extfieldsofstudy: Medicine Biology

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The Old and the New: Discovery Proteomics Identifies Putative Novel Seminal Fluid Proteins in Drosophila*

We used discovery, bottom up proteomics to provide the first in-depth accessory gland proteome in D. pseudoobscura,. Computational bioinformatics identified >500 proteins in the secretory pathway of which 163 were annotated as extracellular., and therefore candidate, seminal fluid proteins. We further compared molecular rates of evolution between intra- and extra-cellular proteins, showing a hierarchy of rapid evolution with putative seminal fluid proteins evolving more rapidly than other secreted proteins, and those proteins evolving more rapidly than intra-cellular proteins. Graphical Abstract Highlights First deep proteomic coverage of an accessory gland proteome in Drosophila. Discovery proteomics identified >3000 proteins of the D. pseudoobscura, accessory gland proteome. Identified 132 putative novel seminal fluid proteins in this species. Demonstrated the exoproteome as the most rapidly evolving subcellular component of the proteome. Seminal fluid proteins (SFPs), the nonsperm component of male ejaculates produced by male accessory glands, are viewed as central mediators of reproductive fitness. SFPs effect both male and female post-mating functions and show molecular signatures of rapid adaptive evolution. Although Drosophila melanogaster, is the dominant insect model for understanding SFP evolution, understanding of SFP evolutionary causes and consequences require additional comparative analyses of close and distantly related taxa. Although SFP identification was historically challenging, advances in label-free quantitative proteomics expands the scope of studying other systems to further advance the field. Focused studies of SFPs has so far overlooked the proteomes of male reproductive glands and their inherent complex protein networks for which there is little information on the overall signals of molecular evolution. Here we applied label-free quantitative proteomics to identify the accessory gland proteome and secretome in Drosophila pseudoobscura,, a close relative of D. melanogaster,, and use the dataset to identify both known and putative novel SFPs. Using this approach, we identified 163 putative SFPs, 32% of which overlapped with previously identified D. melanogaster, SFPs and show that SFPs with known extracellular annotation evolve more rapidly than other proteins produced by or contained within the accessory gland. Our results will further the understanding of the evolution of SFPs and the underlying male accessory gland proteins that mediate reproductive fitness of the sexes.

In Brief
We used discovery, bottom up proteomics to provide the first in-depth accessory gland proteome in D. pseudoobscura. Computational bioinformatics identified Ͼ500 proteins in the secretory pathway of which 163 were annotated as extracellular., and therefore candidate, seminal fluid proteins. We further compared molecular rates of evolution between intra-and extracellular proteins, showing a hierarchy of rapid evolution with putative seminal fluid proteins evolving more rapidly than other secreted proteins, and those proteins evolving more rapidly than intra-cellular proteins.

Graphical Abstract
Seminal fluid proteins (SFPs), the nonsperm component of male ejaculates produced by male accessory glands, are viewed as central mediators of reproductive fitness. SFPs effect both male and female post-mating functions and show molecular signatures of rapid adaptive evolution. Although Drosophila melanogaster is the dominant insect model for understanding SFP evolution, understanding of SFP evolutionary causes and consequences require additional comparative analyses of close and distantly related taxa. Although SFP identification was historically challenging, advances in label-free quantitative proteomics expands the scope of studying other systems to further advance the field. Focused studies of SFPs has so far overlooked the proteomes of male reproductive glands and their inherent complex protein networks for which there is little information on the overall signals of molecular evolution. Here we applied label-free quantitative proteomics to identify the accessory gland proteome and secretome in Drosophila pseudoobscura, a close relative of D. melanogaster, and use the dataset to identify both known and putative novel SFPs. Using this approach, we identified 163 putative SFPs, 32% of which overlapped with previously identified D. melanogaster SFPs and show that SFPs with known extracellular annotation evolve more rapidly than other proteins produced by or contained within the accessory gland. Our results will further the understanding of the evolution of SFPs and the underlying male accessory gland proteins that mediate reproductive fitness of the sexes. Male ejaculates typically consist of a sperm component and a nonsperm component, both of which are transferred to females during mating. The nonsperm component is seminal fluid, containing secreted peptides and proteins (SFPs) 1 , typically produced in the testes and specialized male exocrine glands (1,2). SFPs have profound effects on both male and female reproductive fitness (3) and therefore significant attention has been focused on the role of SFPs in polyandrous species. Polyandry, where females mate with different males across a reproductive bout generating postcopulatory sexual selection, results in ejaculates that compete for fertilization of a limited supply of ova, and females may choose whose sperm will fertilize those limited ova (4). Polyandry also engenders sexual conflict, in which male and female reproductive interests differ, because of the disproportionate costs and benefits of mating between the sexes (5). In internally fertilizing species, postcopulatory sexual selection operates between the male ejaculate that is transferred to and stored in the female reproductive tract (6). SFPs in these species may increase female fecundity, reduce female receptivity, decrease female life span, alter female hunger, and remodel female reproductive tract morphology (2,3,7,8).
