Micrornas Affect Complement Regulator Expression And

Author Manuscript Published OnlineFirst on September 19, 2019; DOI: 10.1158/2326-6066.CIR-18-0818 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

MicroRNAs affect complement regulator expression and

mitochondrial activity to modulate cell resistance to complement-

dependent cytotoxicity

Yaron Hillman1, Mariya Mardamshina2, Metsada Pasmanik-Chor3, Lea Ziporen1,

Tamar Geiger2, Noam Shomron1, and Zvi Fishelson1

1Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel

Aviv University, Tel Aviv 69978, Israel

2Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of

Medicine, Tel Aviv University, Tel Aviv 69978, Israel

3The Bioinformatics Unit, Faculty of Life Sciences, Tel-Aviv University, Tel Aviv

69978, Israel

Corresponding author: Zvi Fishelson, Department of Cell and Developmental

Biology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.

Phone: 9723-6409620. Fax: 9723-6407432. E-mail address: [email protected]

Running title: microRNAs and complement resistance of cancer cells

Keywords: complement-dependent cytotoxicity, cell death, microRNA, CD59,

mitochondria

Acknowledgements

This research was supported in part by grants from the Israel Science Foundation and

the Israel Cancer Association.

Disclosure of Potential Conflicts of Interest

The authors declare no conflict of interest.

1 1

Downloaded from cancerimmunolres.aacrjournals.org on October 1, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 19, 2019; DOI: 10.1158/2326-6066.CIR-18-0818 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

MicroRNAs (miRs) are small RNA molecules that shape the cell

transcriptome and proteome through regulation of mRNA stability and translation.

Here, we examined their function as determinants of cell resistance to complement-

dependent cytotoxicity (CDC). To achieve this goal, we compared the expression of

microRNAs between complement-resistant and -sensitive K562 leukemia, Raji

lymphoma, and HCT-116 colorectal carcinoma cells. Global microRNA array

analysis identified miR-150, miR-328, and miR-616 as regulators of CDC resistance.

Inhibition of miR-150 reduced resistance, whereas inhibition of miR-328 or miR-616

enhanced cell resistance. Treatment of K562 cells with a sublytic dose of complement

was shown to rapidly increase miR-150, miR-328, and miR-616 expression. Protein

targets of these microRNAs were analyzed in K562 cells by mass spectrometry–based

proteomics. Expression of the complement membrane regulatory proteins CD46 and

CD59 was significantly enhanced after inhibition of miR-328 and miR-616.

Enrichment of proteins of mitochondria, known target organelles in CDC, was

observed after miR-150, miR-328, and miR-616 inhibition. In conclusion, miR-150,

miR-328, and miR-616 regulate cell resistance to CDC by modifying the expression

of the membrane complement regulators CD46 and CD59 and the response of the

mitochondria to complement lytic attack. These microRNAs may be considered as

targets for intervention in complement-associated diseases and in anti-cancer,

complement-based therapy.

2 2

Introduction

In our body, cells constantly interact with proteins of the complement system.

For protection from the potentially noxious effects of complement, cells block or

regulate the activation of the complement initiation process and terminal cascades

through specific inhibitors expressed on their surface, mainly CD46, CD55, and CD59

(1,2). CD46, the membrane cofactor protein, and CD55, the decay-accelerating factor,

inhibit the activation of C3b deposition on cells at an early stage (3,4), whereas CD59

blocks the formation of C5b-9 or the membrane attack complex (MAC) at the

terminal stage (5). Malignant transformation and postulated sculpting interactions

with the complement system yield cancer cells with elevated expression of

complement membrane regulators and, thus, are more resistant to complement-

dependent cytotoxicity (CDC)(6). Cancer cells are also known to overexpress several

claimed intracellular inhibitors of CDC, mortalin/GRP75 (7), Hsp90 (8), PKC, and

ERK (9). This study examined the hypothesis that global changes at the

transcription/translation level, in particular, at the microRNA level, occurring during

the malignant transformation process and possibly involving cell encounters with the

innate immune system, generate a complement-resistant cancer cell phenotype.

MicroRNAs (miRs) can potentially halt the expression of multiple protein-

coding genes (10,11), and typically, each microRNA binds at its specific “seed”

sequence region at the 3′ UTR of mRNAs (12). This promotes mRNA degradation or

impairs mRNA translation (13), leading to a decline in protein expression (11). The

human genome encodes thousands of microRNA genes, several of which are

expressed in each cell (14) and body fluids (15), that regulate at least half of all

human gene transcripts (16). MicroRNAs are implicated in multiple diseases,

3 3

including cancer (17-19). Hence, miR-based therapy is now emerging as a novel

approach in treating cancer and various other diseases (20-23).

There is almost no data on the association between microRNAs and the cells’

sensitivity to CDC. We previously identified the contribution of miR-200 and miR-

217 to mortalin function and also to the extent of cell resistance to CDC (24). Here,

we first performed a systemic microRNA array analysis in which the microRNAs’

expression was compared between complement-resistant and complement-sensitive

tumor cells. Validated differential expression analysis identified miR-150-5p, miR-

328-3p, and miR-616-3p as regulators of CDC resistance. Next, the differential

expression of proteins whose expression was affected by these microRNAs was

characterized by comparing the proteome in extracts of microRNA-inhibited versus

control cells. The results suggest that miR-150-5p, miR-328-3p, and miR-616-3p may

be therapeutic targets in cancer immunotherapy and complement-associated diseases.

Materials and Methods

Cells, sera, and antibodies

Cells were cultured in RPMI-1640 (Sigma, Rehovot, Israel) (K562 and Raji

cells) or DMEM (HCT-116 cells) supplemented with 10% (v/v) heat-inactivated fetal

bovine serum (Life Technologies, Grand Island, NY), 1% glutamine, 2% pyruvate,

100 g/ml penicillin, and 400 g/mL streptomycin (Bio-Lab, Jerusalem, Israel) at

37°C and 5% CO2. Fresh cells were thawed every 2 months. Cells were purchased

from ATCC (Manassas, VA) and were mycoplasma free. Cell line authentication was

performed by STR DNA profiling analysis. Complement-sensitive and -resistant cell

lines were generated as indicated in the Results.

