface of a second bacterium (Rickard et al., 2003). The requirement for this type of interaction would explain the decreased co-aggregation phenotype of cwpB and cwpK mutants of S. salivarius. It also suggests that the WPS of S. salivarius act as receptors for the binding of F. nucleatum. This explanation is supported by published data showing that WPS expressed by other oral bacteria are implicated in co-aggregation with F. nucleatum (Rosen and Sela, 2006). In addition to mutations in WPS genes, co-aggregation was also decreased in the srtA and gtfG mutants, indicating the existence of at least one more S. salivarius determinant that remains to be identified. This determinant is likely to be a glycosylated SrtA-anchored surface protein, which may function as an adhesin. This unknown protein is not transported by the SecA2Y2 system and is not a LPXTGcontaining CshA or SRR protein. Thus, S. salivarius JIM8777 appears to express both a cell surface adhesin and a carbohydrate receptor specific for the interaction with F. nucleatum. This may provide a partial explanation for the strain specificity of the coaggregation reaction (Weerkamp and McBride, 1980;Handley et al., 1987;Arzmi et al., 2015;Barbosa et al., 2015;Lima et al., 2017).
In summary, we have identified several auto-aggregation factors from our screen of a random insertion mutant library and selection of spontaneous mutants in SL medium. All of these factors influence biofilm architecture, demonstrating that cell-cell interactions governing auto-aggregation and biofilm development are interconnected. Some of these factors also contribute to the adhesion of S. salivarius to host biological substrates, including MUC2, the human colonic epithelial cell line HT-29, and F. nucleatum. These autoaggregation factors may exert key functions in terms of human health and disease by playing roles in bacterial colonization and persistence, microbial community development and pathogen elimination. Identification of these factors paves the way for further molecular studies. In addition, our results demonstrate that the screening method developed in this study can be used to identify the genes involved in bacteria-bacteria and bacteria-host interactions. This method could be especially useful if applied to isolates, for which molecular biology tools have not yet been developed. Our screen may also facilitate molecular studies of microbial community development of pathogenic or probiotic bacteria, for which aggregation is either a virulence trait or beneficial characteristic, respectively.


Necroptosis Signaling Pathways in Stroke: From Mechanisms to Therapies

It has been confirmed that apoptosis, autophagy and necrosis are the three major modes of cell death. For a long time, necrosis is regarded as a deranged or accidental cell demise. In recent years, there is evidence showing that necrotic cell death can be a well regulated and orchestrated event, which is also known as programmed cell death or “necroptosis”. Necroptosis can be triggered by a variety of external stimuli and regulated by a caspase-independent pathway. It plays a key role in the pathogenesis of some diseases including neurological diseases. In the past two decades, a variety of studies have revealed that the necroptosis related pathway is activated in stroke, and plays a crucial role in the pathogenesis of stroke. Moreover, necroptosis may serve as a potential target in the therapy of stroke because genetic or pharmacological inhibition of necroptosis has been shown to be neuroprotective in stroke in vitro and in vivo. In this review, we briefly summarize re-cent advances in necroptosis, introduce the mechanism and strategies targeting necroptosis in stroke, and finally propose some issues in the treatment of stroke by targeting necroptosis

INTRODUCTION
Stroke is caused by a poor blood flow to the brain, and can be divided into two main types: ischemic stroke and hemorrhagic stroke. Although the incidence of stroke is still increasing, the mortality of stroke tends to reduce in recent years due to the reinforcement of patients' education on stroke prevention [1], early diagnosis and therapy [2], establishment of stroke unit [3], timely thrombolysis [4], and improvement of quality of care [5]. However, stroke is still the second most common cause of death following ischemic heart disease, and accounts for 9% of all deaths world wide [6]. In addition, it has high disability and recurrence rate, increasing the economic burdens to the family and the society. Currently, the available therapeutic strategies for stroke include thrombolytic therapy, anticoagulants, anti-platelet drugs, and neuroprotective agents such as reactive oxygen species (ROS) scavenger [7], endothelial nitric oxide synthase activator [8], mammalian target of rapamycin activator or inhibitor [9], and tumor necrosis factor receptorassociated factor 1 inhibitor [10]. However, thrombolytic therapy is limited due to the short time window and the efficacy of other therapies has not been confirmed in clinical trials so far. Thus, more effort is needed to develop new therapies for stroke on the basis of further understanding of the pathogenesis of stroke.
Stroke may trigger a variety of biochemical and molecular mechanisms leading to the neurological dysfunction including the activation of excitotoxic glutamatergic signaling [11], ionic imbalance [12], and excess production of ROS [7,13]. The scathing mechanisms may proceed via nonprogrammed cell death (necrosis) or by programmed cell death (apoptosis or necroptosis) [14]. It has been confirmed that apoptosis, autophagy and necrosis are the three major modes of cell death [15]. Studies have revealed that cerebral ischemia may cause both necrosis and apoptosis of cells in the brain. At the infarct zone, neurons are mainly necrotic, while the main death form of neurons is apoptosis in the ischemic penumbra or the perifocal tissues during the first few hours after a stroke [16][17][18]. Autophagy takes an important role in adapting to the nutritional deprivation and eliminating aggregated proteins, but inappropriate activation of autophagy also in turn causes cell death in the ischemic brain injury. Thus, autophagy has been regarded as a double-edged sword with therapeutic potential in cerebral ischemia [19].
In the early 1970s, Kerr et al. put forward the term "apoptosis", a regulated cell death process, which is morphologically distinct from necrosis [20]. For a long period, necrosis is regarded as a deranged or accidental cell demise that is a passive process caused by overwhelming stress. Since the 1990s, the purely unregulated nature of necrosis was challenged that necrotic cell death, at least in part, can be a well regulated and orchestrated event as apoptosis [21,22]. The accumulated knowledge has paved the way for the term of "programmed necrosis", which has not been introduced by Chan et al. until 2003 [23]. In 2005, Degterev et al. found that necrostatin-1 (Nec-1) could inhibit necrotic cell death mediated by downstream regulator receptor-interacting protein 1 (RIP1) of Fas/tumor necrosis factor receptor 1 (TNFR1), rather than apoptosis, in the condition of caspase-8 inhibition in a model of ischemic brain injury. Furthermore, Nec-1 has a longer-time window in contrast of Z-VAD-fmk, an inhibitor of apoptosis [24]. These results show that this type of necrotic cell death can be controlled and occur later than apoptosis, and is termed "necroptosis" later [25]. With our understanding of the mechanism of necroptosis in ischemic neurons, it is possible to offer neuroprotection by interfering with necrosis. Caspase-8 activity is partially dependent on the level of ATP, which is insufficient for the maintenance of caspase-8 activity in the acute stage of cerebral ischemia because of a serious shortage of ATP production. Therefore, necroptosis is probably the major route of death of neurons in the acute stage of cerebral ischemia [26]. Moreover, therapy targeting necrosis has a longer-time window, which is clinically important. Thus, the elucidation of mechanisms underlying the necroptosis of neurons and other cells in the brain is helpful to develop new therapeutic targets and effective strategies for the treatment of stroke.
Recently, studies have implicated necroptosis in a variety of disease states, and it has a central pathophysiological relevance in some diseases including stroke [27], myocardial ischemia/reperfusion (I/R) injury [28], atherosclerosis [29], renal I/R injury [30], pancreatitis [31], inflammatory bowel disease [32] and other disorders. In this review, we briefly summarize recent advances in the necroptosis and introduce the mechanisms of necroptosis and clinical strategies targeting necroptosis in the therapy of stroke.

