extfieldsofstudy: Medicine Chemistry

text:
IFN-β Plays Both Pro- and Anti-inflammatory Roles in the Rat Cardiac Fibroblast Through Differential STAT Protein Activation

Cardiac fibroblasts (CFs) contribute to theinflammatory response to tissue damage, secreting both pro- and anti-inflammatory cytokines and chemokines. Interferon beta (IFN-β) induces the phosphorylation of signal transducer and activator of transcription (STAT) proteins through the activation of its own receptor, modulating the secretion of cytokines and chemokines which regulate inflammation. However, the role of IFN-β and STAT proteins in modulating the inflammatory response of CF remains unknown. CF were isolated from adult male rats and subsequently stimulated with IFN-β to evaluate the participation of STAT proteins in secreting chemokines, cytokines, cell adhesion proteins expression and in their capacity to recruit neutrophils. In addition, in CF in which the TRL4 receptor was pre-activated, the effect of INF-β on the aforementioned responses was also evaluated. Cardiac fibroblasts stimulation with IFN-β showed an increase in STAT1, STAT2, and STAT3 phosphorylation. IFN-β stimulation through STAT1 activation increased proinflammatory chemokines MCP-1 and IP-10 secretion, whereas IFN-β induced activation of STAT3 increased cytokine secretion of anti-inflammatory IL-10. Moreover, in TLR4-activated CF, IFN-β through STAT2 and/or STAT3, produced an anti-inflammatory effect, reducing pro-IL-1β, TNF-α, IL-6, MCP-1, and IP-10 secretion; and decreasing neutrophil recruitment by decreasing ICAM-1 and VCAM-1 expression. Altogether, our results indicate that IFN-β exerts both pro-inflammatory and anti-inflammatory effects in non-stimulated CF, through differential activation of STAT proteins. When CF were previously treated with an inflammatory agent such as TLR-4 activation, IFN-β effects were predominantly anti-inflammatory.

Interferon beta (IFN-β) is a cytokine produced by innate immune cells, including macrophages and dendritic cells, as well as non-immune cells, such as fibroblasts and epithelial cells (Ivashkiv and Donlin, 2014). IFN-β elicits a wide range of effects, including anti-inflammatory and pro-inflammatory responses, and also regulates the development and activation of virtually all innate and adaptive immune effector cells. IFN-β has a variety of documented effects on immune cells; for instance, IFN-β modulates TNF-α and IL-10 expression in peripheral blood mononuclear cells (Rudick et al., 1996;Coclet-Ninin et al., 1997), reduces TNF-α expression in monocytes (Jungo et al., 2001), and increases IL-10 expression in dendritic cells (Yen et al., 2015). However, the effects of IFN-β on non-immune cells such as CF remain unknown.
The cellular effects of IFN-β are mediated through the JAK/STAT signaling pathway and vary according to context and cell phenotype (Darnell et al., 1994;Ihle, 1995;Chen et al., 2004). Interferon type I receptor (IFNAR)activation by IFN-β induces signal transducer and activator of transcription (STAT) protein phosphorylation, and the canonical IFN-β signaling pathway can activate the formation of p-STAT1/p-STAT2 heterodimers that bind to interferon regulator factor 9. The activated proteins then translocate to the nucleus, activating the transcription of inflammatory genes known as interferon-stimulated response elements (Platanias, 2005). In contrast, the formation of p-STAT1 and/or p-STAT3 homodimers inhibits the activation of p-STAT1/p-STAT2 heterodimerdependent genes, suppressing the inflammatory properties of IFN-β (Ivashkiv and Donlin, 2014;López de Padilla and Niewold, 2016). Whereas, recent studies have provided significant insight into the roles of activated STAT2 and STAT3 proteins in modulating inflammation, especially in immune and vascular cells (Szelag et al., 2016). STAT3 modulates proliferation, differentiation, survival, oxidative stress, and metabolism in cardiomyocytes, fibroblasts, endothelial cells, progenitor cells, and various inflammatory cells (O'Shea and Murray, 2008;Haghikia et al., 2014). However, the effects of STAT protein activation by IFN-β on modulation of inflammatory response in CF are unclear. In this work, we sought to study the effects of IFN-β on CF pro-inflammatory (MCP-1, IL-1β, TNF-α and IP-10 secretion; VCAM-1 and ICAM-1 expression) and anti-inflammatory effects (IL-10 secretion); and the involvement of STAT pathway in mediating these actions. Furthermore, we hypothesized that these effects could be greatly influenced by inflammatory activation of CF, for which we stimulated CF TLR4 receptor, a well-studied receptor involved in pro-inflammatory fibroblast functions, with lipopolysaccharide (LPS), a TLR4 specific agonist, to promote CF inflammatory environment.

Animals
Male adult Sprague Dawley rats were obtained from the animal breeding facility of the Faculty of Chemical and Pharmaceutical Sciences at the University of Chile. The animals were housed in cages (12-h light/dark cycles) with access to rat chow and water ad libitum. All studies were performed in compliance with the NIH Guide for the Care and Use of Laboratory Animals, updated in 2011 1 , and experimental protocols were approved by our Institutional Ethics Review Committee.

Cell Culture and Treatments
Cardiac fibroblast were isolated from adult Sprague Dawley Rats (3-4 weeks old) using enzymatic digestion, as previously described . Briefly, rats were anesthetized by ketamine/xylazine injection and their hearts extracted in an aseptic environment. Atria were removed and ventricles cut into small pieces (1-2 mm) for later collagenase II digestion. The digestion yield was separated by 10-min centrifugation at 1000 rpm. The pellet was resuspended in 10 ml of Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F12) supplemented with 10% FBS and antibiotics (100 µg/ml streptomycin and 100 units/ml penicillin) and cultured in a humid atmosphere at 5% CO 2 /95% O 2 and 37 • C until confluence (5 days). The purity of the CF population was assessed with several markers. CF showed positive staining for vimentin (Santa Cruz Biotechnology, Santa Cruz, CA, United States) and were negative against sarcomeric actin and desmin (Sigma-Aldrich, St. Louis, MO, United States).
Cells from passage 1 were used for all experiments, and they were maintained in DMEM F12 for 18 h until experiments were performed. CF were pre-treated with IFNβ (500 U/mL) for 30 to 120 min, and LPS (1 µg/mL) was used as inflammatory stimulus. For experiments involved with JAK/STAT inhibition, CF were incubated with Ruxolitinib (JAK inhibitor, 500 nM), or transfected with Stat1, Stat2, and Stat3 siRNAs (100 ng/mL) for 8 h previous Ruxolitinib, IFN-β or LPS incubation.

Isolation of Neutrophils From Bone Marrow
For neutrophils isolation from bone marrow, we used cell sorting by flow cytometry fluorescence strategy (FACS) (BD FACSAria TM III, BD Biosciences) using RP-1 antibody as specific marker for rat neutrophils (Skrajnar et al., 2009). The femur and tibia were extracted from both extremities of the rats, and the epiphysis was excised at the level of the metaphysis of both bone types. The bone marrow is then removed by infusion of DMEM/F12 medium by means of a 25G-wide syringe onto a 100 mm Petri dish. The leukocyte and erythrocyte rich cell suspension was then centrifuged at 300 g for 10 min at room temperature. The erythrocytes present in the suspension were then lysed with 0.83% ammonium chloride (NH 4 Cl) for 7 min and the remaining suspension was washed twice with tissue storage solution (MACS) (2 mM EDTA, 0.5% BSA) and incubated with phycoerythrin (PE) conjugated anti RP-1 antibody at a concentration of 0.5 µl anti-RP-1 per 1 × 10 6 cells in 100 µl of total volume for 30 min, avoiding light. The leukocyte suspension is then washed twice with MACS medium and assayed by Cell Sorting (Beckson-Dickinson FACS flow and sorting cytometer). Approximately 20% of cells isolated were identified as RP-1 + cells (neutrophils). The sorted population consisted of 98% RP-1 positive neutrophils (less than 1-2% monocytes/lymphocytes) and a 99% in viability. Purified neutrophils were centrifuged and pooled for further experiments.

STATs Knockdown
FC were plated in 35 mm to 80% confluency and serum starved overnight. A solution of 4 uL Lipofectamine R + 100 uL Opti-MEM R and 5 uL siRNA (Silencer R ) + 100 uL Opti-MEM R was incubated for 15 min at room temperature, mixed and further incubated for 15 min. CF were treated with the siRNA preparation for 8 h at 37 • C, media replaced DMEM F12 for additional 24 h and lastly cells were harvested for further analysis. The codes products used were: RefSeq Stat1 NM_001034164.1, RefSeq Stat2 NM_001011905.1, RefSeq Stat3 NM_012747.2.

Western Blot Analysis
After respective treatments, cells were lysed in 50 mM Tris, 300 mM NaCl, 1 mM MgCl 2 , 0.5 mM ethylenediaminetetraacetic acid (EDTA), 0.1 mM EGTA, 20% glycerol, 1% NP40 cell lysis buffer, 0.5 mM 1,4-dithiothreitol (DTT), and inhibitor cocktail. Lysates were vigorously vortexed for 10 s and centrifuged at 15,000 rpm for 10 min, and total protein content was determined using Bradford assay. Equivalent amounts of protein were loaded to SDS PAGE. Western blotting was performed by transferring proteins to nitrocellulose membranes and blocking with 10% fat-free milk (w/v) in TBS-Tween for 1 h at room temperature. Membranes were probed with the appropriate primary antibody overnight at 4 • C and then with peroxidase-conjugated secondary antibody for 2 h at room temperature. Finally, the ECL Advance Western Blotting Detection Kit was used for immunodetection. Protein levels were determined by densitometric analysis using Image J (NIH, Bethesda, MD, United States) and normalized to GAPDH and total STAT levels, according to the experiment.