4 4

Normal human serum (NHS) prepared from the peripheral blood of healthy

individuals (3H Biomedical AB, Uppsala, Sweden, 3H1001-100-P,

) served as a source for complement. Its use was

approved by the Ethics Committee of Tel Aviv University. Heat-inactivated serum

(HIS) was prepared by heating NHS at 56°C for 45 minutes. NHS and HIS were kept

frozen at -70°C in small aliquots. A polyclonal anti-serum, directed to human K562 or

carcinoma cells, was prepared in rabbits in the animal facilities of Tel Aviv University

(approved by the Animal Ethics Committee of Tel Aviv University)(25). The

collected antiserum was kept frozen at –70°C in small aliquots. A polyclonal goat

anti-human C3 antibody was purchased from Complement Technology, Inc. (Tyler,

Texas). The mouse monoclonal antibody directed to a neoepitope in human C5b-9

(clone aE11), was purchased from Hycult Biotech (Uden, The Netherlands). Mouse

monoclonal anti-human CD46 (clone MEM-258), anti-human CD55 (clone 67) and

anti-human CD59 (clone MEM-43) antibodies were purchased from AbD Serotec

(Oxford, UK). FITC-conjugated goat anti-mouse IgG, FITC-conjugated donkey anti-

goat IgG, Cy3-conjugated goat anti-mouse IgG, and Cy3-conjugated donkey anti-goat

IgG were purchased from Jackson Immunoresearch (West Grove, PA).

Plasmids and transient transfection

Precursor microRNA expression clones and microRNA inhibitor expression

clones for miR-150, miR-328, miR-616, and control plasmids were purchased from

GeneCopoeia (Rockville, MD, USA). MicroRNA inhibitors specific for miR-328-3p

(MIMAT0000752: 5'CUGGCCCUCUCUGCCCUUCCGU), miR-25-5p

(MIMAT0004498: 5'AGGCGGAGACUUGGGCAAUUG), miR-126-3p

(MIMAT0000445: 5'UCGUACCGUGAGUAAUAAUGCG), miR-500a-5p

(MIMAT0004773: 5'UAAUCCUUGCUACCUGGGUGAGA), miR-150-5p

5 5

(MIMAT0000451: 5' UCUCCCAACCCUUGUACCAGUG), miR-532-3p

(MIMAT0004780: 5'CCUCCCACACCCAAGGCUUGCA), miR-199a-3p

(MIMAT0000232: 5'ACAGUAGUCUGCACAUUGGUUA), and a non-specific

oligonucleotide (negative control A: YI00199006; TAACACGTCTATACGCCCA)

were purchased from Exiqon (Vedbaek, Denmark). The non-specific oligonucleotide

was designed to have no known microRNA targets. K562 cells (4x106) were

resuspended in electroporation buffer (20 mM PIPES, 128 mM glutamate, 10 µM

calcium acetate, 2 mM magnesium acetate in RPMI-1640). They were then mixed

with: (a) 15µg of precursor microRNA expression plasmid or a microRNA inhibitor

expression plasmid, or control plasmids; or (b) 5nM of miRNA inhibitor or negative

control. Finally, they were subjected to electroporation (300 mV, 15 msec) in ECM-

830 (BTX Harvard Apparatus, Holliston, MA) in 4 mm electroporation cuvette. The

cells were then suspended in culture medium and cultured at 37°C for 24 hours

(miRNA overexpression) or 48 hours (miRNA inhibition).

Complement-dependent cytotoxicity and flow cytometry

To investigate complement-dependent cytotoxicity (CDC), parental and variant

K562, Raji, and HCT-116 cells were first treated with diluted antibodies in Hank's

Balanced Salt Solution (Sigma-Aldrich) for 30 minutes at 4°C and then with

complement for 60 minutes at 37°C. Rabbit antibodies directed to K562 or carcinoma

cells were used. NHS (final: 50%) was used as the source for complement and HIS

(final: 50%) as a negative control. The antibody dose used was determined based on a

cytotoxicity titration curve in which the antibody dose was varied in the presence of

50% NHS. When treatment inhibited CDC, the percentage of control cell death was set

to be >50%, and when treatment enhanced CDC, the percentage of control cell death

was set to be <50%. Specific details are given in legends of figures.

6 6

The percentage of cell death was determined by propidium iodide or DAPI

inclusion in a FACSCalibur (Becton Dickinson, Franklin Lakes, NJ). The PI and DAPI

concentrations used did not label intact viable cells. Flow cytometry data were analyzed

with Flowing Software 2.5.1. To calculate the inhibition percentage of cell death, the

cytotoxicity percentage was converted into y/1-y in which 100y = the percentage of cell

death (26). Thus, a percentage cytotoxicity of 50%, y=0.5 and y/1-y = 0.5/(1-0.5) = 1.

To measure C3b or C5b-9 deposition, K562 cells (1x106) were incubated with

a sublytic dose of antibodies (yielding 10–20% dead cells, as determined by titration

curve), diluted in in Hank's Balanced Salt Solution (HBSS), for 30 minutes at 4°C)

and then with NHS or HIS (final: 50%) for 10 minutes at 37 °C. These conditions

were chosen in order to minimize noise from non-specific binding of C3 and C5b-9 to

dying, necrotic cells. Under lytic antibody and complement conditions (yielding

>30% dead cells), many cells were necrotic within 10 minutes treatment. The cells

were then labeled with goat anti-C3 (diluted 1:500 in HBSS) or mouse anti-neo C5b-9

(clone aE11; diluted 1:100 in HBSS) for 30 minutes at 4°C, followed by FITC- or

Cy3-conjugated secondary antibodies (diluted 1:100 in HBSS) for 30 minutes at 4°C.

To quantify complement regulator expression, cells were treated with mouse anti-

CD46, anti-CD55, or anti-CD59 (10 g/ml in HBSS) for 30 minutes at 4°C and then

with FITC-conjugated secondary antibodies (diluted 1:100 in HBSS) for 30 minutes

at 4°C. Finally, the cells were analyzed by flow cytometry as described above.