SFPs were first identified by their canonical signal peptide sequence that direct proteins to the secretory pathway (2). Cross-species comparative work has found that general classes of SFPs are conserved (e.g. proteases and protease inhibitors, lectins and prohormones) suggesting that their mechanisms of action are also conserved. However, individual SFPs can rapidly evolve with signals of accelerated rates of adaptive molecular evolution found in studies of coding sequence and male-biased gene expression observed across different animal taxa (e.g. mammals (9, 10); birds (11); Drosophila (12)(13)(14)). Sex-biased genes in general show faster rates of sequence and expression divergence that is consistent with predictions from sexual selection (e.g. (15) but see (11)).
Despite these general patterns, there are limitations to understanding the evolution of SFPs and their function. For example, SFP identification and their role in influencing fitness is dominated by work in D. melanogaster. This species is relatively highly polyandrous (16) and studies identifying SFPs in species with different mating systems is necessary to un-derstand the evolution of reproductive proteins and their fitness consequences. The advent of high throughput proteomics using LC-MS/MS should allow identification of SFPs, even in nonmodel organisms although tests of adaptive evolution may be restricted (17).
Moreover, SFPs function in a complex network of proteintissue interactions (1,2)). However, the general focus on SFPs (a small subset of the accessory gland proteome) leaves open questions about the full complexity of the accessory gland proteome that supports the production of these critical reproductive proteins. Further, the larger role other accessory gland proteins play in postcopulatory sexual selection, and how the accessory gland proteome responds to such selection in toto has been relatively ignored. For example, despite D. melanogaster being a model system for this work, there is but a single study of its accessory gland proteome which is based on 2D gel electrophoresis (18). The recent advent of high throughput proteomics using LC-MS/MS should allow identification of not only SFPs but of the supporting proteins in the male accessory reproductive tissues. A proteomic study of the oriental fruit fly, Bactrocera dorsalis, identified ϳ3000 male accessory gland proteins by LC-MS/MS (19) but focused only on the proteins with identified signal sequences and did not further study the entire proteome. Although a recent study used LC-MS/MS to determine both the male and female accessory gland proteome of the silk worm, Bombyx mori, no tests of molecular evolution of these proteins were performed (20). Previous studies have shown that the subcellular localization of a protein is a strong predictor of its evolutionary rate, and that extracellular proteins secreted from the cell evolve faster than intracellular proteins (21)(22)(23). Whether this pattern is observed in the male accessory gland requires testing.
Here we aimed to address these limitations by using LC-MS/MS to characterize the accessory gland proteome of Drosophila pseudoobscura, whose mating system, although displaying lower levels of polyandry than the model species D. melanogaster, has nonetheless proven useful for experimental evolution studies documenting rapid sex-specific responses to across-generation changes of the mating system (24,25). Our comparative study also takes advantage the extensive genetic knowledgebase available in Drosophila melanogaster and on SFP functional genomics and evolution. We further characterize the D. pseuddobscura accessory gland proteome by constructing, using bioinformatics and gene ontology (GO), an accessory gland secretome (AcgS), exoproteome (exoP) and candidate SFPs. Finally, we compare rates of molecular evolution between these proteome subcomponents to test how subcellular protein localization impacts evolutionary rates in this system EXPERIMENTAL PROCEDURES Experimental Design and Statistical Rationale-The experimental design included D. pseudoobscura selection lines derived from a naturally polyandrous line as previously described (25, see also Experimental Lines section below). Four replicates of each of two selection lines were used for the LFQ studies which provided the necessary replicative power for high value protein identifications and the statistical power needed for downstream GO category enrichment. We employed standard hypergeometric statistical tests as implemented in Cytoscape v3.7.1 (33,34) and visualized using the Cluego plug-in app (35,36).