Immunofluorescence Assay
Cardiac fibroblasts were fixed in 4% paraformaldehyde solution for 20 min at room temperature and permeabilized in 0.1% Triton X-100 for 10 min at room temperature. Non-specific proteins were blocked with 3% bovine serum albumin solution for 30 min at room temperature. Cells in coverslips were incubated with the appropriate primary antibody overnight at 4 • C and an appropriate fluorophore-conjugated secondary antibody for 2 h at room temperature. Images were obtained using a spinning-disk microscope.

Luminex Immunobead Assay
Supernatant was collected and stored at −80 • C prior to cytokine analysis. Depending on the experiment CF were previously transfected with Stat siRNA and treated with IFN-β or LPS, or both for 24 h. One h before analysis, samples were thawed and analyzed for cytokine and chemokine protein levels using the Milliplex MAP-Rat Cytokine/Chemokine Magnetic Bead Panel Immunology Multiplex Assay according to manufacturer instructions. The proinflammatory cytokines (Il-6, TNF-α) and chemokines (MCP-1 and IP-10); and also antinflammatory cytoquines (IL-10) were measured. Cytokine levels were read on Luminex 200 System, Multiplex Bio-Assay. Quantification was based on standard curves for each cytokine, in the concentration range of 1.5-10,000 pg/ml. Values were standardized by measuring total amounts of cells and expressed as pg/50.000 cells. Error bars indicate theSD for four independent experiments. * * * p < 0.001, * * p < 0.01, * p < 0.05 vs. control. (D-F) CF were pretreated with or without Ruxolitinib (100 and 500 nM) for 1 h and stimulated with IFN-β (500 U/ml) for 24 h. The upper panel of each figure corresponds to the representative image of p-STAT1, p-STAT2, and p-STAT3 expression levels; STAT1, STAT2, and STAT3 were used as loading controls. The graphical analysis is presented at the bottom of each panel.

Migration Assays of Neutrophils
In vitro neutrophil migration assays were performed using Transwell R plates with porous polycarbonate membrane filters (6.5 mm diameter, 8.0 µm pore) between an upper and lower compartment. In the upper chamber, 4 × 10 5 neutrophils labeled with calcein were seeded in 200 µL of DMEM/F12. The lower chamber contained 500 µl of conditioned medium obtained from the treated CF with IFN-β and/or LPS in the presence or absence of anti-STAT siRNA. The neutrophils migrated for 3 h at 37 • C and 5% CO 2 /95% O 2 . The fluorescently labeled neutrophils that migrated to the lower compartment were measured using a fluorescence spectrometer equipped with a microplate (Multi-Mode Reader Synergy 2, BioTek). DMEM/F12 (without CF supernatant) was used in the lower compartment as a negative control and DMEM/F12 + 50% FBS was used as a positive control. The percentage of neutrophil migration was calculated as follows; 100 x [fluorescence of migrated cells/fluorescence of total aggregated cells]. Prior to these studies, we performed neutrophil population identification tests. RP-1 protein expression results confirmed that 20% of the total leukocyte population was composed of neutrophils (Supplementary Figure S1).

Adhesion Assays of Neutrophils
Cell adhesion in vitro was analyzed by Cell Sorting (Beckson-Dickinson FACS flow and sorting cytometer). In brief, leukocytes obtained from rat bone marrow were resuspended in DMEM/F12 medium at a rate of 1.5 × 10 6 cells/100 µl, and then were added to 35 mm plates on a confluent monolayer of CF (15 × 10 4 ) previously transfected with and without Scramble, siRNA Stat1, siRNA Stat2, and siRNA Stat3, and treated in the presence or absence of LPS and IFN-β. After an incubation period of 2 h at 37 • C, cell plates were washed with 1X sterile phosphatebuffered saline (PBS) and CF with adhered leukocytes were peeled off using 0.1% trypsin in 1X sterile PBS. The suspension of CF and adhered leukocytes were then washed with MACS medium for 5 min at room temperature and incubated with anti-RP-1/PE antibody at the rate of 0.5 µl of anti-RP-1 per 10 6 cells in 100 µl of total volume for 30 min, avoiding light. The cell suspension (CF and leukocyte), was then washed twice with MACS medium and assayed by Cell Sorting. Percentage of neutrophil adhesion was calculated as follows; 100 x (number of adherent RP-1 + cells/total number of aggregated RP-1 + cells). The analysis was performed using FlowJo 7.0 (TreeStar Software).

Statistical Analysis
Data are expressed as the mean ± SD of at least three independent experiments. Statistical analysis was performed using one-way ANOVA followed by Tukey's multiple comparison tests with GraphPad Prism Software. p < 0.05 was considered significant.

IFN-β Activates the JAK/STAT Signaling Pathway in CF
IFN-β (100, 500, or 1000 U/mL) induced concentrationdependent phosphorylation of STAT1, STAT2, and STAT3 in CF. Results were significant after 30 min and 1 and 2 h of IFN-β stimulation, respectively (Figures 1A-C). To assess whether STAT phosphorylation was dependent on the activation of JAK/STAT signaling pathway by IFN-β through activation of IFN-β receptor (IFNAR), CF were pretreated with 100 or 500 nM of JAK inhibitor Ruxolitinib, which inhibited IFNβ-induced STAT1, STAT2, and STAT3 protein phosphorylation (Figures 1D-F). Immunofluorescence results were consistent with this finding ( Figure 1G); which showed that stimulation of CF with IFN-β (500 U/mL) for 1 h increased STAT2 and STAT3 nuclear localization (white arrows). Taken together, these results show that IFN-β activates the JAK/STAT signaling pathway through the IFNAR.

In TLR4-Activated CF, IFN-β Inhibits Cytokines and Chemokines Secretion
Cardiac fibroblasts are important cellular effectors of the inflammatory response in cardiac tissue, where TLR4 activation by LPS or heparan sulfate has been shown to induce cytokine and chemokine secretion. Therefore, we evaluated whether IFN-β stimulates cytokines and chemokines secretion by CF. CF treated with IFN-β (24 h) did not modify IL-6 or TNF-α secretion levels, whereas it increased IL-10 secretion (Figures 2A-C). Ruxolitinib significantly inhibited the increase of IL-10 secretion induced by IFN-β. In contrast, treating CF with LPS (24 h) significantly increased IL-6, TNF-α and IL-10 secretion, although LPS produced a smaller effect than IFNβ on IL-10 secretion. Pre-treating CF with IFN-β for 1 h and then LPS for 24 h significantly inhibited the LPS-induced increase in IL-6 and TNF-α secretion (Figures 2A,B). However, IL-10 secretion levels were similar to those for IFN-β, and Ruxolitinib inhibited the effects of IFN-β, in the presence of LPS ( Figure 2C).
In regard to chemokines, treatment with either IFN-β or LPS increased MCP-1 and IP-10 secretion, although LPS produced a larger effect than IFN-β. However, pre-treating CF with IFN-β for 1 h and then LPS for 24 h resulted in lower MCP-1 and IP-10 secretion levels than those associated with LPS treatment. Pre-treatment with Ruxolitinib inhibited the effects of IFN-β in the presence or the absence of LPS (Figures 2D,E).
Collectively, these results suggest that in CF, IFN-β by itself stimulates IL-10, MCP-1 and IP-10 secretion; however, in the presence of a pro-inflammatory stimulus, such as LPS, INF-β dampens the secretion of inflammatory cytokines and chemokines triggered by this pro-inflammatory stimulus.
IFN-β Activates STAT2 and/or STAT3 to Inhibit LPS-Induced Cytokine and Chemokine Secretion in CF STAT1, STAT2, and STAT3 siRNA (si-STAT1, si-STAT2, si-STAT3) were used to evaluate whether IFN-β-activated STAT proteins modulated cytokine or chemokine secretion, either alone or in the presence of LPS. Each siRNA was specific for its particular target (Supplementary Figure S2).
STAT3 activation modulated IFN-β-induced decrease of IL-6 in presence of LPS, since si-STAT3 prevented the decrease of IL-6 induced by IFN-β on LPS-treated CF. Both STAT2 and STAT3 activation were involved in IFN-β-induced decrease of TNF-α levels in LPS treated CF, as demonstrated by si-STAT2 and si-STAT3 prevention of TNF-α decrease. On the contrary, cytokine levels (IL-6 and TNF-α) induced by IFN-β on LPS-treated CF were unaffected by STAT1 knockdown (Figures 3A,B).
In addition, STAT3 (and not STAT1 or STAT2) was also crucial in IFN-β-induced IL-10 secretion, since STAT3 knockdown reverse IFN-β effects both in presence and absence of LPS ( Figure 3C). In the case of MCP-1 and IP-10 secretion increased by IFN-β, it was observed that only STAT1 was necessary for such effect (Figures 3D,E). In contrast, STAT3 played an important role in modulating the IFN-β-induced MCP-1 secretion in the presence of LPS, since si-STAT3 reversed the effects of IFN-β on the LPS-induced activity. Neither STAT1 nor STAT2 modulated these effects of IFN-β ( Figure 3D). Finally, in regard to IP-10 secretion, the effect of IFN-β in presence of LPS was dependent on both STAT2 and STAT3 activation, while being independent of STAT1 ( Figure 3E).