RNA extraction and real-time PCR

RNA was extracted from K562, Raji, or HCT-116 cells using TRI Reagent

(Sigma), and the RNA concentration was measured with a NanoDrop ND-1000

spectrophotometer (NanoDrop Technologies, Thermo Fisher Scientific, Wilmington,

DE). First-strand complementary DNA (cDNA) was synthesized from total RNA (10

7 7

ng) using the TaqMan microRNA reverse transcription kit (Applied Biosystems,

Foster City, CA, USA). This reaction contained a specific stem–loop primer for each

mature target microRNA. Each stem–loop primer was designed to hybridize to only

the fully mature microRNA, and not to the precursor forms of its target. PCR

amplification was carried out using a Step One Plus Real-Time PCR System (Applied

Biosystems) under the following thermal cycler conditions: 2 min at 50 °C, 10 min at

95 °C, 40 cycles for 15 s at 95 °C and 1 min at 60 °C. Results in triplicates were

analyzed with SDS software (Applied Biosystems) and RQ (relative quantity)

Manager Software (Applied Biosystems), for automated data analysis. MicroRNA

relative expression was calculated based on the comparative threshold cycle (Ct)

method. The Ct for each microRNA and endogenous control U6 small nuclear

(sn)RNA (used for normalization) in each sample was used to create ΔCt values

(CtmicroRNA – CtU6 snRNA). Next, ΔΔCt values were calculated by subtracting the

ΔCt value of the control group from the ΔCt value of the tested group. The RQs were

calculated using the equation: RQ = 2−ΔΔct. TaqMan miRNA Assays for hsa-miR-

500a-5p (Assay ID 478309_mir), hsa-miR-532-3p (Assay ID 478336_mir), hsa-miR-

199a-3p (Assay ID 477961_mir), hsa-miR-328-3p (Assay ID 478028_mir), hsa-miR-

616-3p (Assay ID 478177_mir), hsa-miR-486-3p (Assay ID 478422_mir), hsa-miR-

548b-5p (Assay ID 478589_mir), hsa-mir-1254 (Assay ID 478660_mir), hsa-miR-25-

5p (Assay ID 478786_mir), hsa-miR-597-5p (Assay ID 478339_mir), hsa-miR-509-

5p (Assay ID 478965_mir), hsa-miR-150-5p (Assay ID 477918_mir), hsa-miR-126-

3p (Assay ID 477887_mir) and hsa-miR-1225-3p (Assay ID 477875_mir) were

purchased from Applied Biosystems.

MicroRNA arrays

8 8

Complementary DNA (cDNA) was synthesized from total RNA extracted

from K562, Raji, or HCT-116 cells using the Megaplex reverse transcriptase reaction

with the High Capacity cDNA kit (Applied Biosystems, Foster City, CA, USA). The

TaqMan Low-Density Arrays (TLDAs) are quantitative real-time PCR assays

(Applied Biosystems technology, catalog number 4342265) that enable accurate

quantitation of 754 human microRNAs. Each array includes three TaqMan

MicroRNA Assay endogenous controls to aid in data normalization. For each sample,

cDNA (500 ng), the TaqMan Universal PCR Master Mix (No AmpErase UNG;

Applied Biosystems), and RNase-free water were mixed and loaded onto human

TLDA cards A and B (according to the manufacturer’s instructions). The card was

centrifuged and sealed, and PCR amplification was performed using an ABI Prism

7900HT Sequence Detection System (Supplementary Array S1) and analyzed as

described above for real-time PCR.

Proteomic analysis

K562 cells subjected to inhibition of miR-150, miR-328, or miR-616

expression or treated with control, non-specific miR inhibitor (see above) were lysed

using 6 M urea and 2 M thiourea in 0.1 M Tris, pH 8.5, then reduced with 1 mM

dithiothreitol and alkylated with 5 mM iodoacetamide for 30 minutes each, followed

by overnight digestion using LysC-trypsin mix (Promega, Madison, WI). The

resulting peptides were fractioned using strong cation exchange fractionation in

StageTip format and then fractionated by high performance liquid chromatography

(Easy nLC 1000 HPLC system; Thermo Fisher Scientific) coupled online to a Q-

Exactive Plus mass spectrometer (Thermo Fisher Scientific) using the EASY-Spray

ionization source (NSI with spray voltage of 2.10). Peptides from each fraction were

separated with a flow rate of 0.3 l/min for 140-minute linear gradient of water-

9 9

acetonitrile using a PepMap 50 cm long C18 column. All the measurements were done

in positive mode. Raw MS files were analyzed by MaxQuant (version 1.5.3.36) with

the integrated Andromeda search engine. MS/MS searches were performed against the

human Uniprot database (published September 2015). Protein sequences were

reversed to form a decoy database for FDR calculation. Then, a 1% false discovery

rate was applied to both protein and peptide identification. To obtain quantitative data,

the label-free quantification (LFQ) algorithm in Maxquant was used. Predictions of

the miRNA targets were made by using TargetScan (release 7.2)

[http://www.targetscan.org]).

Bioinformatics and statistical analysis

Bioinformatics analysis was performed using Partek Genomics Suite v 6.6

(http://www.partek.com/pgs). Because the results are based on two separate

experiments, batch removal was performed. A total of 20 samples were analyzed (five

replicates for each treatment). Student’s t-test was performed to compare the control

group (cells transfected with control plasmid) and cells transfected with a microRNA

inhibitor plasmid (anti-miR-150, anti-miR-328, or anti-miR-616) with a fold change

cutoff of 1.5. Results are expressed as the arithmetic mean±S.D. An ANOVA test was

performed on LFQ intensities (P<0.05) to compare the groups. Hierarchical clustering

was performed after z-score normalization of the rows (proteins) using Euclidean

distances between averages. For gene ontology enrichment analysis, the

bioinformatics software DAVID (The Database for Annotation, Visualization

and Integrated Discovery) was used (27). Predicted functional associations between

proteins were analyzed with STRING software (https://string-db.org) (28). In the

functional cell analyses, statistical significance (two-sided unpaired Student’s t-test)

was determined with a cutoff of P<0.05.