Stock and Fly Maintenance-We used experimentally evolved sexual selection lines in which the opportunity for post-copulatory sexual selection was either eliminated or facilitated. The establishment and maintenance of the selection lines were previously described in detail (25). Briefly, an ancestral wild-caught population of D. pseudoobscura from Tucson AZ, a naturally polyandrous species (wild caught females have been shown to be frequently inseminated by at least two males at any given time; (26)), was used to establish the selection lines. From this population, four replicate lines (replicates 1-4) of two different sexual selection treatments were established. To modify the opportunity for sexual selection, adult sex ratio in vials was manipulated by either confining one female with a single male which enforces monogamy ("monogamy" treatment, M) and eliminates postcopulatory sexual selection and sexual conflict or one female with six males promoting polyandry ("polyandry" treatment, P (NB: this treatment has also been referred to as E in other publications). Effective population sizes are equalized between the treatments as described previously (27). At each generation, offspring are collected and pooled together for each replicate line, and a random sample from this pool is used to constitute the next generation in the appropriate sex ratios, thus proportionally reflecting the differential offspring production across families. In total, eight selection lines (M1, M2, M3, M4 and P1, P2, P3, P4) are maintained, in standard vials (2.5 ϫ 80 mm), with a generation time of 28 days. All populations are kept at 22°C on a 12L:12D cycle, with standard food media and added live yeast.
Sample Preparation-Flies from replicates 1-4 of each of the selection lines were collected from generations 157, 156, 155, and 153 respectively. We standardized for maternal and larval environments (25), but in brief, parental flies were collected and housed in food bottles, then groups of about 30 were transferred on egg laying plates for 24 h, removed and replaced with a fresh egg plate. This second plate was removed after 24 h, then 48 h later, first instar larvae were collected in groups of 100 and housed in standard molasses/agar food vials at 22°C. Males from these vials were collected on the day of eclosion and housed in vials of 10 individuals, until they were sexually mature (28), and then dissected when they were reproductively mature at 5 or 6 days old.
Using a Leica stereomicroscope, reproductively mature males were ether anesthetized and accessory glands dissected into a drop of PBS using fine dissection needles (supplemental Fig. S1A, S1B). Each accessory gland pair was moved to a fresh drop of PBS and then into a microcentrifuge tube containing 0.5 ml PBS at 4°C until a total of 30 accessory gland pairs per replicate were acquired. Samples were then stored at Ϫ80 following a brief centrifugation. Each tube containing 30 accessory gland pairs was subsequently thawed on ice and pelleted at 20,000 rpm at 4°C for 5 min, supernatants removed, and proteins extracted by addition of 30 l of RIPA buffer (Sigma), containing HALT protease inhibitor mixture and phenylmethylsulfonyl fluoride as per manufacturer instructions (Thermo Fisher Scientific). Samples were then taken through three freeze/thaw cycles using dry ice then thawed at 37°C for 30 s, vortexed then centrifuged at 20,000 rpm for 5 min at 4°C to remove insoluble tissue SDS-PAGE and In-gel Digestion of Proteins-Protein concentrations of the samples described above were determined using a Bradford assay followed by the addition of SDS sample buffer containing 10 mM dithiothreitol. Samples containing 50 g/lane were then loaded onto 4 -12% SDS-PAGE gels and electrophoresed as per manufacturer instructions (Invitrogen). Protein bands were visualized (supplemental Fig. S1C) using Brilliant Blue G Colloidal Concentrate (Sigma). Each gel lane was manually cut into 33-36 pieces of approximately equivalent size and destained using 200 mM ammonium bicarbonate and 40% acetonitrile. Gel pieces were then reduced in 200 l of a 50 mM ammonium bicarbonate buffer containing 10 mM dithiothreitol, followed by alkylation in a similar volume of a 50 mM ammonium bicarbonate containing 55 mM iodoacetamide. Gel pieces were then centrifuged at 13 Kg for 10 s and dried using a vacuum concentrator until all samples were dry (ϳ30 min). The dried pieces were then hydrated in a solution containing 20 l of trypsin (New England BioLabs) and 50 l of acetonitrile and incubated overnight at 37°C. Peptides were extracted the following day using a standard method with a solution of 100% acetonitrile and 5% formic acid and dried down overnight in a vacuum concentrator at 30°C. Resulting peptides were resuspended in 7.5 l of 0.1% (v/v) formic acid, 3% (v/v) acetonitrile, sonicated in a water bath for 5 min and centrifuged at 13 ϫ g for 10 s, before being transferred to a sample vial and loaded into the autosampler tray of the Dionex Ultimate 3000 HPLC system. Samples were set to run using the Xcalibur sequence system.