IFN-β Activates STAT2 and STAT3 to Modulate LPS-Induced Pro-IL-1β Expression in CF
We have previously reported that LPS induces pro-IL-1β expression in CF. To evaluate the effects of IFN-β on expression of this cytokine, CF were treated with LPS for 8 h. LPS significantly increased pro-IL-1β expression, while IFN-β by itself had no effect. However, INF-β significantly decreased LPS-induced pro-IL-1β expression, which was completely abolished when CF were pretreated with Ruxolitinib ( Figure 4A) As shown by the use of siRNA, both si-STAT2 and si-STAT3 were involved in IFN-β effects on pro-IL-1β expression on LPS-treated CF, whereas STAT1 was not involved (Figure 4B).
protein expression in CF (another important characteristic of inflammation). As shown by Figures 5A,B, LPS significantly increased ICAM-1 and VCAM-1 protein expression levels in CF, while prestimulation of IFN-β inhibited LPS-induced expression of ICAM-1 and VCAM-1, by activation of STAT3. On the contrary, neither STAT1 and STAT2 were involved in IFN-β inhibition of cell adhesion proteins expression in LPS-treated CF.

IFN-β Activates STAT3 to Inhibit LPS-Induced Neutrophil Recruitment by CF
Previous work from our laboratory has shown that CF are able to recruit immune system cells by secreting chemokines and increasing cell adhesion proteins on the surface of CF. In agreement with our previous findings, we observed that LPS induced IP-10 and MCP-1 secretion in CF, as well as ICAM-1 and VCAM-1 expression, and that IFN-β inhibited these effects. We then asked whether the changes in both cytokines/chemokines and cell adhesion molecules induced by IFN-β could modulate neutrophil recruitment to CF. Neutrophil migration toward conditioned medium obtained from CF treated with IFN-β was increased respect to media from untreated CF. The migration of neutrophils was also increased when moving toward culture media obtained from LPS-treated CF; however, when CF were pretreated with IFN-β before LPS stimulation a synergic effect was not observed and neutrophil migration was similar to that observed in IFN-β alone (Figure 6). Only STAT1 activation was involved in neutrophil migration to IFN-β-treated CF media, whereas STAT2 and STAT3 were necessary for the migration of neutrophils to culture media obtained from LPS-treated CF that were previously incubated with IFN-β (Figure 6).
For the adhesion assays, 34.3% of the neutrophils adhered to CF surface ( Figure 7A). Adhesion increased to 40.3% for IFN-β-treated CF ( Figure 7B) and to 68.3%for LPS-treated CF (Figure 7C). In a similar trend to our previous results, CF pretreatment with IFN-β before LPS, seems to exert a nonsynergic antinflammatory effects since neutrophil adhesion was lower than LPS stimulation alone (42 versus 68.3%, respectively) ( Figure 7D). Adhesion was not dependent on STAT1 nor STAT2; however, activation of STAT3 by IFN-β, was crucial for inhibiting the LPS-induced neutrophil adhesion (Figures 7E,F). si-STAT3 blocked the effect of IFN-β, boosting neutrophil adhesion to 62.6% ( Figure 7G). Figure 7H shows a graphical analysis of the adhesion results.

DISCUSSION
IFN-β through differential STAT protein activation, either by itself or previous to a pro-inflammatory stimulus (in this case, LPS), acts as a key regulator of the cellular inflammatory response in CF. The proinflammatory actions of IFN-β are STAT1dependent, while its anti-inflammatory activity is mediated by STAT2 and/or STAT3 activation.

IFN-β Activates the JAK/STAT Signaling Pathway in CF
Activation of different STAT proteins by IFN-β produces diverse effects in various cell types (van Boxel-Dezaire et al., 2006). In cardiac tissue some studies have shown that cardiomyocytes express higher levels of IFN-β than CF; however, CF express higher levels of IFNAR, making CF more sensitive than cardiomyocytes to stimulation by IFN-β (Zurney et al., 2007). Type I interferons such as IFN-β can activate all known STAT FIGURE 6 | Neutrophil migration induced by conditioned culture media derived from IFN-β-and LPS-treated CF: assays were performed in transwell chambers. Neutrophils charged with fluorescent calcein were seeded in the upper chamber and allowed to migrate at 37 • C for 3 h to the lower chamber containing conditioned culture medium derived from CF that were transfected with scramble or 200 ng of STAT1 siRNA, STAT2, and STAT3 for 8 h, serum-deprived for 24 h, pretreated with IFN-β (500 U/ml) for 1 h, and stimulated with LPS (1 µg/ml) for 8 h. Fluorescent neutrophils that migrated to the lower compartment were measured using a fluorescence spectrometer, and the chemotactic activity was expressed as the percentage of fluorescent cells in the lower chamber as compared to the fluorescence of the total neutrophils added. Error bars indicate the SD for three independent experiments. * * * p < 0.001 vs. scramble. +++ p < 0.001 vs. scramble + LPS. &&& p < 0.001 vs. scramble + IFN-β. ### p < 0.001 vs. scramble + IFN-β +

LPS.
proteins, and in this study, IFN-β activated STAT1, STAT2, and STAT3 proteins in CF. These actions occurred through JAK, since the effects were inhibited by the JAK inhibitor Ruxolitinib. Zurney et al. (2007), demonstrated that IFN-β induces STAT1 and STAT2 phosphorylation in rat CF (Zurney et al., 2007); however, in CF STAT protein activation is not exclusive to IFN, as IL-6 and Angiotensin II also induce STAT3 activation (Tsai et al., 2008;Meléndez et al., 2010). Our findings show that IFN-β exerts its effects in CF in a time-and concentration-dependent manner. These results are consistent with Pertsovskaya et al. (2013), who demonstrated that IFN-β activates early STAT1 phosphorylation, although additional phosphorylation is observed at later times. Similar results have also been found in other cell types (van Boxel-Dezaire et al., 2006;Happold et al., 2014). Collectively, our results show that IFN-β triggers STAT1, STAT2, and STAT3 protein phosphorylation in CF, with comparable kinetics for all three STAT proteins, at similar IFN-β concentrations and with similar time courses.

IFN-β Through STAT1 Activation Induce a Pro-inflammatory Effect in CF
To date, there has been little published evidence regarding modulation of cytokine and chemokine secretion by IFN-β in FIGURE 7 | IFN-β decreases neutrophil adhesion to CF. Leukocytes were labeled with anti-RP1/PE (neutrophil marker) and incubated for 2 h at 37 • C with confluent monolayers of CF transfected with scramble or 200 ng si-STAT and treated with IFN-β and LPS. Cells were analyzed using flow cytometry. (A-G) FACS dots plots are representative images of the percentage of neutrophils adhered to CF. (H) Graphical analysis of the percentage of neutrophils in co-culture adhered to scramble-transfected CF with no treatment or treated with IFN-β or LPS. The neutrophil adhesion index was calculated was as follows: 100 x [no. cells (RP1 +) adhered/total number of cells (RP-1 +)]. Error bars indicate the SD for three independent experiments. * * * p < 0.001 vs. scramble. +++ p < 0.001 vs. scramble + LPS. ## p < 0.01 vs. scramble + IFN-β + LPS. cultured CF. Our results demonstrate a dual action of IFN-β increasing proinflammatory and also anti-inflammatory cytokine secretion; however, previous to a pro-inflammatory environment (such as TLR4 activation by LPS), it behaves as an antiinflammatory agent.
Chemokines, including MCP-1 and IP-10, are crucial for assembling the immune response, acting as chemoattractants for various immune cells (Frangogiannis, 2004). Our results show that IFN-β increased MCP-1 and IP-10 secretion by activating STAT1. Cumulative evidence suggests that STAT1 activation promotes the release of IP-10, in murine macrophages (Park et al., 2015), and it is well-known that increased STAT1 phosphorylation induces p65-NF-κβ phosphorylation and acetylation, promoting its activation and therefore increasing p65-NF-κβ-dependent proinflammatory response (Furrer et al., 2016). Whereas other studies have showed different results regarding the effect of STAT activation on chemokine and cytokine secretion. In human umbilical vein endothelial cells, MCP-1 mRNA expression was dependent on STAT3 activation (Wiejak et al., 2013). According to our results, we suggest that STAT1 activation by IFN-β plays a key role in the pro-inflammatory response of cultured CF.
In CF Previous to a Pro-inflammatory Stimulus, IFN-β Through STAT2 and/or STAT3 Activation Induces an Anti-inflammatory Effect Our results show that IFN-ß by itself, produces a modest increase in secretion of cytokine IL-10. However, when CF are challenged (1) IFN-β induces a pro-inflammatory response in the CF through STAT1 protein activation (green arrows), characterized by increased expression of the chemokines IP-10 and MCP-1, as well as (2) increased neutrophil migration, favored by chemokines released from CF.
with a potent pro-inflammatory stimulus like LPS, IFN-ß shows a stronger anti-inflammatory effect, as IFN-β inhibited LPSinduced IL-6 and TNF-α secretion. Our results suggest that STAT3 activation is crucial for IFN-β-induced IL-10 secretion, as well as its suppressive effect on the LPS-induced TNF-α and IL-6 secretion. Studies in other cell types partially coincide with our results. Ramgolam et al. (2009), suggested that IFN-β1 induces IL-10 secretion through STAT1 and STAT3 activation, and this effect inhibits Th17 cell differentiation (Ramgolam et al., 2009). However, other studies have found results contrary to ours. In murine dendritic cells, IFN-β-induced IL-10 secretion is mediated through the IFNAR in a STAT2-dependent manner (Yen et al., 2015), whereas STAT1 was also identified as a negative regulator of IL-10 expression in monocytes in vivo (VanDeusen et al., 2006). Our results suggest that in CF, IFN-β previous to a pro-inflammatory stimulus, provides a protective, anti-inflammatory effect increasing IL-10 levels and inhibiting LPS-induced IL-6 and TNF-α secretion.
Various studies have shown that CF are the main source of IL-1β in the infarcted myocardium (Long, 2001;Nian et al., 2004). Boza et al. (2016) demonstrated that in CF, LPS increases pro-IL-1β expression, which is dependent on the PI3K/Akt and NF-κβ signaling pathways. However, LPS does not activate the NLRP3 inflammasome and consequently does not triggers pro-IL-1β fragmentation and active IL-1β secretion. The same authors demonstrated that ATP is required for inflammasome assembly and activation, which triggers fragmentation of pro-IL-1β to its active form IL-1β and stimulates its secretion . Our results show that IFN-β inhibits LPS-induced pro-IL-1β expression through the JAK/STAT2 and/or STAT3 pathways.
Although the exact mechanism remains unknown, there is evidence that IFN-β affects IL-1β secretion. Guarda et al. (2011), reported that IFN-I inhibits IL-1β production in bone marrowderived murine macrophages through transcription of p-STAT1 dimers, repressing the activity of the inflammatory proteins NLRP1 and NLRP3 (Guarda et al., 2011). These interesting findings are consistent with our results, suggesting that IFN-I is capable of inhibiting pro-IL-1β production. The STAT-activated proteins implicated in the studies are different, likely attributable to the different cell types. However, other data from immune cell studies show contradictory results. Malireddi and Kanneganti (2013), demonstrated that IFN-I activates the inflammasome through upregulation of caspase-1 and expression of NLRP3, RIG-I, and AIM2, potentiating the inflammatory response. Given the importance of CF in IL-1β expression and secretion, our results suggest that in CF, the IFN-β through STAT2 and STAT3 activation negatively regulates the inflammatory response triggered by a pro-inflammatory stimulus.