Results

Complement-sensitive and -resistant cells: MicroRNA array analysis

In order to identify microRNAs involved in complement-dependent cytotoxicity

(CDC), we generated complement-resistant cell variants. K562 leukemia, Raji

lymphoma, and HCT-116 colorectal carcinoma cells were exposed to several cycles of

CDC over 2-3 months. After each cycle, the surviving cells were further grown and

subjected to another cycle of CDC. Briefly, in each exposure cycle, cells (1x106) were

treated at 4°C for 30 minutes with a rabbit anti-cancer antibody and then with NHS

(50%) for 60 minutes at 37oC, under conditions yielding 70-90% cell death, washed into

culture medium, and were grown in the incubator until disappearance of most dead cells

from cell culture (3-10 days). The cells were supplemented with fresh culture medium

every 48 hours. The number of exposure cycles required to gain resistance to CDC was

5 for HCT-116 cells, 8 for Raji and 10 for K562 cells. Parental cells (non-treated) were

maintained during the selection period under continuous growth at 37°C and were

divided in fresh culture medium every 48 hours. Consequently, the surviving cells

became significantly more resistant to CDC than parental cells, which were kept during

the selection period under continuous growth (Figs. 1A-C). To identify miRNA-

mediated resistance, RNA samples were extracted from parental and resistant variant

cells, analyzed on microRNA array cards, and parental (sensitive) and variant (resistant)

cells were compared (Supplementary Array S1). The expression of 19 microRNAs was

found to be higher in all resistant K562, Raji, and HCT-116 cells relative to their

parental cells, and the expression of five microRNAs was lower (Student's t-test,

P<0.01)(Fig. 1D, Supplementary Table S1). The expression fold-change of 14

microRNAs, calculated from the microRNA array data (Fig. 1E), was confirmed by

real-time PCR of the same RNA samples (Fig. 1F).

MicroRNA expression and complement-dependent cytotoxicity

The interaction of cancer cells with complement has been investigated in

great detail in K562 cells (1,29-31). Hence, these cells were chosen for an in-depth

analysis of the function of the microRNAs in CDC. To determine whether the

observed upregulation or downregulation of microRNA expression observed above

was causative for abnormal CDC responses or incidental to the selection process, the

effect of microRNA inhibition on CDC was examined. For this purpose, seven

microRNAs were tested, and two of them were found to have a statistically significant

impact on CDC (Fig. 2A). Inhibition of miR-328 resulted in reduced sensitivity to

CDC, whereas inhibition of miR-150 increased it. Therefore, miR-328, miR-150, and

miR-616 were chosen for further investigation. MiR-616 was included in the extended

analysis for two reasons: (i) its expression was elevated in complement-resistant cells

(Supplementary Table S1), and (ii) it was predicted (according to TargetScan.org) to

target CD46 and CD59, two important complement regulatory proteins.

The specific effect of these three microRNAs on the cell sensitivity to CDC

was then examined in HCT-116 cells transfected with a miR-150, miR-328, or miR-

616 expression plasmid or a control plasmid for 24 hours. Upon treatment with rabbit

anti-carcinoma antibody and complement (as described in the Methods), the death of

cells overexpressing miR-150 was significantly (half-fold change, P=0.006) than that

of cells overexpressing miR-328 or miR-616 (1.35-fold change, P=0.0008 or 1.69-

fold change, P=0.022, respectively) relative to the death of the control cells

(Supplementary Fig. S1). K562 cells were transfected with a specific microRNA

inhibitory plasmid (48 hours) or with a miR-150, miR-328, or miR-616 expression

plasmid (24 hours). The cells were then treated with rabbit anti-K562 antibody and

complement and cell death was measured. Inhibition of miR-150 increased cell

sensitivity (Fig. 2B), whereas overexpression of miR-150 resulted in reduced

sensitivity (Fig. 2E). Inhibition of miR-328 or miR-616 resulted in reduced cell death

(Figs. 2C-D), whereas overexpression of miR-328 or miR-616 increased cell death

(Figs. 2F-G). These observed effects were not consistent with the findings of the

global profiling of miRNAs in complement-selected cells (Fig. 1). Because expression

of miR-328 and miR-616 was upregulated in the selected, relatively resistant cells, we

expected that their inhibition would lead to increased sensitivity to CDC and not to

protection from CDC. An explanation addressing this discrepancy is offered in the

Discussion.

Effect of sublytic complement on expression of miR-150, miR-328, and miR-616

Sublytic and non-lytic doses of complement do not cause cell death but instead

activate a variety of cellular responses (32,33), including elevation of cell resistance

to CDC (34). Therefore, the impact of sublytic complement on the basal expression of

miR-150, miR-328, and miR-616 was also investigated. K562 or HCT-116 cells were

incubated with a sublytic dose of rabbit anti-K562 or -carcinoma antibody,

respectively, and complement for 1 hour. RNA was extracted from half of the cells,

while the other half was grown in culture medium for 3 hours at 37°C and then RNA

was extracted from these cells. RT-PCR showed that treatment for 1 hour with

sublytic complement triggered in K562 cells enhanced expression of miR-150 (3.5-

fold), miR-328 (2.5-fold), and miR-616 (9.4-fold)(Fig. 2H). After 4 hours, microRNA

expression declined. HCT-116 cells also responded, albeit to a lower extent, to

sublytic complement with enhanced microRNA expression (Fig. 2I). However, the

response in HCT-116 cells was more pronounced 4 hours after treatment (miR-150:

1.9-fold; miR-328, and miR-616: 1.5-fold).

Effect of modified microRNA expression on C3 and C5b-9 deposition

The ability of microRNAs to regulate cell resistance to complement-dependent

cytotoxicity raises the possibility that these microRNAs, through their target genes,

affect the extent of C3 and C5b-9 binding to the cells. To test this hypothesis, K562

cells were transfected with a microRNA inhibitor or with a microRNA expression

plasmid, followed by rabbit anti-K562 antibody and complement treatment. Inhibition

of miR-150 increased C3 deposition (Fig. 3A), and overexpression of miR-150

reduced C3 deposition (Fig. 3B). Inhibition of miR-328 did not have an appreciable

effect on C3 deposition (Fig. 3C), and overexpression increased C3 deposition (Fig.

3D). MiR-616 inhibition or overexpression had no appreciable effect on the amount of

bound C3 (Figs. 3E-F). The extent of C5b-9 deposition was then analyzed. Inhibition

of miR-150 increased C5b-9 deposition (Fig. 4A), and its overexpression reduced

C5b-9 deposition (Fig. 4B). Inhibition of miR-328 reduced C5b-9 deposition (Fig.

4C), whereas its overexpression increased C5b-9 deposition (Fig. 4D). C5b-9

deposition was significantly reduced after miR-616 inhibition and enhanced after

overexpression (Figs. 4E-F).