CHARACTERISTICS OF NECROPTOSIS
Necroptosis has some distinctive features compared to other modes of cell death, especially apoptosis (Table 1). Morphologically, necroptosis is characterized by morphology resembling unregulated necrosis: early loss of plasma membrane integrity, gain in cell volume and swelling organelles. In contrast, apoptosis is characterized by cell shrinkage, plasma membrane blebbing, and nuclear and organelle condensation and fragmentation. Biochemically, significant depletion of cellular ATP and leakage of intracellular contents are present during necroptosis, suggesting an energyconsuming process. Molecularly, necroptosis has no involvement of caspase and its signals are dependent on RIP1, receptor-interacting protein 3 (RIP3) and mixed lineage kinase domain-like protein (MLKL), while caspase activation is indispensible for the apoptosis. During the necroptosis, the permeabilization of plasma membrane, a major characteristic of necroptosis, may cause the release of damage associated molecular patterns (DAMPs) including mitochondrial DNA and high-mobility group box 1 protein, which may induce immune response and inflammation [33].
Although there are distinctive features between necroptosis and other cell death modes, there is still complicated interrelationship between necroptosis and other cell death pathways, and necroptosis has been viewed as a back-up process when apoptosis is abrogated [34] (Fig. 1).
The most extensively studied pathway leading to necroptosis is triggered by TNF-α/TNFR-induced pathway [43]. After the binding of TNF-α to TNFR1, the activated receptor may interact with RIP1 via the death domains (DD), which Signaling by the Bcl-2 family proteins; Regulation by death receptors; Caspase activation; DNA digestion by endonucleases; Cytosolic release of mitochondrial cytochrome c, SMAC and AIF may recruit cellular inhibitor of apoptosis proteins (cIAPs) (such as cIAP1 and cIAP2) and subsequently form the plasma membrane associated complex, leading to the activation of nuclear factor κB and mitogen-activated protein kinases (MAPKs). During this process, RIP1 can be polyubiquitinated by cIAPs and other E3 ubiquitin ligases [44]. The auto-ubiquitination and subsequent degradation of cIAPs induce the RIP1 deubiquitination, which is able to dissociate RIP1 from the cell membrane, causing its conversion from a pro-survival protein into a pro-death protein [45]. The binding of cytoplasmic RIP1 to FAS-associated death domain (FADD) may recruit procaspase-8, leading to the caspase-8 activation. The activated caspase-8 is able to inhibit the necroptosis through cleaving the key regulators of necroptosis (such as RIP1 and RIP3). When the caspase-8 activity is inhibited, cell apoptosis is inhibited and the necroptosis is induced [46]. The binding between RIP1 and RIP3 via their homotypic interaction motif (RHIM) domains may form a functional amyloid signaling complex, also known as "necrosome", which then induces the auto-phosphorylation of RIP3 and the subsequent execution of necrosis [47]. In addition to RIP1, it has been reported that there are other two RHIM-containing proteins, Toll/IL-1 receptor domaincontaining adaptor inducing IFN-β (TRIF) and DNAdependent activator of interferon regulatory factors (DAI), can activate RIP3 within the necrosome [47]. For example, TLR3 as a biological stimulus exert necroptotic effects via the adapter protein TRIF [38]. Certain pathogens can also trigger necroptosis depending on the DNA-dependent activator of DAI [42,48]. Furthermore, IFNs trigger necroptosis independently of RIP1 via the IFN-induced protein kinase PKR [39]. Previous studies have shown that ROS may also regulate Smac mimetic/TNF-α-induced necroptotic signaling [36]. Recently, Han et al. found that RIP1 autophosphorylation on serine residue 161 (S161) is promoted by mitochondrial ROS, which is indispensable for the recruitment of RIP3 into the necrosome. This suggests that the ROS can act as a new biological trigger, exerting necroptotic effects [37].

Execution of Necroptosis
Pseudokinase MLKL is a substrate of RIP3 and may serve as a major executioner of necroptosis [49,50]. There is evidence showing that RHIM dependent oligomerization and intramolecular autophosphorylation of RIP3 may recruit and phosphorylate MLKL, which induces a conformational change in the pseudokinase domain and the subsequent exposure of the 4-helical bundle domain [51,52]. At present, two available non-exclusive models are proposed for the executioner mechanism of MLKL: (1) the plasma membrane serves as a platform for the recruitment of Ca 2+ or Na + ion channel [53]; (2) the binding of MLKL to negatively charged phosphatidylinositol phosphates forms a direct pore-forming complex [54].
However, RIP3 also activates, independent of MLKL, ROS-driven necroptosis and FADD-mediated apoptosis [55,Fig. (1). Three modes of programmed cell death. Apoptosis, a programmed form of nonlytic cell death, can induce the activation of effector caspases via a variety of upstream signals. In the so-called "intrinsic cell death pathway," the P53 expression mediated by DNA damage triggers the production of Bax, a kind of pro-apoptotic protein, which can act to permeabilize the outer membranes of the mitochondria, releasing cytochrome c, which results in the oligomerization of an adapter protein, APAF1. APAF1 then binds to and activates the initiator caspase, caspase-9, and apoptosome forms. Bcl-2 protein family has anti-apoptotic activity. In the extrinsic cell death pathway, normally, caspase 8 can trigger apoptosis by classical caspase cascade. In the presence of caspase 8 inhibition, necroptosis is activated by the phosphorylation of RIP1 and RIP3, and then necrosome forms, which may activate the executioner of necroptosis, MLKL, leading to necroptosis. Autophagy can be induced in response to environmental signals including amino acids, hormones, and energy consumption. The bestcharacterized regulatory protein, mTOR, can act to inhibit autophagy. The Class III PI3K complex contributes to membrane expansion, resulting in the formation of a closed double-membrane structure, the autophagosome, which fuses with a lysosome to form an autolysosome. 56]. RIP3 binds to Ca 2+ -calmodulin-dependent protein kinase (CaMKII) and, via phosphorylation, oxidation or both, activates CaMKII, which accounts for the opening of mitochondrial permeability transition pore and leads to myocyte necroptosis [57]. Thus, mitochondrial ROS not only trigger necroptosis but also act as an executive of necrosis, which means a positive feedback loop. It is worth mentioning that necroptosis may cause rapid plasma membrane permeabilization and the subsequent release of cell contents and exposure of DAMPs [58]; the pattern recognition receptor may recognize these DAMPs to activate immune response and induce the subsequent clearance of necrotic cells [59]. It has been found that defective clearance of necrotic cells may cause the persistence of inflammation and excessive tissue injury, which serves as a mechanism underlying the pathogenesis of some diseases [60]. In conclusion, necroptosis fuels the vicious circle via amplifying inflammatory response or mitochondrial ROS.