IFN-β Activates STAT2 and STAT3 to Decrease Cell Adhesion Molecule Expression and Neutrophil Recruitment
Immune cell recruitment is a key step in the progression of inflammation-associated cardiovascular pathologies. This step depends initially on chemokine secretion and ICAM-1 and VCAM-1 expression at the inflammation site (Kukielka et al., 1993;Cook-Mills et al., 2011). Couture et al. (2009), have shown that TNF-α and PMA increase neutrophil recruitment by CF. Previous results from our laboratory have shown that in CF, TLR4 activation by LPS, induces monocyte recruitment by CF themselves, due initially to the monocyte migration favored by MCP-1 secretion; whereas ICAM-1 and VCAM-1 expression in turn favors monocyte adhesion . Our results show that in CF, IFN-β activates STAT3 to inhibit LPSinduced ICAM-1 and VCAM-1 expression. However, other studies have reported different results for the effect of IFN-β on adhesion protein expression. Combined treatment with TNFα, IL-1β, and IFN-β has been shown to increase ICAM-1 and VCAM-1 expression in human umbilical vein endothelial cells (Shen et al., 1997;Dhib-Jalbut and Marks, 2010). Similarly, in the alveolar macrophages of rats with bleomycin-induced pulmonary fibrosis, STAT1 activation correlated significantly with increased ICAM-1 expression and severity of lung tissue inflammation (Fan and Wang, 2005). Collectively, our results reinforce the concept that STAT2 and/or STAT3 activation by IFN-β are essentials for triggering the anti-inflammatory response in CF.
Neutrophil infiltration occurs within the first few hours after tissue injury (Braunwald and Kloner, 1985), representing a crucial step for initiation of inflammatory repair (Kakkar and Lefer, 2004;Frangogiannis, 2006). Our results show that in CF, treatment with IFN-β alone increases neutrophil migration through STAT1 activation. There is abundant and contradictory data in the literature regarding the effects of IFN-β on cell migration. IFN-β prevents neutrophil infiltration in animal models of cerebral inflammation (Veldhuis et al., 2003a,b), and decreases serum-induced migration of polymorphonuclear cells (Aas et al., 1996). On the other hand, our results show that when CF are challenged by a pro-inflammatory stimulus such as LPS, IFN-β through STAT2 and STAT3 activation decreases both chemokine secretion and adhesion molecule expression. This effects decreases neutrophil recruitment, demonstrating the anti-inflammatory properties of IFN-β in CF.
To date, no other in vitro studies have elucidated the effect of IFN-β and/or STAT activation on neutrophil recruitment by CF. Our results allow us to conclude that previous to a pro-inflammatory environment induced by an inflammatory insult, IFN-β exerts an anti-inflammatory effect in CF, reducing neutrophil recruitment. It is well-known that neutrophil recruitment after cardiac injury is crucial for rapidly inducing the healing process; however, prolonged neutrophil recruitment may lead to chronic inflammation in the tissue and eventually cardiac fibrosis (Frangogiannis et al., 2002;Frangogiannis and Entman, 2005;Frangogiannis, 2006).

CONCLUSION
Collectively, our results help to understand the in vitro regulation of INF-β-mediated inflammatory response in CF. IFN-β plays a dual role in regulating inflammation, acting as a crucial player in modulating the pro-or anti-inflammatory behavior of CF depending of environment. IFN-β by itself, activates STAT1 to generate a pro-inflammatory response, characterized by increased chemokines MCP-1 and IP-10 expression, which increase neutrophil migration (see Figure 8). However, IFN-β also activates STAT3 to induce secretion of the anti-inflammatory cytokine IL-10 (see Figure 9, blue arrow). Nevertheless, previous to a pro-inflammatory environment TLR4-activated, IFN-β triggers a marked anti-inflammatory effect in CF by activating STAT2 and/or STAT3. This anti-inflammatory effect is characterized by inhibition of LPS-induced IL-6, TNF-α, MCP-1, and IP-10 secretion and reduction of pro-IL-1β, ICAM-1, and VCAM-1 expression, decreasing neutrophil recruitment.

AUTHOR CONTRIBUTIONS
SB, RA, CH, RV, PB, and GD-A contributed to the conception and design of the study. CM and VP-J organized the database. SB, FO-S, and RV performed the statistical analysis. SB and GD-A wrote the first draft of the manuscript. CM, VP-J, PB, and CH wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy:
Biology
Environmental Science

text:
Extracellular Enzyme Activity Profile in a Chemically Enhanced Water Accommodated Fraction of Surrogate Oil: Toward Understanding Microbial Activities After the Deepwater Horizon Oil Spill

Extracellular enzymes and extracellular polymeric substances (EPS) play a key role in overall microbial activity, growth and survival in the ocean. EPS, being amphiphilic in nature, can act as biological surfactant in an oil spill situation. Extracellular enzymes help microbes to digest and utilize fractions of organic matter, including EPS, which can stimulate growth and enhance microbial activity. These natural processes might have been altered during the 2010 Deepwater Horizon oil spill due to the presence of hydrocarbon and dispersant. This study aims to investigate the role of bacterial extracellular enzymes during exposure to hydrocarbons and dispersant. Mesocosm studies were conducted using a water accommodated fraction of oil mixed with the chemical dispersant, Corexit (CEWAF) in seawater collected from two different locations in the Gulf of Mexico and corresponding controls (no additions). Activities of five extracellular enzymes typically found in the EPS secreted by the microbial community – α- and β-glucosidase, lipase, alkaline phosphatase, leucine amino-peptidase – were measured using fluorogenic substrates in three different layers of the mesocosm tanks (surface, water column and bottom). Enhanced EPS production and extracellular enzyme activities were observed in the CEWAF treatment compared to the Control. Higher bacterial and micro-aggregate counts were also observed in the CEWAF treatment compared to Controls. Bacterial genera in the order Alteromonadaceae were the most abundant bacterial 16S rRNA amplicons recovered. Genomes of Alteromonadaceae commonly have alkaline phosphatase and leucine aminopeptidase, therefore they may contribute significantly to the measured enzyme activities. Only Alteromonadaceae and Pseudomonadaceae among bacteria detected here have higher percentage of genes for lipase. Piscirickettsiaceae was abundant; genomes from this order commonly have genes for leucine aminopeptidase. Overall, this study provides insights into the alteration to the microbial processes such as EPS and extracellular enzyme production, and to the microbial community, when exposed to the mixture of oil and dispersant.