Proteomic analysis of the differentially expressed proteins

To identify the proteins regulated by miR-150, miR-328, or miR-616 that are

involved in complement resistance, a proteomic analysis was performed on K562 cell

lysates following inhibition of each of these specific microRNAs. The intensity values

of the expressed proteins (7987 proteins, expressed in log2) in K562 cells transfected

with miR-150, miR-328, or miR-616 inhibitor and the control group (five repeats for

each treatment group) were compared. T-test analysis showed that considerably more

proteins were up- or downregulated by miR-616 inhibition (285 proteins) than by

miR-150 (125 proteins) or the miR-328 inhibition (162 proteins), and several proteins

were affected by two or three of the microRNA inhibitors (Fig. 5, All differentially

expressed proteins). Mostly, the influence of each microRNA inhibitor on the K562

proteome was unique. Hierarchical clustering of the proteins differentially expressed

after inhibition of miR-150, miR-328, or miR-616 (each relative to the control)

demonstrated that inhibition of each of the microRNAs resulted in a similar number of

up- and downregulated proteins and that the five experimental replicates of each

treatment were homogenous (Supplementary Fig. S2).

Effect of the microRNAs on the expression of membrane complement regulators

The observed impact of miR-150, miR-328, and miR-616 on complement

deposition and cell sensitivity to CDC suggested that one or more of the proteins

regulated by them may modulate cell-complement interactions and resistance to CDC.

The first candidates tested were the complement membrane regulatory proteins CD46,

CD55, and CD59. Both CD46 and CD59 were predicted to be direct targets of miR-

616 (TargetScan). However, a secondary effect through transcription or translation

regulatory factors cannot be ruled out. The proteomic data revealed a significant

increase in CD46 and CD59 expression, but not in CD55, after inhibition of miR-328

(CD46: FC=1.78, P=0.009; CD59: FC=1.47, P=0.01) or miR-616 (CD46: FC=1.97,

P=0.003; CD59: FC=1.67, P=0.001)(Fig. 6A-C). To validate the proteomic findings,

the effect of miR-616 inhibition on complement regulatory protein expression was

further examined. To this end, K562 cells were transfected with a miR-616 inhibitor

or a control plasmid and after 48 hours, were labeled with mouse anti-CD46, anti-

CD55, or anti-CD59 and FITC-conjugated secondary antibody and then analyzed. A

significant CD46 and CD59 upregulation in cells expressing a miR-616 inhibitor was

confirmed by flow cytometry (Fig. 6D). MiR-150 overexpression in K562 cells

significantly increased CD46 and CD55 expression on the cell surface after 24 hours

(Figs. 6E-F), but had no effect on CD59 expression (Fig. 6G).

Enrichment analysis of the differentially expressed mitochondrial proteins

Analysis of the biological pathways, significantly affected in the list of

proteins up- or downregulated by each microRNA inhibitor was performed by using

DAVID gene ontology enrichment software. Several biological processes (BP) and

molecular functions (MF) were enriched after each microRNA inhibition

(Supplementary Table S2). The three microRNA inhibitors significantly affected the

mitochondrial proteome (GOTERM_CC_ALL: GO:0005739~mitochondrion)(anti-

miR-150: P=0.0099; anti-miR-328: P=0.010; anti-miR-616: P=0.0009). The proteins

involved in the enriched mitochondrial processes identified by DAVID for each

microRNA inhibitor treatment group were combined with additional proteins

identified as mitochondrial in the STRING analysis (https://string-db.org) of the

differentially expressed proteins. The combined lists of postulated mitochondrial

proteins were confirmed as presenting mitochondrial proteins in the GeneCards

database (https://www.genecards.org/)(35). Thus, all together, the number of

mitochondrial proteins found differentially expressed was 15 by anti–miR-150, 20 by

anti–miR-328, and 70 by anti–miR-616 (Fig. 5). Of the upregulated proteins, only

three predicted miR-150 targets, no predicted miR-328 targets, and 23 predicted miR-

616 targets were identified (TargetScan Human; Supplementary Fig. S3). The other

differentially expressed proteins were probably incidental targets of these

microRNAs.

We then analyzed the mitochondrial proteins affected by each microRNA

inhibitor by heatmap and by STRING. Heatmap diagrams (Figs. 7A,C,E) show the

segregation of the differentially expressed mitochondrial proteins into two groups

based on treatment (control vs. microRNA inhibitor). The corresponding STRING

diagrams (Figs. 7B,D,F, respectively) provide insight into protein-protein interactions

and the top enriched functions of the differentially expressed mitochondrial proteins.

MiR-150 inhibition affected proteins involved in catalytic activity, organonitrogen

compound metabolic processes and oxidoreductase activity. MiR-328 inhibition

affected proteins involved in small-molecule metabolic processes, substrate-specific

transmembrane transporter activity and organonitrogen compound metabolic

processes. MiR-616 inhibitions affected proteins involved in catalytic activity,

oxidation-reduction, and organonitrogen compound metabolic processes.

Discussion

MicroRNAs have been studied extensively and have been shown to regulate

various cell functions and to be involved in pathogenesis (36,37). Alterations in

microRNA expression are detected in many cancer types and are implicated in cancer

progression (17-19). This study demonstrated the extensive involvement of

microRNAs specifically in cell resistance to complement-dependent cytotoxicity. We

postulated that alterations in microRNA expression, which arise during tumor

progression, may also account for the complement resistance of cancer cells. The

results shown here indicated an association between the basal complement resistance

of cancer cells and their global microRNA expression profile. Comparison of

microRNA profiles between parental K562, Raji, and HCT-116 cells and their

selected variant cells, which express increased resistance to CDC, indicated an altered

expression of multiple microRNAs. Of those, 24 microRNAs were common to the

three cell types, and 19 microRNAs were upregulated and five were downregulated

compared to control. Based on the validation results, the anticipated target genes for

each microRNA, and the initial testing of their relevance to CDC, we focused on three

of the 24 common microRNAs: miR-150, miR-328, and miR-616.