Disposal of Necrotic Cells
The macrophages may internalize necrotic cells by forming spacious macropinosomes [61], which is accompanied by macrophage ruffling and has involvement in the sorting of fluid-phase macromolecules [62]. Apoptotic cells may emit a series of well-defined 'find-me' [63,64] and 'eat-me' signals. As in apoptosis, under some conditions, cells experiencing necrosis may also externalize phosphatidylserine before the plasma membrane permeabilization [65], which facilitates their recognition and internalization by phagocytes [66,67].

Inhibitors of Necropotisis
The majority of the knowledge of necroptosis is gained using necroptosis inhibitors or genetic modification technique. To date, some inhibitors of necroptosis have been developed for the study of necroptosis.

RIP1 Inhibitors
The first inhibitor of necroptosis (Nec-1) was identified in 2005 in a phenotypic screen for small molecules that are able to inhibit the TNF induced necrosis in human monocytic U937 cells [24]. Nec-1 was subsequently shown to inhibit the kinase activity of RIP1 [EC 50 = 494 nM)] [27]. However, Nec-1 is identical to the indoleamine-2,3- Fig. (2). Necroptosis-related signalling pathways and it's therapeutic targets. Necroptosis can be induced by multiple triggers,such as tumor necrosis factor (TNF), interferons (IFNs), viral nucleic acid, reactive oxygen species (ROS). Caspase-8 and cellular inhibitor of apoptosis proteins (cIAPs) prevent either RIP1 or RIP3 activation. When the caspase-8 activity is inhibited, necroptosis is induced. The binding between RIP1 and RIP3 via their homotypic interaction motif (RHIM) domains may form "necrosome", which then induces the autophosphorylation of RIP3 and subsequent execution of necrosis. There are other two RHIM-containing proteins, Toll/IL-1 receptor domaincontaining adaptor inducing IFN-β (TRIF) and DNA-dependent activator of interferon regulatory factors (DAI), can activate RIP3 independently of RIP1 within the necrosome. Furthermore, IFNs also trigger necroptosis independently of RIP1 via the interferon-induced protein kinase PKR. ROS can activates RIP1 autophosphorylation on serine residue 161 (S161), and subsequently enables RIP1 to recruit RIP3. Pseudokinase MLKL is a substrate of RIP3 and may serve as a major executioner of necroptosis. At present, two available non-exclusive models are proposed for the executioner mechanism of MLKL. However, RIP3 also activates, independently of MLKL, ROS-driven necroptosis. Necroptosis may cause rapid plasma membrane permeabilization and the subsequent release of cell contents and exposure of damage associated molecular patterns (DAMPs), which in turn trigger necroptosis. In conclusion, necroptosis fuels the vicious circle via amplifying inflammatory response or mitochondrial ROS. Necrostatin-1 (Nec-1) was shown to inhibit the kinase activity of RIP1. Furthermore, there are three RIP3 kinase inhibitors, GSK'840, GSK'843 and GSK'872. Necrosulfonamide (NSA) inhibits MLKL and is able to protect human cells from necroptotic stimuli. These might be attractive therapeutic targets. dioxygenase (IDO) enzyme inhibitor methyl-thiohydantointryptophan. In late studies, investigators developed a new inhibitor of necroptosis, Nec-1s (also called 7-Cl-O-Nec-1; EC 50 =206 nM). Nec-1s lacks IDO inhibitory activity, but has increased plasma stability and increased specificity for RIP over a broad range of kinases as well as no toxic effect described for Nec-1 [68]. Several structurally distinct necrostatins (Nec-1, Nec-3, Nec-4, Nec-5 and Nec-7) are also developed, but Nec-7 (EC 50 = 10.6 µM) does not inhibit RIP1 kinase [69].

RIP3 Inhibitors
There are three RIP3 kinase inhibitors, GSKʹ 840, GSKʹ 843 and GSKʹ 872. Of them, GSKʹ 843 and GSKʹ 872 at higher concentrations may promote TNF-induced RIP1 dependent apoptosis and caspase 8 activation [70], while GSKʹ 840 exhibits the most specific profile. In addition, GSKʹ 840 is particularly interesting because it does not induce RIP1 kinase activity-dependent apoptosis at higher concentrations, which is in contrast to GSKʹ 843 and GSKʹ 872. Currently, however, GSKʹ 840 can not inhibit murine RIP3, which prevents its assessment in murine models.

MLKL Inhibitors
) was the first compound reported to inhibit MLKL [49]. In fact, NSA was used to identify MLKL as a downstream target of RIP3. Of note, NSA is able to protect human cells from necroptotic stimuli, which is not found in murine cells because NSA alkylates the Cys86 of human MLKL and Cys86 is absent in murine MLKL. Thus, it is not suitable in murine preclinical models. In recent years, a new class of MLKL inhibitors based on 'compound 1' (also known as GW806742X or SYN-1215) is described to target the pseudokinase domain of MLKL [51].

NECROPTOSIS ASSOCIATED PATHOLOGIES
Over the past two decades, a variety of studies have shown that necroptosis is closely related to many pathologies, including I/R injury, inflammatory diseases, infection, autoimmune diseases and other disorders [71].

I/R Injury
Ischemia is caused by the obstruction of blood flow to a tissue, resulting in the insufficient supply of oxygen and nutrients, and if severe, in cell death. Reperfusion following the restoration of blood flow may cause a burst of ROS, also leading to cell death. The renal I/R was to be attenuated in RIP3-deficient mice [72], and RIP3 deficiency also decreased cardiac hypertrophy and inflammation after myocardial infarction [73]. In addition, RIP1 inhibitor Nec-1 is also able to attenuate myocardial I/R [74], which is ascribed to the inhibition of oxidative stress, necrosis and inflammation.

Neurological Diseases
Recent studies have shown that necroptosis is involved in the pathogenesis of some neurological diseases including stroke, traumatic brain injury (TBI), spinal cord injury (SCI) and neurodegenerative diseases. You et al. found administra-tion of Nec-1 before or 15 min after controlled cortical impact decreased brain tissue damage (days 14 and 35) and improved motor performance and spatial memory [75]. An lncRNA microarray analysis showed the expression of some lncRNAs related to necroptosis changed in rat hippocampus after TBI [76]. In a mouse TBI model, Nec-1 pretreatment was able to attenuate brain injury via inhibiting apoptosis and autophagy simultaneously [77]. In a SCI rat model, Wang et al. showed Nec-1 pre-treatment protected the spinal cord against injury via inhibiting both necroptosis and apoptosis [78]. In R6/2transgenic mouse model of Huntington's disease, Nec-1 was found to delay the disease onset, and the increased expression of RIP1 was observed at the onset of disease symptoms [79]. In primary newborn mouse cortical cells treated with aluminum, Nec-1 significantly improved the cell viability in a concentration dependent manner; Nec-1 also improves the learning and memory retention of mice treated with aluminum [80].