Extracellular enzymes and extracellular polymeric substances (EPS) play a key role in overall microbial activity, growth and survival in the ocean. EPS, being amphiphilic in nature, can act as biological surfactant in an oil spill situation. Extracellular enzymes help microbes to digest and utilize fractions of organic matter, including EPS, which can stimulate growth and enhance microbial activity. These natural processes might have been altered during the 2010 Deepwater Horizon oil spill due to the presence of hydrocarbon and dispersant. This study aims to investigate the role of bacterial extracellular enzymes during exposure to hydrocarbons and dispersant. Mesocosm studies were conducted using a water accommodated fraction of oil mixed with the chemical dispersant, Corexit (CEWAF) in seawater collected from two different locations in the Gulf of Mexico and corresponding controls (no additions). Activities of five extracellular enzymes typically found in the EPS secreted by the microbial community -αand β-glucosidase, lipase, alkaline phosphatase, leucine amino-peptidase -were measured using fluorogenic substrates in three different layers of the mesocosm tanks (surface, water column and bottom). Enhanced EPS production and extracellular enzyme activities were observed in the CEWAF treatment compared to the Control. Higher bacterial and micro-aggregate counts were also observed in the CEWAF treatment compared to Controls. Bacterial genera in the order Alteromonadaceae were the most abundant bacterial 16S rRNA amplicons recovered. Genomes of Alteromonadaceae commonly have alkaline phosphatase and leucine aminopeptidase, therefore they may contribute significantly to the measured enzyme activities. Only Alteromonadaceae and Pseudomonadaceae among bacteria detected here have higher percentage of genes

INTRODUCTION
The Deepwater Horizon (DwH) oil spill in the Gulf of Mexico was a catastrophic event that released 4.9 million barrels of oil (Turner et al., 2014). This was followed by the use of ∼2.9 million liters of the dispersing agent Corexit in an attempt to clear the oil from the surface of the ocean and protect coastal ecosystems (Kujawinski et al., 2011). Months after this event, the oil disappeared from the surface ocean, with factors such as natural light induced photo-oxidation, volatilization, sedimentation, and microbial degradation playing an important role (Mendelssohn et al., 2012). The ability of some bacterial taxa to degrade oil has been well established (Bailey et al., 1973), and oil biodegradation during the DwH spill has been extensively reported (Hazen et al., 2010;Valentine et al., 2010;Kostka et al., 2011). Following the oil spill, extracellular polymeric substances (EPS) combined with particulates and other materials, operationally defined as marine snow (Quigg et al., 2016), were observed in large amounts (Passow et al., 2012). Much of this marine snow was also found to have oil included in the aggregated material, so that it was referred to as 'marine oil snow' (MOS) by Passow et al. (2012). Enhanced EPS production in the form of transparent exopolymer particles (TEP) was observed in the presence of oil; this material might have acted as a bio surfactant (Kleindienst et al., 2015b).
Given that EPS production by phytoplankton can vary from 3 to 40% of the total primary productivity (Engel, 2002), and considering primary productivity accounts for 45-50 Pg C yr −1 in the ocean (Longhurst et al., 1995;Field et al., 1998), the amount of carbon released as EPS could range between 1.5 and 20 Pg C yr −1 . Furthermore, bacteria are also known to produce EPS (Manivasagan and Kim, 2014), and these EPS have been implicated to play a protective role against adverse environmental condition (Poli et al., 2010). EPS is heterogeneous in composition, consisting mainly of carbohydrates, proteins, monomers of sugars, amino acids, and uronic acids (Underwood et al., 2004;Xu et al., 2011a,b;Quigg et al., 2016). EPS can act as a carbon, nitrogen and phosphorus substrate that assists the growth of bacteria and mixotrophic phytoplankton, thereby boosting microbial activity (Decho, 1990). Several studies have shown that extracellular enzymes facilitate breakdown of complex polymers in EPS to less complex molecules (Jones and Lock, 1993;Romaní and Sabater, 2000;Espeland et al., 2001), which heterotrophic microbes would otherwise not be able to use. Heterotrophic microbes secrete several types of extracellular enzymes (exoenzymes) that assist them in degrading the various components of EPS (Decho, 1990) such as αand β-glucosidase, alkaline phosphatase, leucine amino-peptidase, and lipase (Yamada and Suzumura, 2010). As enzymes are specific to the substrates they act upon and microbes vary in their ability to produce different kinds of enzymes, the differences in activities between enzymes can be used as an indirect indicator of microbial functional diversity in the system and/or the nutrient composition of the system (Caldwell, 2005).
In addition to the EPS, the oil and Corexit can act as a source of carbon to the microbes during the DwH oil spill, which may have affected EPS production and enzyme activities. Oil and Corexit has also known to cause a significant change in the microbial community, favoring hydrocarbon degraders (Bacosa et al., 2015b;Kleindienst et al., 2015a;Doyle et al., 2018). Therefore, how the addition of oil and Corexit can affect EPS production, extracellular enzyme production, microbial community and aggregate formation needs to be examined. In this study, we conducted mesocosm scale experiments with seawater from two sites in the Gulf of Mexico. A chemically enhanced water accommodated fraction of oil (CEWAF) prepared from a mixture of oil and Corexit (ratio of 20:1) in seawater and Controls (no additions) were used to test the effects of oil/dispersant mixture on the microbial community, their enzymatic activity and EPS production in three different layers within the mesocosm tanks (surface, water column, and bottom).

Mesocosm Study
Two mesocosm studies were carried out using seawater collected from the Gulf of Mexico during July 2016. The first site located at 27 • 53N, 94 • 2W (salinity: 30.77 ppt, pH: 8.38, temperature: 30.8 • C) was chosen as an offshore, open ocean site (∼174 km from shore) while the second site at 29 • 22N, 93 • 23W (salinity: 31.13 ppt, pH: 8.02, temperature: 30.5 • C) was chosen as a coastal site (∼20 km from shore). These will be referred to as offshore and coastal respectively. The seawater was supplemented with nutrients at f/20 concentrations (Guillard and Ryther, 1962) before starting the six mesocosms (3 controls, 3 CEWAF). The seawater was used directly as a Control treatment (87 L each mesocosm). Macondo surrogate oil (25 ml) and dispersant in the ratio of 20:1 were combined to produce a chemically enhanced water accommodated fraction (CEWAF) of oil according to Wade et al. (2017). Briefly, the oil and Corexit were added together before being transferred to the corresponding seawater and mixed in 130 L circulating baffled tanks for 24 h under low light and ambient temperature (∼21 • C). At the end of this period, the 87 L of CEWAF was transferred to the mesocosm tanks by pumping from the bottom of the baffled tank in order to avoid the surface slick. The initial nitrate and phosphate concentration were 97 (±4.7) µMol.L −1 and 11.2 (±0.3) µMol.L −1 in the Control and 119 (±1.9) µMol.L −1 and 5.5 (±0.4) µMol.L −1 in the CEWAF of the offshore mesocosm. Whereas, the initial nitrate and phosphate concentration were 133.3 (±33.8) µMol.L −1 and 10.2 (±0.2) µMol.L −1 in the Control and 130.0 (±7.4) µMol.L −1 and 11.0 (±0.3) µMol.L −1 in the CEWAF of the coastal mesocosm respectively. The initial estimated oil equivalent (EOE) concentration in the offshore and coastal CEWAF treatment were 39.06 (±0.77) mg.L −1 and 81.06 (±20.50) mg.L −1 , respectively. The incubation time for the offshore and coastal mesocosms were 96 hrs and 72 h respectively. The incubation time for both the mesocosm were decided based on the percentage of oil consumed/remaining in the CEWAF tanks. Due to technical issues associated with replicating CEWAF with the same initial oil concentration, the percentage oil concentration was chosen as the deciding parameter over actual oil concentration in terminating the study. The offshore mesocosms took 96 h to reach ∼20% of the initial oil concentration, whereas it only took 72 h in the coastal mesocosm.

Sample Collection
Samples for enzyme activity, EPS composition, and total organic carbon (TOC), dissolved organic carbon (DOC), and particulate organic carbon (POC) analysis were collected from three different layers of the mesocosm tanks on the last day. The mesocosm tanks were 74.5 cm long and 43 cm wide. Samples collected in the top 2-5 cm of the tanks were designated as the surface. Samples collected through a spigot mounted on the side of the tanks were designated as water column. Finally, samples collected from the floor of the tanks were designated as the bottom. A syringe was used to collect the samples from the surface and bottom respectively. The samples were collected from these three layers in order to account for the differences in aggregation (higher in the bottom layer for Control and in the surface for CEWAF treatment) observed during the experiment. Samples for bacterial community composition and micro-aggregate counts/sizes were collected concurrently from the water column layer.

Enzyme Assays
Enzyme activities for αand β-glucosidase, alkaline phosphatase, leucine amino-peptidase, and lipase were measured on the last day according to Yamada and Suzumura (2010). The enzymes and the substrates used in this study are listed in Table 1. The substrates were dissolved in milli-Q water so that the final stock solutions were 1 mM. The substrate for 4-Methylumbelliferyl oleate was dissolved in minute volume (250 µl) of DMSO and the concentration was adjusted with milli-Q water to a final concentration of 1 mM, Substrates were then added to Control and CEWAF samples in triplicate to a final concentration of 0.2 mM. The samples were then incubated at room temperature in the dark for 3 h. After incubation, the reactions were stopped by the addition of 1 mL of borate buffer solution (0.4 M) adjusted to pH 8.0 for 7-amido-4-methylcoumarin (AMC)tagged substrates or pH 10.0 for 4-methylumbelliferyl (MUF)tagged substrates. The fluorescence intensity was then measured at excitation/emission wavelengths (nm) of 380/440 (AMC) or 365/448 (MUF) using a spectrofluorometer (Shimadzu RF-5300). The measurements were then corrected with the blank values obtained using heated seawater samples (80 • C for 15 min) in duplicate at the beginning of the incubation. Respective substrates corresponding to the different enzymes were added to the blank samples prior to incubation and measurement.

EPS Analysis
Extracellular polymeric substances composition was measured in terms of carbohydrate, protein, and uronic acid content and total EPS was calculated by summing these parameters. Particles were collected with a polycarbonate filter (0.4 µm, Millipore, United States), and the attached EPS from the particles was then extracted with 0.35 M EDTA followed by an ultrafiltration step to remove the salts and excessive EDTA (Xu et al., 2009(Xu et al., , 2011a. EPS from the dissolved phase was directly obtained by concentrating and desalting using an Amicon Ultra-15 centrifugal filter unit with ultracel-3 membrane (Millipore, 3 kDa). The carbohydrate concentration in the EPS was determined by anthrone method with glucose as the standard (Yemm and Willis, 1954). The protein content of EPS was determined with the help of a Pierce BCA protein assay kit based on a modified bicinchoninic acid method with bovine serum albumin as the standard (Smith et al., 1985). Uronic acids in the EPS were estimated by the addition of sodium borate (75 mM) in concentrated sulfuric acid and m-hydroxydiphenyl according to Blumenkrantz and Asboe-Hansen (1973) with glucuronic acid as the standard for this assay.