Deregulated expression of miR-150 is seen in juvenile myelomonocytic

leukemia and may play a role in the pathogenesis of this disorder (38). Reduced miR-

150-5p expression may contribute to cholangiocarcinoma development and

progression (39). MiR-150 is also suggested to regulate ovarian cancer cell

malignancy (40), and it suppresses colorectal cancer and hepatoma cell migration and

invasion (41,42) and glioma cell proliferation and migration (43). Upregulation of

miR-328 sensitizes non-small cell lung cancer to radiotherapy (44). MiR-328 is also

found to suppress the survival of esophageal cancer cells (45), promote glioma cell

invasion (46), and regulate cancer stem cell-like cells in colorectal cancer (47). MiR-

616 is upregulated in hepatocellular carcinoma (HCC) and is associated with tumor

recurrence and metastasis (48). miR-616 also can potentiate the migration, invasion,

and the epithelial-mesenchymal transition (EMT) phenotype of HCC cells in addition

to also inducing androgen-independent growth of prostate cancer cells (49).

Sensitivity to CDC was examined in cells where microRNA activity was

blocked with specific microRNA inhibitors, as well as with cells overexpressing the

microRNAs. Our results indicated that inhibition of miR-150 increases cell sensitivity

to CDC, whereas inhibition of miR-328 or miR-616 inhibits it. Inversely,

overexpression of miR-150 inhibits cell sensitivity to CDC, whereas overexpression

of miR-328 or miR-616 increases it. Thus, we claim that miR-150 exerts a net

protective impact on CDC, whereas the net impact of miR-328 and miR-616 is the

promotion of CDC. However, the findings of the miRNA array analysis of the

selected, relatively resistant cells challenged this claim. An explanation to this

discrepancy is that the overall miRNome landscape of the complement

selected/resistant cells was vastly modified relative to that of the parental/sensitive

cells. This had developed following prolonged exposure to CDC, once the cells

underwent significant modifications and adaptations to this selective stress. In contrast

to cells undergoing a prolonged selection, cells transfected with a miRNA inhibitor or

an overexpression plasmid underwent microRNA-specific and transient changes,

lasting 24-48 hours, before they were examined for CDC sensitivity. Thus, the

microRNA array analysis showed the net effect of sensitivity-conferring and

resistance-conferring microRNAs, whereas the transient transfection experiments

showed the specific effect of miR-150, miR-328, and miR-616.

Upon treatment with a sublytic dose of complement, cells also increased their

expression miR-150, miR-328, and miR-616. These effects were observed shortly

after treatment (within 1 hour), suggesting a rapid induction of microRNA synthesis

upon triggering by sublytic complement. Sublytic complement can also upregulate the

expression of miR-200b and miR-200c (24). The downstream effects of the

modulated expression of microRNAs awaits further investigation. However, sublytic

complement is known to modify cells in various ways. At non-lytic or sublytic doses,

the C5b-9 complexes are known to activate several intracellular signals and cascades,

such as PKC, ERK, NF-κB, RIPK1, and RIPK3, in K562 cells (50-53). Sublytic

complement induces the secretion of pro-inflammatory cytokines (54), the expression

of adhesion molecules (55), and activation of the inflammasome (56), and is also

shown to enhance the resistance of K562 cells to CDC (34), to perforin toxicity (57),

and to TNF-induced apoptosis (51). It remains to be determined whether these

microRNAs, upon their upregulation by sublytic complement treatment, further

regulate the various cellular activities through their specific target genes.

Two essential steps in complement-dependent cytotoxicity are (a) deposition

of C3 on the surface of the activating cells, and (b) the assembly and insertion of C5b-

9 complexes into the plasma membrane of the target cells (58). The observed effects

of microRNA inhibition and microRNA overexpression on CDC may reflect modified

activation of C3 and/or C5b-9. This was found to be partly correct. MiR-150

inhibition upregulated C3 and C5b-9 deposition, whereas miR-150 overexpression

reduced it. MiR-328 inhibition reduced C5b-9 deposition but not C3 deposition,

whereas miR-328 overexpression enhanced C3 and C5b-9 deposition. MiR-616

inhibition reduced C5b-9 deposition but had no apparent effect on C3 deposition,

whereas miR-616 overexpression enhanced C5b-9 deposition but not C3 deposition.

Because each microRNA can affect, directly and indirectly, the expression of

hundreds or thousands of proteins (10,11), we analyzed which proteins were regulated

in K562 cells by miR-150, miR-328, and miR-616 and determined whether they

affected cell sensitivity to CDC. The proteomic analysis revealed that the expression

of numerous proteins was modified in K562 cells after inhibition of the microRNAs.

Many differentially expressed proteins resulted from inhibiting two of the three

microRNAs and 15 proteins were affected by three microRNA inhibitors.

Most cancer cells overexpress membrane complement regulatory proteins (6).

The membrane complement regulatory proteins CD46, CD55, and CD59 represent the

first line of protection from CDC and by far, they are the most studied cellular

inhibitors of CDC (1,2). CD46 and CD59 were indicated by TargetScan as potential

targets of miR-616. Examination of the proteomic profile of K562 cells subjected to

microRNA inhibition revealed an increase in CD46 and CD59 expression after

inhibition of miR-328 or miR-616, but no effect on CD55. This was confirmed for

miR-616-inhibited cells by flow cytometry but not for miR-328-inhibited cells.

Further analysis by flow cytometry revealed that overexpression of miR-150

increased CD46 and CD55 expression but not CD59 expression. Thus, to some extent,

these microRNA-induced changes in the expression of CD46 and CD59, especially

when both were affected, may account for the observed changes in C3 and C5b-9

deposition and in cell death (6). PIGA (phosphatidylinositol N-

acetylglucosaminyltransferase subunit A), a predicted target of miR-616 was also

significantly upregulated after miR-616 inhibition. PIGA is an essential enzyme for

synthesis at the endoplasmic reticulum of the glycosylphosphatidylinositol (GPI)

required for anchoring CD59 and CD55 to the cell surface (59). Hence, upregulation

of PIGA expression may contribute, at least to some extent, to the upregulated

expression of CD59 recorded in cells with inhibited miR-616. Based on our findings,

overexpression of complement regulatory proteins may result from reduced

expression of miR-616 or elevated expression of miR-150. Previously, we showed

that miR-200b/c upregulates CD55 expression (24). In breast cancer cells, CD46 is

elevated, and C3 deposition is inhibited upon reduced expression of miR520b and

miR520e (60). MiR-19a and miR-20a regulate CD46 expression in endothelial cells

(61). It is therefore envisaged that microRNA-targeted therapy, in cancer or other

diseases, is expected to have an impact on the expression of membrane complement

regulators and, thus, on the resistance of diseased cells to CDC.