Retinal Injury
In retinitis pigmentosa mice with interphotoreceptor retinoid-binding protein (IRBP) deficiency (Irbp -/-), the retina had increased expression of both RIP1 and RIP3, but inhibition of RIP1 kinase activity significantly prevented cone and rod photoreceptor degeneration [81]. In addition, RIP3deficiency resulted in the protection of cone cells against necroptosis in a mouse model of retinitis pigmentosa (rd10 mice) [82]. In an experimental model of retinal detachment, more than 10-fold increase in RIP3 expression was observed after retinal detachment, and Nec-1 or RIP3 deficiency significantly alleviated necrotic changes and inhibited oxidative stress and mitochondrial release of AIF [83]. Nec-1 or RIP3 silencing also inhibited the retinal pigment epithelial cell death secondary to oxidative stress [84] and attenuated retinal I/R [85].

Inflammation
In TNF-induced systemic inflammatory response syndrome (SIRS), Duprez et al. found deletion of RIP3 or pretreatment with Nec-1 completely protected against lethal SIRS and reduced the amounts of circulating DAMPs [86]. RIP3-deficient mice could also be markedly protected from TNF-α-mediated shock, while blocking necroptosis by Nec-1 did not protect from TNF-α/zVAD-mediated shock, and further accelerated time to death [87]. RIP3 expression was found to correlate with the sensitivity of pancreatic acinar cells to necroptosis, and in a cerulein-induced pancreatitis mouse model, RIP3 or MLKL deficiency protected mice from acute pancreatitis [31,88], which suggests the involvement of necroptosis in the pathogenesis of pancreatitis. In ConA-induced acute hepatitis, increased RIP3 expression was observed in the liver, hepatocyte-specific caspase-8 deletion increased necrotic damage and Nec-1 decreased hepatocyte necrotic cell death [89,90]. In acetaminophen-induced acute liver failure mice, hepatic RIP1 and RIP3 were activated, and either pretreatment or post-treatment with Nec-1 significantly attenuated APAP-induced acute liver failure and improved the survival by preventing APAP-induced necroptosis [91]. In mice after chronic ethanol feeding, increased RIP3 expression was detected in the liver [92], and a similar finding was also observed in patients with alcoholic liver disease [93], and RIP3-deficiency protected mice against both ethanol-induced liver injury and inflammation [93]. RIP3 expression also increased in the terminal ileum of patients with Crohn's disease, and the TNF induced cell death was associated with increased RIP3 expression, but prevented by Nec-1 in cultured intestinal organoids [94].

Infection
Vaccinia virus (VV) encodes the caspase inhibitor B13R/Spi2, which blocks apoptosis upon infection, but sensitizes cells to RIP1/RIP3-dependent necroptosis. VV infection induced the formation of pro-necrotic RIP1-RIP3 complex in the liver, accompanied by the induction of TNF expression and inflammation [95], but RIP3-deficient mice and RIP1 D138N/D138N mice showed attenuated necrotic tissue injury and inflammation [95,96]. Murine cytomegalovirus also encodes a necrotic inhibitor, vIRA, which can prevent premature host cell death upon infection. Studies have shown that vIRA may inhibit necrotic cell death after TNF treatment by disrupting the pronecrotic RIP1-RIP3 complex [97]. Necrotic cell death has also been observed after infection by other viruses, such as human immunodeficiency virus type-1, herpes simplex virus type-1 and T3D, and Nec-1 pre-treatment is able to attenuate the cell death induced by these viruses [98][99][100].

NECROPTOSIS AND STROKE
Some studies have revealed that necroptosis is closely related to the pathogenesis of stroke as Nec-1 (Nec-1s) or genetic modulation of necroptosis related proteins may improve brain injury and neurological function after stroke ( Table 2).
Liu et al. found necroptosis mediated TNF-induced toxicity of HT-22 hippocampal neuronal cells because these cells were sensitive to TNF-α only upon caspase blockage and subsequently underwent necrosis and the cell death was suppressed by knockdown of CYLD, RIP1, RIP3 or MLKL [101]. In cultured neurons exposed to hemoglobin or hemin (an in vitro model of intracerebral hemorrhage [ICH]), features of ferroptotic and necroptotic cell death, but not caspase-dependent apoptosis or autophagy, were observed, and chemical inhibitors of ferroptosis and necroptosis protected against hemoglobin-and hemin-induced toxicity (abrogating death by >80%) [102]. Qu et al. investigated the functions and mechanisms of MLKL in mediating neuronal damage in developing brain after hypoxia-ischemia (H/I). In neurons treated with oxygen-glucose deprivation (OGD) plus caspase inhibitor zVAD treatment (OGD/zVAD), RIP1 and RIP3 expressions were up-regulated and the interaction of RIP1-RIP3 with MLKL increased; siRNA mediated inhibition of MLKL attenuated neuronal death induced by OGD/zVAD [103]. In addition, induction of RIP1 protein instability was able to protect the primary neurons against OGD/zVAD induced necroptosis [104]. In an in vitro model of global ischemia (hippocampal neurons exposed to OGD), RIP1 and RIP3 expression increased and both formed necrosomes, but caspase-8 mRNA expression was transiently decreased following OGD; RIP3 knock-down (KD) abrogated OGDinduced necrotic neuronal death while RIP3 over-expression exacerbated neuronal death following OGD [105]. Necropto-sis was shown to be an early mechanism of cell death occurring within the first 24 h post-ischemic injury [106] and inversely linked to the caspase-dependent cell death [107].
Degterev et al. for the first time found that necroptosis contributes to mouse middle cerebral artery occlusion (MCAO) brain injury in vivo and the mechanism was distinct from that of apoptosis [24]. Moreover, they identified a specific and potent small-molecule inhibitor of necroptosis, Nec-1, which could block a critical step in the necroptosis (but not apoptosis) and exerted neurological protection in this animal model. Furthermore, the protective effect of 7-Cl-Nec-1 was also detectable even when it was administered 6 h after the onset of injury, and they speculated the extended time window of neuroprotection by 7-Cl-Nec-1 in vivo might reflect a delayed induction of necroptosis during stroke. Xu et al. investigated the neuroprotection of Nec-1 in vivo and in vitro [108]. They found Nec-1 could protect the mouse brain against I/R injury, and combined use of an apoptosis inhibitor Gly(14)-humanin and Nec-1 conferred synergistic neuroprotection in vivo and in vitro. In a global cerebral I/R injury model, neuronal death and rat mortality were greatly inhibited by Nec-1 and 3-MA (an autophagy inhibitor) pre-treatment, but not by Ac-DMQD-CHO (caspase-3 inhibitor) [109] and there was accompanied nuclear translocation and co-localization of RIP3 and AIF as well as their interaction after I/R injury. Moreover, they also evaluated the cell specificity of necroptosis. Results showed caspase-8 was expressed richly in glial fibrillary acidic protein (GFAP) -positive astrocytes and Iba-1-positive microglia, but not in NeuN-positive neurons. Thus, they concluded that caspase-3 and caspase-8 are not involved in regulating hippocampal CA1 neuronal death induced by I/R injury. In a global cerebral ischemia induced by the four-vessel occlusion method, Nec-1 induced inhibition of RIP3 up-regulation and nuclear translocation was also found to be related to its neuroprotection [110].
In a collagenase-induced ICH mouse model, Zhu et al. found the ultrastructural features of necrosis at 24 h after ICH by electron microscopy, and less necrotic cells were also found in RIP3-deficient mice after ICH [111]. In the same model, King et al. found Nec-1 was able to significantly reduce hematoma volume, limit cell death, reduce blood-brain barrier opening, attenuate edema development to sham level, and improve neurobehavioral outcome at 72 h after ICH [112]. Similar findings were found in the study of Su et al. [113]. In another in vivo ICH model established by the injection of autologous blood, inhibition of RIP1 by a specific chemical inhibitor or genetic knockdown attenuated brain edema and improved neurological score, which was also confirmed in an in vitro model using OxyHb-treated neurons [114]. Chang et al. also found the specific inhibitor Nec-1 also suppressed apoptosis and autophagy to exert neuroprotective effects after ICH [115]. In an endovascular perforation induced subarachnoid hemorrhage (SAH) rat model, SAH was found to up-regulate RIP1, RIP3, phosphorylated DRP1 and NLRP3 inflammasome, and Nec-1 treatment reduced RIP1, RIP3, phosphorylated DRP1 and NLRP3 inflammasome, subsequently alleviating brain injury secondary to SAH [116].
In neonatal brain H/I model, blockade of RIP-1 kinase after H/I afforded neuroprotection [106] and this protection  was possibly by interrupting RIP1-RIP3-driven oxidative injury and inflammation [117]. In the study of Qu et al., results showed siRNA mediated inhibition of MLKL was also able to reduce infarct area and improve neurological score in an experimental neonatal rat H/I model [103]. In a mouse model of brain H/I injury, the increased infarct size in mice with hyperglycemia was prevented by treatment with Nec-1s, suggesting that increased necroptosis accounts for the exacerbation of brain injury in conditions of hyperglycemia [118], and prevention of necroptosis was also found to attenuate H/I-induced injury of type 2 diabetic mouse brain [119]. In the moderately ischemic brain, necroptosis was predominantly found in the core of the lesion, the peri-infarct area of the severely ischemic brain had significantly more necroptosis than the peri-infarct area of the moderately injured brain, and necroptotic cell death was as active in the peri-infarct area as it was in the core of the lesion [120].
In oligodendrocytes treated by OGD/zVAD in vitro, the expression of RIP3 was up-regulated, and the interaction of RIP3 with RIP1, MLKL, and CaMKIIδ increased; inhibition of the RIP3-MLKL or RIP3-CaMKIIδ interaction attenuated OGD/zVAD induced oligodendrocyte death, which was not found after inhibition of RIP3-RIP1 interaction. These protective mechanisms have involvement of the translocation of MLKL to the oligodendrocyte membrane and the phosphorylation of CaMKIIδ. In neonatal H/I rats, inhibition of RIP3-MLKL or RIP3-CaMKIIδ interaction attenuated the disrupted development of myelin [121]. Acidotoxicity is common among neurological disorders, such as ischemic stroke. Wang et al. investigated the relationship between acidosis and neuronal necroptosis [122]. They found acid stimulation could recruit RIP1 to the acid-sensing ion channel 1a (ASIC1a) Cterminus, causing RIP1 phosphorylation and subsequent neuronal death; in a mouse MCAO model, ASIC1a-RIP1 interaction and RIP1 phosphorylation were found in affected brain areas, and the Asic1a deficiency significantly prevented RIP1 phosphorylation and brain damage, which indicates ASIC1a-mediated RIP1 activation is involved in ischemic neuronal injury. Overload of iron, which is released from hemoglobin, also contributes to brain injury after ICH. Dai et al. found iron overload promoted the necroptosis of primary cortical neurons but curcumin was able to attenuate ferrous chloride induced necroptosis [123]. Excitotoxicity is a key mechanism of tissue destruction in cerebral ischemia (stroke). Investigators found PARP activation was involved in the glutamate-induced necroptosis of HT-22 cells [124], and necroptosis was related to the N-Methyl-D-aspartateinduced excitotoxicity in rat cortical neurons [125].