TOC, DOC, and POC Analysis
TOC and DOC were determined using a Shimadzu TOC-L analyzer (Xu et al., 2011b). For POC analysis, water sample was filtered through a pre-combusted GF/F membrane (0.7 µm, Whatman, United States), and then quantified using a Perkin Elmer Series II CHNS 2400 analyzer, after HCl-fuming to remove the carbonates. Acetanilide (71.09%) was used as the analytical standard (Xu et al., 2011a). Samples from the offshore mesocosm were limited, therefore only samples from the coastal mesocosm were analyzed.

Dissolved Oxygen (DO) Concentration
A calibrated 556 MPS YSI meter (Yellow Springs, OH, United States) fitted with a DO/Temperature sensor (5563-10) was used to measure the DO (mg L −1 ) directly in the surface and

Microbial and Micro-Aggregate Counts/Sizes
Direct cell counts were performed on the samples collected from the water column on the last day in three replicate tanks per treatment. Samples were visualized with an epifluorescence microscope (Zeiss Axio Imager.M2) after staining the fixed samples with DAPI (45 µM final concentration) for 5 min in the dark and filtering them onto 25 mm, 0.2 µm black polycarbonate filters, according to Doyle et al. (2018). Microbial cell counts were performed at 1000× magnification and, due to their much larger size, micro-aggregates were quantified at 400× magnification. For micro-aggregate abundance, the presence of a micro-aggregate was counted, not the number of cells present per micro-aggregate. Micro-aggregates were defined as groups of cells in clumps 10-200 µm in diameter, often found gathered around drops of oil. Size fraction analysis of aggregates was also performed using Z1 dual-threshold Coulter counter (Beckman Coulter). It should be noted that these aggregates do not exactly correspond to the microbial micro-aggregates measured above, and likely include those as well as other particles in that size fraction. Samples (15 mL) from the water column were taken on the last day and analyzed immediately. Particles of four different size ranges (5-10, 10-20, 20-50, and >50 µm) were counted with a 100 µm aperture. A sample of filtered seawater was used as blank (typically less than 10 particles were counted). Samples were diluted with filtered seawater (0.2 µm) if the particle coincidence at the aperture exceeded 5%, where particle coincidence is the chance of more than one particle passing through the aperture at once.

Bacterial Community Composition
Prokaryotic community composition (Bacteria and Archaea) was analyzed as described in detail in Doyle et al. (2018). Briefly, samples (150 ml) collected from the water column in three replicate tanks per treatment concurrently with other samples were pre-filtered through 10 µm filters to remove most eukaryotic cells followed by filtration onto 47 mm 0.22 µm Supor PES filter membranes (Pall). Total DNA was extracted from filters using FastDNA Spin kits (MP Biomedical). 16S rRNA gene (hyper-variable V4 region) was PCR amplified with GoTaq Flexi DNA Polymerase (Promega) according to Caporaso et al. (2012), with specifics in Doyle et al. (2018). Amplifications were performed using the 515F-806R universal primer pair according to recent revisions (Apprill et al., 2015;Parada et al., 2016), which included Golay barcodes and adapters for Illumina MiSeq sequencing. The products were combined and quantified with the QuantiFluor dsDNA System (Promega), pooled and purified with an UltraClean PCR Clean-Up Kit (MoBio Laboratories). The library, along with the three sequencing primers, were sent to the Georgia Genomics Facility (Athens, GA, United States) for MiSeq sequencing (v2 chemistry, 2 × 250 bp). Sequence processing was carried out using mothur v.1.36.1 (Schloss et al., 2009) following a modified version of the protocol described in Kozich et al. (2013). Analysis of rarefaction curves was conducted using all available reads. Generation of operational taxonomic units (OTUs) and analysis of alpha and beta diversity was conducted using a dataset subsampled to 39,054 samples per read (Supplementary  Table S1). Goods coverage was >0.99 for all samples.
Raw DNA sequence data used in this project can be found in the NCBI Genbank database under accession numbers SAMN07795505-SAMN07795510 (Offshore) and SRR6176504, SRR6176505, SRR6176497, SRR6176488, SRR6176473 and SRR6176442 (Coastal).

Screening of Microbial Genomes
To determine which of the most relatively abundant bacterial families detected are potentially capable of extracellular enzyme production, we searched the Integrated Microbial Genomes (IMG) database (Markowitz et al., 2006) for genomes within these orders using the "Find Functions" search and the "Enzymes (list)" filters for each bacterial order searched, similar to previous work (Jacobson Meyers et al., 2014). EC 3.4.11.1 was used to search for the gene(s) encoding leucine aminopeptidase, EC 3.1.3.1 was used to search for the gene(s) encoding alkaline phosphatase, EC 3.1.1.3 was used to search for genes encoding for lipase, EC 3.2.1.20 was used to search for genes encoding α-glucosidase, and EC 3.2.1.21 was used to search for genes encoding β-glucosidase. Genomes were scored as positive if they contained a gene that encoded for an exoenzyme, and then the number of genes within the family was tallied to calculate the percentage of genomes within that family capable of producing each enzyme. This analysis depends on a few assumptions: (a) if an organism is a member of a clade in which a specific enzyme is more abundant, then that organism is more likely to have the enzyme, (b) the annotations in IMG are reliable, and (c) that the exoenzymes assayed are expressed extracellularly, rather than intracellularly.

Statistical Analysis
The results were statistically analyzed by using one-way ANOVA with multiple comparisons of the mean of each group with the mean of every other group using a Tukey test. These statistical analyses were performed using GraphPad Prism software (version 7.0f).

RESULTS
Measurement of α-glucosidase activities in different layers of the CEWAF tanks revealed highest activity at the surface in both the offshore and the coastal mesocosms (One way ANOVA: p < 0.02; Figure 1A). Similar patterns were seen for both β-glucosidase and alkaline phosphatase, although the differences in alkaline phosphatase were not statistically significant (Figures 1B,C). In the Control tanks, the activities of α-glucosidase, β-glucosidase and alkaline phosphatase were significantly higher at the bottom than in other layers in the offshore mesocosm (One way ANOVA: p < 0.0005; Figure 1). While the activities were similarly higher at the bottom layer in the Control tanks of the coastal mesocosm, the differences FIGURE 1 | Average enzyme activities (±SD) of (A-E) αand β-glucosidase, alkaline phosphatase, lipase, leucine amino-peptidase measured in the surface, water column and bottom of the offshore and coastal mesocosms.
were statistically significant only for alkaline phosphatase (One way ANOVA: p < 0.0015). Comparison of Control vs. CEWAF tanks in the coastal mesocosm revealed significant differences for α-glucosidase, β-glucosidase and alkaline phosphatase only at the surface (Two way ANOVA: p < 0.004). However, for the offshore mesocosm, α-glucosidase and β-glucosidase activities were significantly higher in CEWAF compared to Control in all three layers (Two way ANOVA: p < 0.0001), whereas alkaline phosphatase was significantly higher only at the surface (Two way ANOVA: p = 0.0011).
Measurement of lipase activities revealed slightly different profile compared to α-glucosidase, β-glucosidase and alkaline phosphatase ( Figure 1D). For CEWAF tanks, lipase activities were highest in the surface compared to other layers in the offshore mesocosm (Two way ANOVA: p < 0.002), similar to that observed for α-glucosidase, β-glucosidase and alkaline phosphatase. However, in the coastal mesocosm, lipase activities were the lowest in the water column (Two way ANOVA: p < 0.002), and similar between surface and bottom layer in CEWAF tanks. Comparison of Control vs. CEWAF tanks in the offshore mesocosm revealed significant differences only at the surface for the offshore mesocosm (Two way ANOVA: p < 0.0001). However, in the coastal mesocosm, lipase activities were significantly higher in CEWAF than Control in both surface and bottom layers (Two way ANOVA: p < 0.004).
Measurement of leucine aminopeptidase activities in different layers of the CEWAF tanks revealed highest activity at the surface in the offshore mesocosm (One way ANOVA: p < 0.0001; Figure 1E). A similar pattern was observed for leucine aminopeptidase in the coastal mesocosm, however the differences were not statistically significant (Figure 1). In Control tanks, the activities were higher at the bottom for leucine aminopeptidase in the offshore mesocosm (One way ANOVA: p < 0.0001), whereas the activities were similar in the coastal mesocosm. CEWAF had significantly higher leucine aminopeptidase activities than the Control in all the layers for the offshore mesocosm (Two way ANOVA: p < 0.02), however, significant differences were only seen at the surface for the coastal mesocosm (Two way ANOVA: p = 0.02). Overall, most of the enzymes showed higher activities at the surface for CEWAF treatments and at the bottom for Control treatment in the offshore and/ or coastal mesocosms (Figure 1). In addition, overall enzyme activities were significantly higher in CEWAF treatment than in the Control for both offshore (Unpaired t-test: p = 0.0057) and coastal (Unpaired t-test: p = 0.0312) experiment (Supplementary Figure S1).