Liquid Chromatography-MS/MS (LC-MS/MS) Data
Collection-All MS data were collected using an LTQ Orbitrap Elite hybrid mass spectrometer (Thermo Fisher Scientific) equipped with an Easy-Spray (Thermo Fisher Scientific) ion source. Peptides were separated using an Ultimate 3000 Nano LC System (Dionex). Peptides were desalted on-line using a capillary trap column (Acclaim Pepmap100, 100 m, 75 m ϫ 2 cm, C18, 5 m; Thermo Fisher Scientific) and then separated using 60 min reverse phase gradient (3-40% acetonitrile/ 0.1% formic acid) on an Acclaim PepMap100 RSLC C18 analytical column (2 m, 75 m id ϫ 10 cm; Thermo Fisher Scientific) with a flow rate of 0.25 l/min. The mass spectrometer was operated in standard data dependent acquisition mode controlled by Xcalibur 2.2. The instrument was operated with a cycle of one MS (in the Orbitrap) acquired at a resolution of 60,000 at m/z 400, with the top 20 most abundant multiply-charged (2ϩ and higher) ions in a given chromatographic window further subjected to CID fragmentation in the linear ion trap. An FTMS target value of 1e6 and an ion trap MSn target value of 10,000 were used. Dynamic exclusion was enabled with a repeat duration of 30 s with an exclusion list of 500 and exclusion duration of 30 s.
Database Construction of the D. pseudoobscura Accessory Gland Proteome (AcgP)-To obtain the deepest possible coverage of the proteome, we combined the global proteomic data set of all eight replicates and searched mass spectra data files using Sequest HT within the Proteome Discover suite (Thermo Fisher Scientific, San Jose, CA; version 1.4.1.14) using Drosophila pseudoobscura pseudoobscura fasta file (Uniprot UP000001819, 20,816 entries December, 2015 release).
Peptide matches were further analyzed and validated within Scaffold Qϩ (Proteome Software; version 3.2.0) using X!Tandem. Sequest HT and X!Tandem searches were set with a fragment ion mass tolerance of 0.60 Da and a parent ion tolerance of 10.0 parts per million. The oxidation of methionine (15.99), carboxyamidomethyl of cysteine (57.02), and acetyl modification on peptide N terminus (42.01) were set as variable modifications. Files from Sequest HT searches within the same gel lane were merged together as Mudpit using Scaffold, which calculated False Discovery Rates (FDRs) using a reverse concatenated decoy database (FDR was set at 1.0%). Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the PeptideProphet (29) and protein identifications were accepted if they could be established at greater than 99.0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet Algorithm (30). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. The data set was filtered so that every protein must be identified by at least two unique peptides in any one of the biological replicates. Although a conservative approach, this procedure ensured a robust data set devoid of potential misidentifications often caused by use of a single peptide for protein identification. To establish a working list of the AcgP, protein IDs from Scaffold were converted to D. pseudoobscura Fly Base gene numbers (FBgns) using the Uniprot website (Uniprot.org). The resulting D. pseudoobscura FBgns were then used to query Flybase (Flybase.org) to retrieve orthologous D. melanogaster genes from the OrthoDB orthology tables as implemented in Flybase (flybase.org). A complete listing can be found in supplemental Table S1. Note that in all cases, except where noted, only strict 1:1 orthologs were used in this study.
Database Construction of the D. pseudoobscura Accessory Gland Secretome (AcgS) and Exoproteome (exoP)-As a secretory organ, the accessory gland is expected to contain the cellular machinery necessary for efficient and sustained secretory activity throughout the adult reproductive life cycle. To examine and focus on potential activities related to secretion, we assembled an in silico AcgS from the 3281 FBgns of the AcgP as input into Uniprot resulting in 5624 UniProtKB IDs (which includes all predicted protein isoforms). Fasta protein sequence files from each Uniprot entry were downloaded and submitted to SignalP (31) and Phobius (32), using default settings. The protein IDs were combined and exported into Excel yielding a final list of 771 UniProt identifiers. The Uniprot IDs were mapped back to 506 unique D. pseudoobscura FBgns (via Uniprot) which, after submission to OrthoDB (via Flybase) resulted in a final list and 506 D. melanogaster 1:1 orthologs. Candidate SFPs were identified by first querying the list of 515 AcgS genes in Flybase for Gene Ontology (GO) terms containing "extracellular." We also compared this list to RNAseq data of the accessory gland in this population and found that 100% of our AcgS proteins were expressed in the accessory gland (Snook, unpublished data). The resulting list of 163 proteins therefore represents the accessory gland exoproteome (exoP) and is considered to contain a representative sampling of a major fraction of SFPs.
Pathway, GO Enrichment, and Protein Interaction Network Analyses-The finalized data sets were used for downstream bioinformatic analyses and subsequent visualizations of GO enrichment. The protein coding sequences of the AcgP were downloaded from Uniprot and submitted to Blast2go for annotation and tabulation of the three major GO categories, biological process (BP), molecular function (MF) and cellular component (CC). GO enrichment and network visualization and analysis was performed with Cytoscape v3.4 (33,34) and ClueGO plugin version 2.2.4 (35,36). Network parameters used were specific for each dataset as detailed in figure legends and supplemental tables. Protein interaction network analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), a program that calculates the degree of protein-protein network interconnectivity (37).