CONCLUSION AND PROSPECTIVE
6.1. Although some studies have confirmed that necroptosis is involved in the pathogensis of stroke, and treatment targeting necroptosis related key proteins have been confirmed to be neuroprotective in different animal models and different cell types, few studies are undertaken to investigate the clinical use of necroptosis inhibitors or gene modification of necroptosis. As shown above, RIP1, RIP3 and MLKL are the three proteins specific to necroptosis. Thus, clinical strategies may target RIP1, RIP3, MLKL, or RIP1/RIP3 interaction, but inhibition of TNFR seems to be infeasible because TNFR related signaling pathways are related to some cell processes and not unique to necroptosis. Of note, NSA is able to inhibit MLKL in human cells, but may not affect the necroptosis in animal models. As shown in a lot of drugs, favorable results are usually obtained from pre-clinical studies, but less effectiveness is observed in clinical practice. Thus, more studies are needed to investigate the role of necroptosis in stroke in clinical settings.
6.2. As mentioned above, Nec-1 is identical to the IDO enzyme inhibitor methyl-thiohydantoin-tryptophan, and thus it might also inhibit the IDO enzyme, but Nec-1 has been used in a majority of studies, and more specific inhibitor 7-Cl-Nec-1 is seldom used. Whether concomitant inhibition of IDO may bias the results is still unclear. Other inhibitors of necroptosis have never been used in stroke models. 6.3. Although a variety of studies investigated the therapeutic effects of necroptosis inhibitors in different disease models, few studies explored the potential toxicity of these inhibitors. Degterev et al. investigated the effects of Nec-1 on cell adhesion, actin and microtubule cytoskeletons, the morphology of various cellular compartments and others, and no detrimental effects were observed [24]. What are the half maximal inhibitory concentration and median lethal dose is needed to confirm in animal and cell models, and more studies are warranted to investigate the toxicity of these inhibitors of necroptosis, which is crucial for its clinical application. Moreover, the pharmacokinetics of these inhibitors (bioavailability, site where they are metabolized and the enzymes responsible for their metabolism, way in which they are excreted, their permeability to blood brain barrier and so on) are also needed to elucidate in animal models before its clinical use.
6.4. The dose-response curve is needed to be delineated to confirm the optimal dose of inhibitors. The optimal routes by which these drugs are used are also needed to investigate. Necroptosis inhibitors are usually administered intraperitoneally or intracerebroventricularly, and whether intravenous administration achieves similar effectiveness and which is the better route for administration are also needed to confirm. 6.5. As studied by Degterev et al., the new inhibitor 7-Cl-Nec-1 was able to exert neuroprotective effects even administered at 6 h after stroke. Thus, what is the optimal time point at which these inhibitors still achieve neuroprotection is warranted to determine. This may be based on the dynamic change in cell necroptosis of the injured brain. 6.6. It has been confirmed that there is cross talk among different death modes. In the available studies, several also revealed that combined inhibition of necroptosis and apoptosis exerted synergistic neuroprotection in animal models. Whether this is clinically applicable is also required to confirm in more animal models. 6.7. As mentioned above, the role of necroptosis in the stroke has been investigated in oligodendrocytes, neurons and HT-22 cells. However, not only oligodendrocytes and neurons are involved in the pathogenesis of stroke, but also astrocytes, neural progenitor cell, vascular epithelial cells and other cell types in the brain are also closely related to the occurrence of stroke. Thus, what is the role of necroptosis of other cell types and whether there is an interaction between the necroptosis of two or more cell types are still needed to be studied in detail.

CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or otherwise.


Apolipoprotein A5 3'-UTR variants and cardiometabolic traits in Koreans: results from the Korean genome and epidemiology study and the Korea National Health and Nutrition Examination Survey

BACKGROUND/OBJECTIVES This study aimed to test the association between APOA5 3'-UTR variants (rs662799) and cardiometabolic traits in Koreans. SUBJECTS/METHODS For this study, epidemiological data, Apolipoprotein A5 (APOA5) genotype information, and lymphoblastoid cell line (LCL) biospecimens from a subset of the Ansung-Ansan cohort within the Korean Genome and Epidemiology study (KoGES-ASAS; n = 7,704) as well as epidemiological data along with genomic DNA biospecimens of participants from a subset of the Korea National Health and Nutrition Examination Survey (KNHANES 2011-12; n = 2,235) were obtained. APOA5 mRNA expression was also measured. RESULTS APOA5 rs662799 genotype distributions in both the KoGES-ASAS and KNHANES groups were 50.6% for TT, 41.3% for TC, and 8.1% for CC, which are similar to those in previous reports. In both groups, minor C allele carriers, particularly subjects with CC homozygosity, had lower high-density lipoprotein (HDL) cholesterol and higher triglyceride levels than TT homozygotes. Linear regression analysis showed that the minor C allele significantly contributed to reduction of circulating HDL cholesterol levels [β = −2.048, P < 0.001; β = −2.199, P < 0.001] as well as elevation of circulating triglyceride levels [β = 0.053, P < 0.001; β = 0.066, P < 0.001] in both the KoGES-ASAS and KNHANES groups. In addition, higher expression levels of APOA5 in LCLs of 64 healthy individuals were negatively associated with body mass index (r = −0.277, P = 0.027) and circulating triglyceride level (r = −0.340, P = 0.006) but not significantly correlated with circulating HDL cholesterol level. On the other hand, we observed no significant difference in the mRNA level of APOA5 according to APOA5 rs662799 polymorphisms. CONCLUSIONS The C allele of APOA5 rs662799 was found to be significantly associated with cardiometabolic traits in a large Korean population from the KoGES-ASAS and KNHANES. The effect of this genotype may be associated with post-transcriptional regulation, which deserves further experimental confirmation.

Conflicting findings on the above-described associations have been reported [14,15], and most studies in Korea have been conducted using small samples, of which the results are not generalizable to the Korean population. Therefore, this study aimed to test the association between APOA5 3'-UTR variants of rs662799 as functional-tag SNPs at APOA5 and cardiometabolic traits using data from subpopulations of the Korean Genome and Epidemiology Study: Ansung-Ansan cohort study (KoGES-ASAS) and the Korea National Health and Nutrition Examination Survey (KNHANES, 2011(KNHANES, -2012.

Study participants
This study was conducted using two separate datasets composed of the KoGES-ASAS and KNHANES (2011-2012). The procedure and design of the KoGES-ASAS are described elsewhere [16,17]. With regard to the KoGES-ASAS, 10,030 individuals aged 40-69 years living in the Ansan (urban) and Ansung (rural) districts were recruited for baseline in 2001-2002. The aim was to construct a genomic and epidemiologic database to examine the effects of genetics and the environment on disease prevalence in Koreans. Questionnaire-based interviews were conducted with participants in a community clinic, where they were questioned regarding their socio-demographic status, lifestyle, health, and medical history. They also underwent anthropometric measurements, clinical examinations, as well as biannual follow-up examinations. The current study was based on data collected from a total of 8,841 participants, for whom DNA samples for genotyping were available. Among them, participants with preexisting cancer (n = 97), diabetes (n = 783), or CVD (n = 202) at the time of enrollment in the study were excluded. Participants with TG levels > 600 mg/dL (n = 55) were also excluded, leaving 7,704 participants for analysis. This study protocol was approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention (KCDC; KBP-2016-062) and the Institutional Review Board at Korea University (KU-IRB-16-EX-272-A-1). The KNHANES is a nationwide crosssectional survey conducted by the KCDC. This survey is composed of three different sections: health examination, health interview, and nutrition survey. Detailed information on the KNHANES is available elsewhere [17]. Among the 16,576 participants (8,518 in 2011 and 8,058 in 2012), those aged < 20 years were excluded. We further excluded those who had been diagnosed with cancer as well as women who were pregnant or breastfeeding. After considering all exclusion criteria, 2,600 participants (492 in 2011 and 2,108 in 2012) whose DNA samples were available for genotyping were included from the study. After further excluding participants with diabetes (n = 278), CVD (n = 74), or TG levels > 600 mg/dL (n = 13), data on a total of 2,235 participants were finally used for the analysis. The KNHANES was approved by the Institutional Review Board of KCDC (2011-02CON-06-C, 2012-01EXP-01-2C). This study protocol was approved by the Institutional Review Board of KCDC (KBP-2016-062) and the Institutional Review Board at Korea University (KU-IRB-16-EX-137-A-1).

General information and anthropometric and biochemical measurements
Demographic and behavioral data on participants in the KoGES-ASAS and KNHANES (i.e., age, sex, education level, physical activity, cigarette smoking, and alcohol consumption) were obtained from survey questionnaires administered by trained interviewers. Education level was divided into four groups: elementary school, middle school, high school, and university. Current smokers and current drinkers were defined as those who smoked cigarettes or drank alcoholic beverages regularly at the time of the survey. The level of total metabolic equivalents (METs), as a representative of physical activity, was calculated by summing METs during each activity type (2.4 for light, 5.0 for moderate, and 7.5 for intense activities) [18]. For anthropometric and biochemical measurements, procedures and assay methods for the KoGES-ASAS and KNHANES are described elsewhere in detail [17,19]. Height and body weight were measured, from which the body mass index (BMI; kg/m 2 ) was calculated as the weight divided by the height squared. Blood pressure (BP) was repeatedly measured by a trained technician using a mercury sphygmomanometer. Two readings were taken on the left and right arms of each subject in the supine position with a 5-min rest between readings. Measurements were recorded to the nearest 2 mmHg, and averages were calculated for systolic and diastolic BPs (SBP and DBP). Hypertension was defined as an SBP of ≥ 140 mmHg or DBP of ≥ 90 mmHg, previous diagnosis of hypertension as self-reported by the participants, or taking anti-hypertensive medication. In the KoGES-ASAS, blood samples were collected for biochemical analysis after at least 8 h of fasting. Fasting levels of glucose (mg/dL), TG (mg/dL), total cholesterol (mg/dL), and high-density lipoprotein cholesterol (HDL cholesterol, mg/dL) were measured using an automatic analyzer (ADVIA 1650 and 1680; Siemens, Tarrytown, NY, USA). In the KNHANES, blood samples were collected after overnight fasting for the analysis of biochemical markers. Fasting levels of glucose (mg/dL), TG (mg/dL), total cholesterol (mg/dL), and HDL cholesterol (mg/dL) were measured using an automatic analyzer (Hitachi 7600; Hitachi, Tokyo, Japan). In both studies, LDL cholesterol was calculated using the Friedewald equation [LDL cholesterol (mg/dL) = total cholesterol (mg/dL) -HDL cholesterol (mg/dL) -(TG (mg/dL))/5] in subjects with a TG level of < 400 mg/dL [20].