Enrichment and functional analyses of the proteins differentially expressed

after microRNA inhibition indicated alterations in several organellar and functional

pathways that could be relevant in cell sensitivity to CDC. Two mitochondrial

proteins, MSRA (methionine sulfoxide reductase A) and MT-CO3 (mitochondrially

encoded cytochrome C oxidase III), were affected by the three microRNA inhibitors.

MSRA functions in the repair of oxidatively damaged proteins and protects cells from

reactive oxygen species (ROS) (62). It also enhances the activity of complex IV of the

respiratory chain and increases mitochondrial ATP synthesis (63). MT-CO3 is a

subunit of the mitochondrial respiratory chain complex IV that catalyzes electron

transport from cytochrome c to oxygen and the pumping of protons, thus, supporting

oxidative phosphorylation and ATP synthase activity (64). The mitochondrion is

considered to be the primary target of the death signal inflicted by the complement

C5b-9 complexes (65). Hence, modulation of mitochondrial protein expression and

mitochondrial metabolic activities are expected to affect cell sensitivity to CDC. It

remains to be further investigated which of the numerous differentially expressed

mitochondrial proteins are indeed protective from CDC.

Whether or not the CDC-regulating microRNAs miR-150, miR-328, and miR-

616 participate in cancer cell resistance to other non-complement stresses awaits

further investigation. However, based on their observed targets in the mitochondria,

we hypothesize that they will significantly affect other cell responses to oxidative

stress and toxicity. As discussed above, the mitochondrial proteins MSRA and MT-

CO3 were upregulated upon inhibition of miR-150, miR-328 and miR-616. Based on

their functions (62-64), together with the other mitochondrial proteins identified by

STRING, they are expected to protect cells from oxidative stress. Cytotoxic

lymphocytes damage the mitochondrial outer membrane via granzyme B, causing

cytochrome C release and apoptosis (66). Granule-mediated cytotoxicity by cytotoxic

T lymphocytes (CTLs) and NK cells induces mitochondrial damage via caspase-

dependent and caspase-independent mechanisms, causing disruption of the

mitochondrial transmembrane potential and the generation of ROS, which are

required for the induction of cell death (67,68). NK cells are shown to induce

apoptosis in eosinophils through ROS generation, a process inhibited by

mitochondrial inhibitors (69). Regulated necrosis induced by TNF also targets the

mitochondria and causes ROS accumulation (70). Based on the above, it is reasonable

to assume that the observed modulation by miR-150, miR-328, and miR-616 on the

expression of mitochondrial resilient proteins will also affect CTL and NK cell-

mediated cytotoxicity. Two complement membrane regulators, CD55 (71,72) and

CD59 (73), inhibit NK-mediated cytotoxicity. As shown here, miR-150 upregulated

CD55 expression, and miR-616 downregulated CD59 expression. MiR-200b/c was

also shown to upregulate CD55 expression (24). Thus, miR-150, miR-200, and miR-

616 are expected to affect NK-mediated cytotoxicity via CD55 and/or CD59

regulation.

In conclusion, through reorganization of the cell proteome, miR-150, miR-

328, and miR-616 were shown to regulate the assembly and/or stability of the C5b-9

complex and the durability of cells attacked by C5b-9. The influence of microRNAs

on CDC was reciprocal. Cell triggering with a sublytic dose of complement induced

upregulation of miR-150, miR-328, and miR-616 expression. Thus, following

membrane insertion of C5b-9, the balance between cell survival and death was shifted

according to the relative expression of miR-150, miR-328, and miR-616. Enforced

modulation of the expression of these microRNAs may be considered as a means to

increase cancer cells’ sensitivity to CDC during cancer therapy or to reduce tissue

sensitivity to CDC before transplantation or during autoimmune and inflammatory

reactions.

Authors Contributions

YH and ZF designed the experiments and wrote the paper; YH and MM conducted

the experiments; LZ assisted in performing several of the experiments; MP-C

performed the bioinformatic analysis; TG supervised the proteomic analysis; NS

supervised the microRNA array analysis; MP-C, TG, MM and NS critically reviewed

the paper.

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Legends to Figures

Figure 1. Analysis of microRNAs in complement-resistant and complement-

sensitive cells. (A) K562, (B) Raji, and (C) HCT-116 cells were treated with rabbit

polyclonal antibody for 30 minutes at 4°C and then with complement (normal human

serum, NHS) [50%]) for 1 hour at 37°C, and the surviving cells were re-grown and

further subjected to several cycles of these treatments (10 cycles for K562 cells, 8

cycles for Raji cells and 5 cycles for HCT-116 cells). Sensitivity of the selected,

relatively resistant cells and control non-treated cells (NT) to antibody and

complement was compared. Percentage cell death (Mean± SD) was determined by

propidium iodide inclusion. **P <0.01 relative to NT cells (student's t-test). (D)

Identification of differentially expressed microRNAs via a microRNA array. The

quantity of microRNAs in complement-resistant and non-treated K562, Raji, and

HCT-116 cells was examined, as described in Methods. The number of microRNAs

either upregulated or downregulated (resistant/NT) is presented in a Venn diagram;

cutoff: 0.67>fold change>1.5 and P<0.05 (student's t-test). The 19 microRNAs that

were upregulated and the five microRNAs that were downregulated in the three types

of resistant cells are listed. (E) The fold-change in the expression of 14 microRNAs

up- or downregulated in the three resistant cell types, relative to parental cells, is

shown. (F) Validation of the microRNA data. The DNA samples used for the

microRNA array analysis were further subjected to quantitative RT-PCR. The fold-

change in the expression of the same 14 microRNAs in resistant/NT cells is shown.