These two species have the same karyotype as D. pseudoobscura and show reasonable divergence (median pairwise dS ϭ 0.102 versus D. lowei and dS ϭ 0.26 versus D. affinis) thus avoiding substitution rate saturation. To identify orthologs of the identified AcgP D. pseudoobscura proteins in the two other Drosophila genomes we combined two approaches. First we used gene annotation ignoring isoforms specification using only the longest isoform identified to maximize (as these are difficult to identify within a proteomic screen). We then used best BLAST hits (39) of the D. pseudoobscura gene against each of the two other genomes but excluded gene sets for which annotation was contradictory to the D. pseudoobscura annotation. Using a pipeline, we developed earlier (40), sequences were aligned using MUSCLE (41), uncertain sequences filtered out using ZORRO (42) and input files converted with pal2nal (43). Sequences were then analyzed using PAML v4.9 (44) to obtain dN/dS values for each gene set (one-ratio estimates). For the one-ratio estimates, median differences in dN, dS and dN/dS among groups (AcgP, AcgS, exoP) were tested using a nonparametric two-tailed test (45).

RESULTS
The D. pseudoobscura AcgP-A D. pseudoobscura AcgP was constructed from peptide-based ("bottom-up") shotgun MS/MS spectral data obtained from eight independent runs. For the purposes of assembling a proteome with the broadest coverage, data from all runs were pooled together resulting in a total of 3757 UniProt IDs that mapped to 3160 unique FlyBase gene names. Because a latter research goal is to quantify how sexual selection affects the production of proteins in the male accessory gland, here we present the overlap between proteins identified in the four replicates each of M and P selection lines. These data sets were highly correlated with Ͼ90% (3534/3757) overlap (Fig. 1A). Likewise, proteins with values in all four replicates for each selection line represented most identified proteins (M-line 2103/3649; 57.6% and P-line, 2235/3642; 61.4%). A complete listing and tabulation of these results can be found in supplemental Table S1. The small number of proteins unique to each population (Munique ϭ 115; P-unique ϭ 108; Fig. 1A) most likely represent missed protein assignments because of low quantities (as measured by total spectral counts). Indeed, the average total spectral counts for the unique set of proteins (ave. 4.5; n ϭ 223) was 16-fold lower than the average across the entire dataset (ave. 72.8, n ϭ 3874). We do not consider these unique proteins further here and base the remainder of the description of male accessory gland proteins on those that are shared by each treatment.
The D. pseudoobscura AcgS-Signal peptides are a ubiquitous class of short (20 -22 aa) N-terminal sequences, that target proteins for translocation across, and into, the endomembrane system of the cell (46,47). Collectively proteins containing signal peptide sequences are considered part of the secretory pathway, and a subclass-those secreted into the extracellular space-are termed the secretome (also referred to as the exoproteome, exoP). Therefore, some or all components of the exoP can be considered as candidate SFPs. Given the secretory nature of the Drosophila accessory gland, we therefore queried the AcgP for proteins containing canonical signal sequences using two predictive programs, SignalP and Phobius (see Methods). SignalP (31) is a neural network-based algorithm designed to detect canonical N-terminal signal sequences and discriminate against N-terminal transmembrane regions known to reduce predictive power, and Phobius uses a combined model of both transmembrane and signal peptide predictors (32,48). The combined output of both resulted in 771 Uniprot IDs that mapped to 535 D. pseudoobscura FBgns (Fig. 1B; supplemental Table S3). D. melanogaster orthologs (OrthoDB via Flybase) subsequently returned 506 D. melanogaster orthologs to the D. pseudoobscura AcgS including a small percentage (8/511, 1.6%) of "1:many" matches included in the analysis to capture the greatest proteome coverage of the secretome. Thus, the AcgS represents ϳ15% (535/3281; 16.3%) of the entire AcgP consistent with similar calculations for the predicted human secretome (ϳ15%, http://www.proteinatlas.org/ humanproteome/secretome).
Gene Ontology (GO) Functional Analysis of the AcgS-The high degree of orthology between the D. pseudoobscura

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AcgS genes and D. melanogaster (506/528), supplemental Table S3) provided a putative orthologous secretome useful for GO analysis and network visualization. A significant enrichment in BP terms was observed, many related to multicellular organism reproduction (n ϭ 113, p ϭ 7E-7), reproduction (n ϭ 116, p ϭ 7E-7), behavior (n ϭ 53, p ϭ 2.1E-5), and proteolysis (n ϭ 66, p ϭ 3.5E-6). The secretome is enriched in MF terms related to oxidoreductase activity (n ϭ 55, p ϭ 7.1E-7) and hydrolase activity (n ϭ 138, p ϭ 8.5e-14; see supplemental Table S3 for a complete list of all AcgS enriched BP, CC, and MF GO categories). We also examined the subcellular localization of the AcgS using the Cerebral layout tool implemented in Cytoscape. As expected for functions related to secretion and proteins containing signal sequences targeted to the secretory pathway, the predicted subcellular localization of AcgS proteins were skewed toward extracellular and plasma membrane proteins (Fig. 3).