EPS Composition Across the Layers
The polysaccharide, protein, and uronic acid content was measured to determine the overall EPS composition (Figure 2). Total EPS concentration was highest in the water column in both treatments for both mesocosms (Two-way ANOVA: p < 0.0001). In Control tanks, significantly higher concentration of EPS was observed at the bottom layer compared to the surface layer in the offshore mesocosm (Unpaired t-test: p = 0.009). Similar trends  were observed in the coastal mesocosm samples, however, the differences were not statistically significant ( Figure 2D). In the CEWAF tanks, there was more EPS in the surface than the bottom layer in both the mesocosms (Unpaired t-test: p < 0.003). Exposure to CEWAF treatment significantly increased the amount of polysaccharide, proteins and uronic acids produced in the water column and in the surface (Two-way ANOVA: p < 0.0001; Figure 2). Comparison of EPS composition in the water column of CEWAF relative to the Control, showed higher production of proteins in both the coastal (5.3 fold) and offshore (8.1 fold) mesocosms followed by uronic acids (4.4 and 2.6 fold) and carbohydrates (2.9 and 2.4 fold). Comparison of EPS composition in the surface of CEWAF relative to the Control showed similar patterns to that observed in the water column.

TOC, DOC, and POC
In the coastal mesocosm, TOC and DOC concentrations were highest in the surface layers in both treatments (Two-way ANOVA: p < 0.0025) ( Figure 3A). Overall, there was significantly more TOC and DOC in the CEWAF tanks than in the Control tanks at all layers ( Figure 3B) (Two-way ANOVA: p = 0.0007). POC concentrations were significantly higher in the CEWAF treatment in the surface layer than in the Control (Unpaired t-test: p < 0.00001) (Figure 3C). The bottom POC concentration was higher than both the water column (Two-way ANOVA: p = 0.0007) and surface layers (Two-way ANOVA: p = 0.0056) in the Control treatment. In the CEWAF treatment, the POC was higher at the surface than in the water column and bottom ( Figure 3C), although these differences were not statistically significant (One-way ANOVA: p > 0.2571).

Dissolved Oxygen Concentration
The DO concentration in the CEWAF mesocosms was very low on the last day, decreasing to almost zero at the bottom of the offshore and coastal tanks ( Table 2). A decrease in DO in the CEWAF treatments from 0.3 (±0.12) mg L −1 at the surface to 0.1 (±0.04) mg L −1 at the bottom of the tanks was observed in offshore and 0.4 (±0.01) mg L −1 to 0.22 (±0.04) mg L −1 in coastal tanks ( Table 2). These concentrations are considered to be hypoxic to anoxic. By comparison, the Control treatments had a significantly higher dissolved oxygen concentration throughout the tank with an average value of 6 (±0.37) mg L −1 in offshore and 7.5 (±1.11) mg L −1 in the coastal mesocosms from surface to bottom.

Microbial and Aggregate Counts
Compared to the Control, bacterial counts in the CEWAF treatments were approximately 3.3 fold higher in offshore and nearly 2 fold higher in coastal seawater (Figure 4). Similarly, the micro-aggregate counts in CEWAF were nearly 14 fold higher in offshore and 12 fold higher in coastal as compared to Control treatments (Two-way ANOVA: p < 0.0001). Further analysis of the aggregates based on size showed significantly higher particle concentrations in 5-10, 10-20, and 20-50 µm size range in the CEWAF treatment compared to the Control (Two-way ANOVA: p < 0.0001) (Figure 5). Control tanks had significantly higher number of particles than CEWAF treatments in the size range of >50 µm in the coastal mesocosm (Two-way ANOVA: p < 0.0001); however, no differences were observed for the same size range in offshore tanks. Particles abundance was more similar between coastal and offshore tanks across all the size ranges except for 10-20 µm, where it was higher in the coastal mesocosm (Two-way ANOVA: p < 0.0001) (Figure 5).

FIGURE 4 | (A,B) Average bacterial and micro-aggregate numbers (±SD) in
Control and CEWAF treatments from the offshore and coastal mesocosms.

Microbial Community Composition
Prokaryotic communities in Control treatments were more diverse in both mesocosms than in the CEWAF treatments (Supplementary Figure S2 and Supplementary Table S1). Based on 16S rRNA amplicon sequence data, Bacteria were detected at much higher relative proportion than Archaea in both mesocosm experiments (Figure 6). Microbial community composition was dramatically different between the Control and CEWAF samples for both the coastal and offshore mesocosms. The mean relative abundance of the family Alteromonadaceae was highest, followed by Piscirickettsiaceae, Rhodobacteraceae, Flavobacteriaceae, Pseudomonadaceae, and Rhodospirillaceae. Among these abundant families, the genera with the highest relative abundances was Marinobacter, followed by Alteromonas (both belonging to the family Alteromonadaceae), Methylophaga (Piscirickettsiaceae), unclassified Oceanospirillales, Cycloclasticus (Piscirickettsiaceae), Aestuariibacter (Alteromonadaceae) and Tenacibaculum (Flavobacteriaceae). Three different OTUs of Marinobacter (family Alteromonadaceae), OTU3, OTU8 and OTU11, represented 21% of the CEWAF community in the offshore and 45% of the CEWAF community in the coastal experiments, respectively (Supplementary Table S2). OTU level responses were not always uniform within a genus. For example, the relative abundance of the two most abundant OTUs of Methylophaga (family Piscirickettsiaceae) was different between treatments, with OTU2 having highest relative abundance in CEWAF in the offshore mesocosm and OTU5 exhibiting higher relative abundance in the Control for both mesocosm experiments.
Compared to all the extracellular enzymes, α-glucosidase was present in the lowest percentage (<75%) of genomes amongst all the abundant bacterial orders (Figure 7). β-glucosidase was 100% positive in Cellvibrionaceae, members of which were abundant in both the CEWAF treatments. In addition, β-glucosidase was highly positive (76-99%) in Pseudomonadaceae, which was abundant in both CEWAF treatments and the offshore Control treatments (Figure 7). Lastly, Erythrobacteraceae, which had higher relative abundance in the offshore mesocosm than the coastal mesocosm were highly positive for β-glucosidase as well (Figure 7). Alteromonadaceae and Pseudomonadaceae had the highest percentage of genomes positive for lipase (51-75%) (Figure 7). Both these orders, especially Alteromonadaceae, were significantly abundant in both the CEWAF treatments. Relative to other extracellular enzymes, leucine amino-peptidase was highly positive (76-100%) amongst all the abundant bacterial orders with the exception of Flavobacteriaceae and Saprospiraceae (Figure 7). Alkaline phosphatase was highly positive (76-99%) amongst Alteromonadaceae, Pseudomonadaceae and Cellvibrionaceae, which were abundant in both the CEWAF treatments (Figure 7). Amongst bacterial orders abundant in the Control treatments, alkaline phosphatase were 100% positive in FIGURE 7 | Distribution of bacterial orders with high relative abundance (defined as orders which are present at ≥1% relative abundance in two or more samples) and percentages of sequenced genomes containing exoenzymes assayed. The x-axis measures the relative abundance of taxa. AP, alkaline phosphatase; LAP, leucine aminopeptidase; αGlu, α-Glucosidase; βGlu, β-Glucosidase. The sequence of the genes coding for these enzymes were obtained from IMG database.