Genotyping information
Detailed information on DNA preparation and genotyping of the KoGES-ASAS is provided elsewhere [19]. Briefly, DNA samples were isolated from the peripheral blood of participants and genotyped using the Affymetrix Genome-Wide Human SNP Array 5.0 (Affymetrix, Inc., Santa Clara, CA, USA). The accuracy of genotyping was calculated with Bayesian robust linear modeling using the Mahalanobis distance genotyping algorithm. A total of 352,228 SNPs in 7,704 participants became available after preimputation quality control, namely, 1) exclusion of SNPs with high missing genotype call rates > 5% with a minor allele frequency < 0.01 and not in Hardy-Weinberg equilibrium (HWE, P < 1 × 10 -6 ) and 2) removal of samples with sex mismatch. Genetic principal components were computed in a subset of 304,225 SNPs after excluding additional 48,003 SNPs (not in HWE under a more conservative criterion, P < 1 × 10 -5 ) through the EIGENSTRAT software package. In the present study, a gene variant at APOA5 (rs662799) was included in the analyses to test its association with cardiometabolic traits. For DNA samples from the KNHANES (Institutional Review Board no. KBP-2016-062, KU-IRB-16-EX-137-A-1), APOA5 (rs662799) genotyping was screened using the TaqMan fluorogenic 5' nuclease assay (ABI, Foster City, CA, USA). The final volume for the polymerase chain reaction (PCR) was 5 μL comprising 10 ng of genomic DNA and 2.5 μL of TaqMan Universal PCR Master Mix with 0.13 μL of 20 × assay mix. Thermal cycling conditions were as follows: 50°C for 2 min to activate uracil N-glycosylase and prevent carry-over contamination, 95°C for 10 min to activate DNA polymerase, and 45 cycles of 95°C for 15 s and 60°C for 1 min. PCR was always performed using 384-well plates with a Dual 384-Well GeneAmp PCR System 9700 (ABI), and the endpoint fluorescent readings were obtained on an ABI PRISM 7900 HT Sequence Detection System (ABI). Duplicate samples and negative controls were included to ensure the accuracy of genotyping.

RNA extraction and semi-quantitative reverse transcription-PCR
To compare mRNA expression levels according to APOA5 genetic variant (rs662799), lymphoblastoid cell lines (LCLs) of 64 healthy individuals (males, n = 27; females, n = 37) were obtained from the subgroup of the KoGES-ASAS (Institutional Review Board no. KBP-2016-062, KU-IRB-16-EX-272-A-1). Total RNA was extracted from LCLs using the RibospinTM Kit (GeneAll, Korea), in accordance with the manufacturer's protocol. cDNA was synthesized from 1 μg of RNA using oligo-dT and Superscript TM II reverse transcriptase (Invitrogen, USA). One microgram of cDNA was amplified with quantitative real-time PCR using the SYBR Green PCR Kit (Qiagen, USA). PCR was conducted using the QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystems, Foster City, CA), and the conditions were as follows: 15 min at 95°C, followed by 40 thermal cycles of 94°C for 30 s, 60°C for 20 s, and 72°C for 30 s. Target-specific primers for real-time PCR were designed using Primer Express® software. Sequences of the designed primers were as follows: APOA5 (sense, 5'-ACG CAC GCA TCC AGC AGA AC-3'; antisense, 5'-TCG GAG AGC ATC TGG GGG TC-3') and glyceraldehyde 3-phosphate dehydrogenase (GAPDH; sense, 5'-TCC ACC ACC CTG TTG CTG TA-3'; antisense, 5'-ACC ACA GTC CAT GCC ATC AC-3'). Obtained data were analyzed using the comparative cycle threshold (Ct) method and were normalized by the GAPDH expression value. Melting curves were generated for each PCR reaction to ensure purity of the amplification product. The delta-delta-cycle threshold  ) method was used to calculate changes in gene expression as a relative fold difference between experimental and endogenous control samples. Values are expressed as fold changes relative to the control and as mean ± standard deviation (SD).

Statistical analysis
Statistical analyses were performed using Stata SE 12.0 (Stata Corp., Carolina, USA). First, the distribution of variable values was investigated. Fasting blood glucose and TG levels were log-transformed to mimic a Gaussian distribution. Descriptive statistics of all variable values are presented as mean ± standard error (SE) for continuous variables and as a number and percentage for categorical variables. Effect allele frequency was calculated using the following formula: (counts of heterozygotes + 2 × count of homozygotes of effect allele)/(2 × total count). HWE equilibrium was tested using Stata. Mean differences in variables among the three different rs662799 genotype groups were compared using one-way analysis of variance after considering potential covariates for continuous variables and using chi-squared test for categorical variables. Pearson's correlation analysis was conducted to evaluate relationships between the mRNA abundance of APOA5 in LCLs of participants and selected variables. To estimate the effect of genotype on cardiometabolic risk factors, a linear regression model was used in an additive scale model, with adjustments for potential covariates. Results are presented as estimated regression coefficients β with 95% confidence interval (CI) for continuous variables. Corresponding P-values are also provided. Table 1 presents the general information and cardiovascular risk parameters of the two study populations. Mean values of age as well as proportions of sex, smoking status, and hypertension (the KoGES-ASAS: 31.3% vs. the KNHNAES: 29.2%) were similar between the two study population groups. Genotype distribution of the APOA5-1131T > C (rs662799) in the population was in Hardy-Weinberg equilibrium. The minor C allele frequency was 0.288 in each population group as well as in total population, which is consistent with previous observations in the Korean population [10][11][12]. Genotype distributions of APOA5 rs662799 in the total population were 50.6% for TT, 41.3% for TC, and 8.1% for CC (50.7% for TT, 41.4% for TC, and 7.9% for CC in the KoGES-ASAS and 50.4% for TT, 40.9% for TC, and 8.7% for CC in the KNHNAES, respectively). Table 2 presents the lifestyle and socio-economic information of the study participants according to the APOA5 rs662799 polymorphism. There were no significant differences in age, proportions of sex, education level, MET, smoking status and drinking status in either the KoGES-ASAS or KNHANES group.      The values were described as mean ± SE. 1) Statistical differences were determined using one-way analysis of variance (ANOVA) for continuous variables with Bonferroni's multiple correction (P < 0.05). 2) Tested after log-transformation. KoGES-ASAS, Korean Ansung-Ansan cohort within the Korean Genome and Epidemiology study; KNHANES, Korea National Health and Nutrition Examination Survey; BP, blood pressure; HDL, high-density lipoprotein. Tested by a linear regression model for cardiovascular risk factors 1) Sex, age, area, education level, metabolic equivalent, smoking status, and drinking status were adjusted for analysis using Ansan-Ansung data 2) Sex, age, year, education level, metabolic equivalent, smoking status, and drinking status were adjusted for analysis using KNHANES data 3) Tested after log-transformation Table 4. Contribution of minor C allele in the APOA5 rs662799 polymorphism to cardiovascular risk (circulating HDL-cholesterol and triglyceride) Fig. 1. Relationships between APOA5 mRNA expression and body mass index, circulating triglyceride, and high-density lipoprotein cholesterol levels and/or mRNA abundance of APOA5 according to the APOA5 rs662799 genotype. Results are expressed as r (correlation coefficient) or mean ± SE tested by Pearson's correlation analysis or independent t-test (non-parametric test); n.s. indicates no statistically significant differences in the values among the groups.