Figure 2. Modulation of microRNA expression affects cell sensitivity to CDC. (A)

K562 cells were transfected with microRNA inhibitors specific for the indicated

microRNAs or with a non-specific oligonucleotide (C) as a negative control. After 48

hours, the cells were treated with rabbit polyclonal antibody for 30 minutes at 4°C and

then with NHS (50%) for 1 hour at 37°C and the percentage of cell death (Mean±SD)

was determined by PI inclusion. *P<0.05, relative to control (student's t-test). (B-G)

K562 cells were transfected either with a microRNA inhibitor plasmid (expressing

mCherry) for (B-D) 48 hours or (E-G) a microRNA expression plasmid (expressing

GFP) for 24 hours or (B-G) a control (C) plasmid. The cells were then treated with

antibody and NHS, as described above, and the percentage of cell death (Mean±SD)

was measured by (B-D) DAPI inclusion or (E-G) by PI inclusion. *, P<0.05, **,

P<0.01 relative to the control (student's t-test). (H-I) Sublytic complement effects on

expression of miR-150, miR-328, and miR-616. (H) K562 or (I) HCT-116 cells were

incubated with a sublytic dose of antibody for 30 minutes at 4˚C and then with HIS or

NHS for 1 hour at 37˚C, and the cells were cultured further at 37˚C. RNA was

extracted from the cells at 1 hour or 4 hours after treatment, reverse transcribed, and

subjected to quantitative RT-PCR. The fold-change in the expression of miR-150,

miR-328, and miR-616 in cells treated with NHS in comparison with their expression

in cells treated with HIS were calculated ((Mean±SD; representative of 3 independent

experiments; Line drawn at Relative Quantification=1).

Figure 3. Modulation of miR-150, miR-328, and miR-616 and complement C3

deposition. (A,C,E) K562 cells were transfected with a microRNA inhibitor plasmid

for 48 hours, (B,D,F) a microRNA expression plasmid for 24 hours, or (A-F) a control

plasmid. Then, the cells were treated with a sublytic dose of rabbit anti-K562 antibody

for 30 minutes at 4˚C followed by NHS (50%) for 10 minutes at 37˚C. The cells were

then labeled with goat anti-C3 antibody and a fluorescently labeled secondary

antibody and subsequently analyzed by flow cytometry. Mean fluorescence intensity

(MFI) values of cell-bound C3, representative of three independent experiments, are

shown. *P<0.05 relative to control (Mean±SD, student's t-test). MFI values of cells

labeled with secondary antibody only or control cells treated with antibody and HIS

and labeled for C3 were below 100 and did not differ between the two cell types.

Figure 4. Modulation of miR-150, miR-328, and miR-616 and complement C5b-9

deposition. K562 cells were transfected with (A,C,E) a microRNA inhibitor plasmid

for 48 hours, (B,D,F) a microRNA expression plasmid for 24 hours, or (A-F) a control

minutes at 4˚C, followed by NHS for 10 minutes at 37˚C. The cells were then labeled

with anti-C5b-9 (aE-11) and with a fluorescently labeled secondary antibody and

analyzed by flow cytometry. Mean fluorescence intensity (MFI) values of cell-bound

C5b-9, representative of three independent experiments, are shown. *P<0.05,

**P<0.01 relative to control (Mean±SD).

Figure 5. Proteomic analysis of K562 cells transfected with a microRNA

inhibitor. (Upper Venn diagram) Comparison of all differentially expressed

proteins resulting from each microRNA inhibition vs. control. K562 cells were

transfected with a microRNA inhibitor plasmid or a control plasmid. After 48 hours,

cells lysates were prepared and analyzed as described in Methods. The resulting

peptides were fractioned by HPLC and analyzed in a mass spectrometer. To obtain

quantitative data, a label-free quantification algorithm, integrated into Maxquant, was

used. Proteins whose expression was either upregulated or downregulated upon

inhibition of miR-150, miR-328, or miR-616 compared to control are shown in a

Venn diagram; fold-change >1.5 and P<0.05 (student's t-test). Names of

differentially expressed proteins shared by the three microRNAs are listed. Shared

mitochondrial differentially expressed proteins appear in Bold. (Lower Venn

diagram) Focus on mitochondrial proteins. The differentially expressed proteins that

reside and function in the mitochondria are presented in a Venn diagram; fold-change

difference>1.5 and P<0.05 (student's t-test).

Figure 6. Modulation of the membrane complement regulatory proteins by

microRNAs. (A-C) The expression of (A) CD46, (B) CD55, and (C) CD59 in lysates

of K562 cells transfected with microRNA inhibitors or with control, determined by

the proteomic analysis described in Methods, is shown as the mean±SD of Log2 of

intensity value units of five independent experiments. *P<0.05; **P<0.01, relative to

control (student's t-test). (D) K562 cells were transfected with miR-616 inhibitor

plasmid or with control (C) plasmid as a negative control. After 48 hours, the cells

were labeled with anti-CD46, anti-CD55, or anti-CD59 and then with fluorescently

labeled secondary antibody. Cells were then analyzed by flow cytometry, and the

mean fluorescence intensity (MFI) values, representative of 3 independent

experiments, were determined. The expression of each regulator in anti-miR-616-

treated cells were normalized to their levels in control cells (set as 100). *P<0.05;

**P<0.01 relative to control (student's t-test). (E-G) K562 cells were transfected with

a miR-150 expression plasmid or a control plasmid. After 24 hours, the cells were

labeled with (E) mouse anti-CD46, (F) anti-CD55, or (G) anti-CD59 and fluorescently

labeled secondary antibody. Cells were then analyzed by flow cytometry and MFI

values, representative of 3 independent experiments, were determined. *P<0.05

relative to control (student's t-test).

Figure 7. Proteomic analysis of K562 cells transfected with a microRNA

inhibitor: Focus is on mitochondrial proteins. K562 cells were transfected with a

microRNA inhibitor plasmid or a control plasmid. After 48 hours, cells lysates were

prepared and analyzed as described in Methods. The resulting peptides were

fractioned by HPLC and analyzed in a mass spectrometer. To obtain quantitative data,

a label-free quantification algorithm, integrated into Maxquant, was used.

Mitochondrial proteins whose expression was either upregulated or downregulated

after inhibition of (A-B) miR-150, (C-D) miR-328, or (E-F) miR-616 are shown as

Heatmaps and STRING interactomes, respectively. The color-coded legend for each

STRING interactome represents the major location or function of each protein. Fold-

change >1.5 and P<0.05 (student's t-test).

MicroRNAs affect complement regulator expression and mitochondrial activity to modulate cell resistance to complement-dependent cytotoxicity

Yaron Hillman, Mariya Mardamshina, Metsada Pasmanik-Chor, et al.

Cancer Immunol Res Published OnlineFirst September 19, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/2326-6066.CIR-18-0818

Supplementary Access the most recent supplemental material at: Material http://cancerimmunolres.aacrjournals.org/content/suppl/2019/09/19/2326-6066.CIR-18-0818.D C1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet Manuscript been edited.

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