DISCUSSION
The goal of this study is to better understand the role of extracellular enzymes, EPS production, and microbial community composition in the presence of a mixture of oil and dispersant. Five different enzymes: αand β-glucosidase, lipase, alkaline phosphatase and leucine amino-peptidase were studied in the surface, water column and bottom of the mesocosm tanks filled with either coastal or offshore water from the Gulf of Mexico with and without CEWAF. Elevated extracellular enzyme activities have been previously reported in oil containing aggregates . Also dense microbial colonization and active degradation of oil and components of EPS have been reported Arnosti et al., 2016), suggesting a link between extracellular enzymes and microbial aggregates.
α and β-glucosidase help in the digestion of complex polysaccharides (Reese et al., 1968). These enzymes help in cleavage of glycosidic bonds with α and β-glucosidase acting at α and β -linkages allowing the release of glucose molecule from polysaccharides (de Melo et al., 2006). In the Control treatments, highest α and β-glucosidase activity was seen in layers whereby the polysaccharide concentration in the EPS was lower and vice-versa. Similar patterns but relatively higher levels of activity, were observed in CEWAF treatments as well. This inverse relationship between glucosidase activity and EPS polysaccharide concentration across the three layers is indicative of active breakdown of polysaccharides by these enzymes.
α and β-glucosidase have been shown to help the turnover of polysaccharides secreted by phytoplankton and bacteria, as EPS fueling heterotrophic metabolism in the ocean (Piontek et al., 2010). We therefore hypothesize that the increased activity of these enzymes at the bottom for the Control and surface for CEWAF might help breakdown of the EPS, thereby enhancing the availability of simple carbon in the form of glucose. In addition, compared to the Control, a relatively higher glucosidase activity was observed in CEWAF in both the offshore and coastal mesocosms. Enhanced polysaccharide degradation by extracellular enzymes, especially laminarin, which are targets of glucosidase was observed by Arnosti et al. (2016) in oil associated aggregates. In addition, the polysaccharide concentrations decreased in the Control treatments from ∼0.15 mg.L −1 at 0 hrs (data not shown) to ∼0.0003 mg.L −1 at 96 h in the bottom of offshore and ∼0.18 mg.L −1 (data not shown) to 0.0002 mg.L −1 in the coastal mesocosm. Similarly in the CEWAF tanks, the polysaccharide concentrations decreased from ∼0.19 mg.L −1 (data not shown) at 0 h to ∼0.0009 mg.L −1 at 72 h in the surface of offshore and ∼0.36 mg.L −1 (data not shown) to 0.002 mg.L −1 for the coastal mesocosm. We therefore hypothesize that CEWAF induced enhanced EPS secretion in the form of polysaccharide at an earlier time point of this experiment that may have been rapidly degraded by these glucosidases. Ziervogel et al. (2012) previously showed a correlation between lipase activity and oil degradation. Therefore, the higher lipase enzyme activity at the surface and at the bottom of the mesocosm tanks for the CEWAF treatments, along with other hydrocarbon degrading enzymes (not measured in this study), might have played a role in breakdown of the oil components. Analysis of relative prokaryotic abundance showed the presence of lipase producing families, such as Alteromonadaceae, Oceanospirillaceae, and Pseudomonadaceae, in relatively higher numbers. Several other studies focusing on DwH oil spill have reported the presence of these families in samples containing high concentrations of oil (Dubinsky et al., 2013;Lamendella et al., 2014;King et al., 2015;Campeão et al., 2017). Additionally, the lipase producing genomic capabilities detected among members of the families Altermonadaceae, and Pseudomonadaceae suggest that these taxa might have played a significant role in the degradation of oil in the CEWAF treatment. The products of these extracellular hydrocarbon degrading enzymes might have led to increased carbon availability in the CEWAF treatment.
Leucine aminopeptidase (LAP) helps microbes acquire nitrogen by breaking down proteins and peptide molecules (Fruton and Mycek, 1956). Likewise, alkaline phosphatase is responsible for degradation of organic phosphates, providing a source of phosphorus to microbes (Martínez, 1968). Both leucine amino-peptidase and alkaline phosphatase activity patterns had higher activities at the surface than in the water column and the bottom of the mesocosm tanks for CEWAF treatments. EPS protein content was higher in the water column of the mesocosms than at the surface or bottom, similar to that observed for polysaccharides. Moreover, the higher leucine amino-peptidase and alkaline phosphatase activities at the surface for the CEWAF treatment pattern matches well with αand β-glucosidase activities. This suggests active assimilation of nitrogen and phosphorus through leucine amino peptidase and alkaline phosphatase along with carbon through α and β-glucosidase from the EPS, which could have contributed in the generation of microbial biomass. There are other reports where enzyme activities have been shown to correlate with the C:N:P requirements of microbes (Sinsabaugh et al., 2008(Sinsabaugh et al., , 2009. Therefore, we hypothesize that the products of these extracellular enzyme activities observed in our mesocosm tanks may have supported microbial requirements that lead to increased microbial biomass observed in the CEWAF tanks. Analysis of alkaline phosphatase, leucine amino-peptidase and β-glucosidase in whole water and <10 µm size fractions were statistically indistinguishable (Whitaker, personal communication), indicating that at least large eukaryotes > 10 µm are not responsible for the vast majority of activity measured. These assumptions aside, similar to other studies (Gutierrez et al., 2013;Kleindienst et al., 2015b;Yang et al., 2016a,b), our observations showed that Alteromonadaceae, represented largely by Marinobacter, Alteromonas, and Aestuariibacter, are the most abundant bacterial order detected and also very commonly have alkaline phosphatase and leucine aminopeptidase. This indicates a potentially large contribution to overall enzyme activity from these genera to alkaline phosphatase and leucine aminopeptidase enzyme activity rates. In particular, for AP and Lipase, the percentages of enzyme positive genomes are highly uneven, indicating that a consortia of microbes is necessary to efficiently hydrolyze EPS in situ.
It is also interesting that the family Piscirickettsiaceae is abundant but seems to only use leucine amino-peptidase out of the five enzymes in any significant proportion. All other abundant prokaryotes potentially contribute to 3-4 of the measured enzymes in a similar proportion within the family, but Piscirickettsiaceae operate outside this trend. This may indicate that this abundant family gets its phosphorus and carbon through other means. The two genera common in our mesocosm experiments, Methylophaga and Cycloclasticus, are putative hydrocarbon degraders (Gutierrez and Aitken, 2014). Therefore, they may have used hydrocarbons for carbon and enzymes other than lipase or glucosidases to metabolize that carbon-source.
Some members of the most abundant families in our mesocosms are known as "cheaters"; abundant community members that have minimal to no contribution in terms of enzyme activity (Allison, 2005). For example, members of the family Planctomycetaceae and SAR11 clades (Unclassified, Surface 1, and Surface 2) were abundant in the Control treatments but genomes from these lineages do not harbor abundant exoenzymes: < 10% of Pelagibactereaceae genomes have any of the enzymes assayed here, with the exception of leucine aminopeptidase, which is present in 47% of the genomes queried. Interestingly, there were almost no families lacking extracellular enzyme producing ability (cheaters) in the CEWAF treatments. This could be due to the potential toxicity of oil and Corexit, or the hypoxic environment in the CEWAF treatments restraining the microbial community exclusively to a few dominant families that are being selected due to their ability to tolerate and degrade hydrocarbons (Hamdan and Fulmer, 2011;Bacosa et al., 2012Bacosa et al., , 2015bLiu et al., 2017). Such reduced microbial diversity limited to fewer families such as Alteromonadaceae, Pseudomonadaceae, Oceanospirillaceae, Piscirickettsiaceae, and Idiomarinaceae has been reported in other studies focusing of DwH oil spill (Kleindienst et al., 2015b;Yang et al., 2016a,b).
The overall abundance of potential oil degraders such as members of Alteromonadaceae, Pseudomonadaceae, Cellvibrionaceae, and Piscirickettsiaceae in the CEWAF treatments suggests oil may have acted as the primary carbon source. However, since the glucosidase were higher in CEWAF treatments, we hypothesize the products of glucosidases may have provided additional carbon source as well. Moreover, the heavy supply of carbon also increases the demand for other elements such as nitrogen and phosphorus, which are not present in oil. We assume, some portions (in addition to the supplemented nutrients in the mesocosm tanks) of the required high levels of nitrogen and phosphorus to support the growth may have been provided by means of extracellular enzyme reactions such as alkaline phosphatase and leucine aminopeptidase. The overall activities of enzymes were relatively higher in CEWAF treatments than Control, and similar observations were made by Kleindienst et al. (2015b). Therefore, the products of these enzymes may have played a role in the high cell numbers seen in this treatment relative to Control. Several reports indeed have shown that the products of these extracellular enzyme can support the growth of bacteria (Frankenberger and Dick, 1983;Vetter and Deming, 1999). Apart from higher cell counts, statistically higher micro-aggregate counts was also observed in the CEWAF treatments compared to Control. This can be explained by relatively higher protein content of the EPS produced in response to CEWAF. We hypothesize that increase in proteins content may have enhanced the amphiphilic nature of the EPS, which may have facilitated better interaction of the EPS with the oil. This in-turn may lead to formation of more micro-aggregates in CEWAF treatment. Such role of EPS interaction with oil in the formation of aggregates has been previously suggested (Passow et al., 2012;Kleindienst et al., 2016;Quigg et al., 2016). Although, the enzyme activities and EPS production were higher in the CEWAF treatments than in Control, any interpretation on the effect of dispersant addition alone has to be taken with caution as the study did not compare CEWAF to WAF or a Corexit only treatment. Kleindienst et al. (2015b) suggested Corexit could suppress microbial activity, on the other hand, Bacosa et al. (2015a,b) showed positive effect on growth and alkane degradation in the presence of the dispersant Corexit. There were many differences between the experimental conditions used in Bacosa et al. (2015a,b) and Kleindienst et al. (2015b) such as temperature (8 • C vs. ambient temperature similar to our study) and site of sampling depth (1500 m vs. surface similar to our study). Kleindienst et al. (2015b) worked with a closed bottle system in the dark for 6 weeks while our study lasted a few days in an open system. These differences and others in experimental conditions may have influenced the outcome of dispersant addition on microbial activity. Therefore, despite the observation of higher microbial activity in our CEWAF treatments, further studies are essential to gain a more detailed understanding the effects of dispersant addition alone. Different levels of enzyme activities, EPS production, cell and aggregate counts were observed in response CEWAF of the offshore relative the coastal mesocosm. We hypothesize that these differences could be primarily due to the different initial conditions in the offshore and coastal waters.

CONCLUSION
Our study shows that addition of oil and dispersant Corexit enhances extracellular enzyme activity and EPS production in relative to Control, and a similar comparison between oil and dispersant mixture with oil only treatment is needed. Microbial community in CEWAF treatment was mostly dominated by hydrocarbon degraders such as members of Alteromonadaceae, Pseudomonadaceae and Cellvibrionaceae. The higher protein content of EPS in response to CEWAF may have facilitated increased aggregation. However, further studies comparing CEWAF treatment with WAF with time course measurements are needed to discern the effect of dispersant addition on extracellular enzyme and EPS production and aggregation.

AUTHOR CONTRIBUTIONS
MK designed the experiment, conducted the enzyme measurements and data analysis, and wrote the manuscript. CX helped in conducting the EPS and carbon analysis and manuscript preparation. KS helped in designing the experiment and writing of the manuscript. LB helped in measurements of the particle size and manuscript preparation. MB helped in conducting the EPS and carbon analysis. SD conducted the genomic and associated data analysis and manuscript preparation. JG and JH helped in conducting the experiment. JS helped in genomic data analysis and manuscript preparation. PS helped in designing the experiment and manuscript preparation. AQ mentored the study and helped in designing the experiment and manuscript preparation.