The exoP as a Proxy to Identify Putative SFPs-To identify potential candidate SFPs (essentially the exoP component of the AcgS), we queried the AcgS for the GO CC term "extracellular" and obtained a final list of 163 proteins ( Fig. 1B; supplemental Table S4). This list is a conservative estimate as 85 genes of the AcgS had no GO CC functional annotation and 28/85 were unannotated in all 3 major categories. However, inspection of the BP or MF annotation of the remaining 57 proteins revealed "extracellular" terms, suggesting several possible candidates for inclusion in the exoP including Ͼ20% (13/57) with annotations related to proteolysis and protease inhibitors (supplemental Table S4). We suggest this correlation because SFPs across diverse taxa (see below) contain several proteases, some with critical reproductive functions (13, 49 -51).
In addition to identified exoP proteases, we used Cytoscape and Cluego network analysis of the annotated exoP to return enriched BP terms of major functional categories. These included insemination, sperm competition, copulation reproduction, female mating behavior, and regulation of female receptivity, and response to wounding (Table II;

Novel Drosophila SFP Identification
S28 supplemental Table S4). These are all terms in which D. melanogaster SFPs are known to impact sex-specific fitness. We next compared our putative SFP list with a list of 212 D. melanogaster SFPs assembled from the literature (13,52,53) and found an overlap of 32.1% (68/212), including 32 SFP genes that have been functionally well-studied in D. melanogaster, such as Acp53Ea, Acp53C14d and Acp53C14c (supplemental Table S4). We also found no overlap of the 85 AcgS genes that had no CC annotation with the 212 D. melanogaster SFPs. Likewise, comparison of our putative SFP list to that of 29 D. pseudoobscura SFPs computationally identified from D. melanogaster SFPs (54) found an ϳ50% (14/ 29) overlap.
Molecular Evolutionary Rates of Accessory Gland Protein Genes-We tested for rates of molecular evolution in male reproductive proteins by estimating omega (dN/dS substitution rates) for each set of proteins: candidate SFPs, secretome proteins (minus SFPs), and the remaining accessory gland proteome proteins using orthologs from two closely related species in the obscura group, D. lowei and D. affinis. We found that putative D. pseudoobscura SFPs (Exoproteome) are evolving faster than both accessory gland proteome proteins (AcgP; median omega ϭ 0.088 versus 0.052, p ϭ E-09) and accessory gland secretome minus the Exoproteome (AcgS; median omega ϭ 0.088 versus 0.078, p ϭ 2.7E-02; Fig. 5). AcgS proteins also evolve faster than the AcgP proteins (median omega ϭ 0.052 versus 0.078, p ϭ 7 ϫ 10 Ϫ9 ;  Fig. 5). DISCUSSION We used label-free quantitative proteomics to describe the accessory gland proteome and its subcomponents, including identifying candidate SFPs, in D. pseudoobscura. This species is less polyandrous than D. melanogaster and patterns of evolution therefore may differ, given the role SFPs play in postcopulatory sexual selection. Indeed, the microevolutionary response of sex-biased gene expression to experimental sexual selection in this species is different than that of D. melanogaster (24,55,56). Here we identified 163 proteins that meet many criteria for being putative SFPs, 132 which were previously unknown for this species. GO term enrichment for biological processes for putative SFPs returned terms related to those expected to influence reproductive fitness including sperm competition. We found that only one third of the exoP overlapped with previously described D. melanogaster SFPs. Four obvious but not mutually exclusive possibilities exist to explain the differences in the lists: (1) not all SFPs have yet been discovered in either species, (2) the different SFP discovery methods used in various studies will necessarily result in variable lists, (3) our exoP list contains false positives, and/or (4) although nearly all the proteins in the AcgS, from which we bioinformatically derived the exoP, were homologous with the D. melanogaster genome, some of these proteins may have rapidly diversified to function as SFPs. From a discovery perspective, there is yet no AcgP and AcgS equivalent in D. melanogaster. Such a resource would improve and extend the predictive abilities of identifying putative SFPs in related species and help understand the evolution of this tissue that generates proteins with profound fitness consequences on both sexes. Related to false positives and potential evolutionary recruitment, we computationally derived putative SFPs but reproductive proteins with extracellular function does not necessarily mean they will be transferred to females (64). Future work will require more downstream analyses of these putative SFPs that will also inform about recruitment. Such analyses include testing that these are transferred to females and functionally determining their effect on sexspecific fitness by taking advantage of the published genome of this species and the increasing use of sophisticated gene editing technology in previously nonmodel organisms (e.g. (57)). Putative SFPs that would be good targets for further investigation include the D. pseudoobscura SFPs with protease function that were not identified in D. melanogaster. We argue this because seminal fluid proteases in D. melanogaster are well-known reproductive players, regulating proteolytic and post-mating reproductive processes in a variety of arthropod taxa including Drosophila (13,20,49,50).