Associations between the APOA5 rs662799 polymorphism and cardiovascular risk factors
hand, there were no significant differences in BP, BMI, fasting glucose, and total and LDL cholesterol according to the APOA5 rs662799 polymorphism. To estimate the effect of genotype on circulating levels of TG and HDL cholesterol, a linear regression model was constructed with adjustments for potential covariates.  (Table 4).
Relationship between APOA5 mRNA expression and BMI, circulating TG, or the APOA5 rs662799 genotype To investigate the relationship between APOA5 gene expression level and blood parameters, we measured the mRNA abundance of APOA5 in LCLs of participants (n = 64, 27 males and 37 females; 54.1 ± 1.1 years) using quantitative real-time reverse transcription polymerase chain reaction (RT-PCR). As shown in Fig. 1, a higher APOA5 expression level (-∆Ct) in LCLs was negatively associated with BMI (r = -0.277, P = 0.027) and circulating TG level (r = -0.340, P = 0.006). However, APOA5 mRNA expression level (-∆Ct) was not significantly correlated with circulating HDL cholesterol level (r = 0.179, P = 0.156). On the other hand, no significant difference was observed in ApoA5 mRNA level (-∆Ct) between APOA5 rs662799 TT homozygotes (n = 32) and C allele carriers (APOA5 rs662799 TC + CC, n = 32).

DISCUSSION
While the association between the APOA5 rs662799 (T/C) polymorphism and blood lipid profile remains controversial, we confirmed previous studies on Koreans [9,10] that reported that the minor C allele in APOA5 rs662799 is significantly associated with elevation of TG and reduction of HDL cholesterol levels in a large Korean population, from subsets of the KoGES-ASAS and KNHANES. Our results are in line with a recent meta-analysis that showed that the APOA5 rs662799 C allele is associated with elevated circulating TG levels, regardless of ethnicity [21]. Other recent meta-analyses reported that the APOA5 rs662799 C allele and CC genotype confer increased risks for the development of coronary artery disease (CAD) [22] and ischemic stroke [23], indicating a possible mediating role for circulating TG in the association between the risk variant at APOA5 and the atherosclerotic process.
ApoA5 consists of four exons and encodes a 366-amino-acid protein, and APOA5 rs662799 is located in the promoter region of APOA5 proximal to the APOA1/C3/A4 gene cluster on chromosome 11 [5,6,12,13]. Regarding the function of ApoA5 in TG metabolism, it was previously demonstrated that APOA5 overexpression in mice resulted in elevation of plasma ApoA5 levels and marked reduction of circulating TG levels [6]. There is also evidence that serum TG levels were remarkably increased 4-fold in mice with APOA5 knockout [5]. In addition, several plausible proposals have been made regarding the biological mechanisms by which genetic variants at APOA5 affect TG metabolism in humans. For example, the minor C allele is thought to impair ribosomal translation efficiency, thereby reducing the level of ApoA5 translated from this mRNA [24,25]. In line with this, previous studies have reported that the APOA5 rs662799 C allele is associated with decreased circulating ApoA5 levels or activity, which may result in an impaired interaction with LPL activity and increased circulating TG levels in certain metabolic contexts [26,27]. Alternatively, a peroxisome proliferator response element in the promoter region of APOA5 was identified as a target gene for peroxisome proliferator-activated receptor-α (PPARα) [28]. This indicates a possible role for the PPARα pathway in the association between ApoA5 and circulating TG. In addition, the association of the APOA5 rs662799 polymorphism with CAD was suggested to be attributable to linkage disequilibrium with APOC3 variants or to other closely linked genetic variations [29].
In this study, we observed that the minor C allele significantly contributed to reduction of circulating HDL cholesterol as well as elevation of circulating TG levels in a large Korean population. While mRNA levels of APOA5 expressed from LCLs of healthy individuals were negatively associated with BMI as well as circulating TG, mRNA abundance was not significantly different according to the APOA5 rs662799 polymorphism. This result suggests that the genotype effect was more or less weak and this SNP might modulate APOA5 expression at the posttranscriptional level. Indeed, it was very recently proposed that the miR-binding site created by the rare c. * 158C allele in APOA5 3' UTR causes liver post-transcriptional down-regulation of APOA5 by miR-485-5p, at least partially accounting for the subsequent elevation of plasma TG levels in humans [30], which warrants further experimental confirmation. While evidence of the role of ApoA5 in LPL-mediated TG metabolism has been generally accepted, it has been additionally speculated that the composition of gut microbiota is related to circulating ApoA5 levels or APOA5 polymorphisms as well as related metabolic properties in Koreans [31,32]. Lim et al. suggested that APOA5 rs651821 polymorphisms may contribute to compositional changes in MetS-related gut microbiota [31]. It was observed that the minor C allele in APOA5 rs651821, which increases circulating TG levels and MetS incidence, was significantly associated with reduced abundance of Bifidobacterium and its parent taxon Actinobacteria, independent of the individual's MetS status [31]. ApoA5 is known to be produced mainly in the liver but is also expressed at a low level in the intestine, and its function is to modulate chylomicron production [33,34]. Therefore, carriers of the minor C allele of APOA5 rs651821 have different lipid properties in the gut compared with non-carriers, which may lead to alteration of gut microbiota composition as mentioned above [31], suggesting a role for microbiome pattern in the regulation of ApoA5. More interestingly, Oliva et al. recently suggested that APOA5 genetic and epigenetic variabilities may jointly regulate circulating TG levels [35]; minor allele carriers of APOA5 SNPs (rs662799, rs3135506, and 724C > G) had significantly higher circulating TG levels (by an average of 57.5%) than non-carriers [35]. At the same time, APOA5 promoter and exon 3 were hypermethylated, whereas exon 2 was hypomethylated. In particular, exon 3 methylation was positively correlated with circulating TG levels and a lipoprotein profile linked to atherogenic dyslipidemia [35]. Taking these findings together, circulating TG levels were the highest in minor allele carriers of at least one APOA5 SNP, possibly together with a high methylation percentage in exon 3 (≥ 82%) among the population, which was not experimentally proven in the present study. Since blood TG levels are strongly affected by not only genetic susceptibility but also environmental factors such as diet and lifestyle, this result may enhance our understanding of the effect of APOA5 on TG levels.
Despite a lack of experimental data supporting the observed association between APOA5 gene polymorphism and cardiometabolic traits, this study was able to confirm that the minor C allele of APOA5 rs662799 is significantly associated with cardiometabolic traits and, more specifically, negatively associated with circulating HDL cholesterol and positively associated with TG level in a large Korean population from the KoGES-ASAS and KNHANES. In addition, this genotype may exert its effect via post-transcriptional regulation, which further requires experimental confirmation. Our results should add to our current understanding that APOA5 might be a useful target for clinical therapeutic intervention.