In addition to our list of potentially novel candidate SFPs, several D. melanogaster SFPs with known impacts on postcopulatory reproductive fitness were also found. For example, we identified nearly all SFP members of the canonical Sex Peptide network (2,53). In D. melanogaster, Sex Peptide

Molecular & Cellular Proteomics 18.13 S29
bound to sperm is transferred to the female seminal receptacle during copulation and is required for both long-term female resistance to remating and for sperm release from storage (58). We identified the gene duplicate pair lectins CG1652 and CG1656, aquarius (CG14061), intrepid (CG12558), antares (CG30488), seminase (CG10586), CG17575 (a cysteinerich secretory protein), and CG9997 (a serine protease homolog) (59). CG9997 is processed in the female and males that do not produce this protein are unable to transfer the lectins, which are required to slow the rate at which CG9997 is processed in the female. All proteins identified in the D. melanogaster SP network, except SP itself, were detected in our putative list of SFPs. Absence of detectable D. pseudoobscura SP protein is consistent with the lack of a recognized SP ortholog in this species and raises the interesting possibility that either the D. pseudoobscura SP ortholog has significantly diverged, or has been replaced by another gene. If indeed a bona fide D. pseudoobscura SP gene exists, then further MS searches using algorithms to detect amino acid replacements (60) may be useful in the search for this elusive SFP. One aim of this work was to extend the focus from solely SFPs to the functional complexity of other proteins in the accessory gland tissue. We generated a robust accessory gland proteome containing 3160 proteins, representing the FIG. 4. GO network analysis of the exoP. The five major enriched GO BP categories visualized by Cytoscape and Cluego. Processes related to reproduction and insemination are clearly prominent in the network including as are other processes related to the adhesion and extracellular matrix assembly and maintenance. Categories of cellular functions related to wound healing and defense/immunity were also recovered. first accessory gland proteome to be described in Drosophila. 96% of these proteins showed homology to D. melanogaster. The AcgP proteins in D. pseudoobscura were enriched for cellular components expected from a tissue whose primary function is secretory, including several cellular component GO terms related to membranes, extracellular regions, and peptidase complex. The top biological process GO terms clearly indicated a large investment in processes directly and indirectly related to protein synthesis, protein assembly, transport, and secretion. We then in silico concatenated this list to include proteins with secretory signal sequences to identify 506 accessory gland secretome proteins, which were enriched for GO terms related to the biological processes of reproduction, behavior, and proteolysis, with these proteins heavily biased toward subcellular localizations in the plasma member or extracellular components, as predicted from proteins with secretory signals. As previously noted, describing the D. melanogaster AcgP and AcgS, along with other related species, will provide the basis for evolutionary analyses required to understand how selection, particularly arising from postcopulatory selection pressures on males to influence female reproductive fitness, has acted on this tissue.
Documenting the AcgP then allowed testing another aim of our work-determining signals of molecular evolution in different subcomponents of accessory gland proteins. Rapid evolution at the molecular level is common for reproductive proteins including SFPs (e.g. (12,(61)(62)(63)). However, extracellular proteins in general exhibit more rapid molecular evolution than proteins restricted to functions within the cell (21)(22)(23). We therefore compared molecular evolution rates of D. pseudoobscura with its close relatives across proteins from different subcomponents of the male reproductive accessory gland tissue that identified genes encoding SFPs with significantly faster rates of molecular evolution compared with both the AcgS (i.e. other secreted protein encoding genes that were not candidate SFPs) and to non secreted accessory gland proteome genes. Moreover, we also found that secretome genes showed higher rates of molecular evolution than nonsecreted proteins (i.e. the remainder of the AcgP). These results support not only previous work that SFP genes evolve faster than non-SFP genes, but also the more general finding that genes coding proteins which interact extracellularly evolve more rapidly than those that remain within the cytoplasm, irrespective of reproductive function (22,23). CONCLUSION Increasing emphasis on understanding how SFPs impact reproductive fitness across many different organisms requires not only identifying those proteins but also understanding the protein complexity of the SFP-producing tissues. Using organisms with different mating systems and testing the extent to which signatures of rapid molecular evolution are shared across taxa, and across the different environments in which proteins function (i.e. intra-versus extra-cellular), will generate improved understanding of the causes and consequences of SFP evolution. Here we show that the use of label-free quantitative proteomics methods can address such questions and, specifically, will serve as the basis for more detailed work in this species on the role of postcopulatory sexual selection and reproductive protein evolution.
Acknowledgments-We thank the reviewers for their careful and thoughtful comments and criticisms that helped to make this work better. We also thank the many people who have contributed to the maintenance of the Snook experimental evolution lines.

DATA AVAILABILITY
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD012545.  Author contributions: T.L.K., H.S., and R.R.S. designed research; T.L.K. and M.A.R. contributed new reagents/analytic tools; T.L.K., H.S., M.A.R., T.I.G., and R.R.S. analyzed data; T.L.K., H.S., T.I.G., and R.R.S. wrote the paper; H.S. performed research; R.R.S. co-corresponding author.


text:
DDX17 promotes hepatocellular carcinoma progression via inhibiting Klf4 transcriptional activity

DEAD box RNA helicase 17 (DDX17) is a transcriptional regulator of several transcription factors, which is more appreciated than its role in RNA metabolism. However, prognostic value and biofunction of DDX17 in HCC remain unclear. Illuminating the mechanism underlying the regulating HCC progression by DDX17 may contribute to therapeutic strategies. In our study, we report for the first time that DDX17 was overexpressed in HCC specimens by using The Cancer Genome Atlas (TCGA) and immunohistochemistry (IHC) and correlated to clinical pathological characteristics and patients’ survival. In vitro, DDX17 was ascertained to alter HCC migratory and invasive capacities after overexpression and knockdown in HCC cell lines. Moreover, by performing co-immunoprecipitation (Co-IP) and GST-pull down assay, the physical association between DDX17 and Klf4 was discovered and validated. Additionally, DDX17 could modulate expressions of Klf4 target genes including E-cadherin, MMP2 by inhibiting the promoter activity. The potent correlation between DDX17 and Klf4 target gene expressions was further appraised by a same set of 30 HCC tissues. Besides, we discovered that DDX17 could not deploy its function in regulating Klf4 target gene expressions and HCC progression in Klf4-depletion condition. Intriguingly, DDX17 failed to interact with Klf4 once the zinc-finger domain was deleted and inhibited the binding of Klf4 on MMP-2 promoter. Collectively, our study enucleates novel mechanism of DDX17-mediated oncogenesis by suppressing the transcriptional activity of Klf4 thus is likely to be a therapeutic target in HCC.

Introduction
Hepatocellular carcinoma (HCC) ranks as the fifth and seventh most common malignancy globally in men and women, respectively, also is the second leading cause of cancer-related deaths 1 . Besides, almost 80 hundred thousand new cases of HCC occurred in 2018 worldwide, surprisingly among them nearly 79 hundred thousand deaths was caused by HCC 1 , which definitely denotes that the incidence, as well as mortality of HCC have not been decreased although substantial improvement through treatment strategies is achieved. Therefore, pursuing for novel biomarkers with high sensitivity and specificity is so challenging that it requires urgent research.
DDX17 (DEAD Asp-Glu-Ala-Asp) box helicase 17, also known as p72, is a typical member of DEAD box family and it gained attention for its role as a kind of RNA helicases 2 . Besides, it has become increasingly clear that DDX17 acts as a transcription factors (TFs)-associated protein, such as NFAT-5, HDAC1, SOX2, and betacatenin [3][4][5][6] , and exhibits its function in various tumors. Aberrant DDX17 expression was found in colon 7 and lung cancers, which promotes tumor progression 3,7 . However, the landscape of DDX17 in HCC involving its functional role and molecular mechanisms in the context of oncogenesis has never been deciphered.
Krüppel-like factor 4 (Klf4, GKLF) is a well-known TF pivotal for the maintenance of the pluripotency stem cell state in embryonic stem cells (ESC) 8 . Loss of Klf4 expression is well established in different cancers 9,10 . Our previous study confirms that Klf4 is also deregulated in HCC and could regulate numerous cancer cell processes 11,12 . Corroborated evidence suggests the presence of Cys 2 /His 2 zinc-finger motifs in carboxy-terminal domain of Klf4 confers preferential binding to GC/GTrich or CACCC element sequences in its target gene promoter and enhancer regions followed by expression changes of broad array of genes covering cell cycle and proliferation, such as p21, p27, p57 [13][14][15] , and tumor metastasis like matrix metalloproteinase 2 (MMP-2) and E-cadherin 16,17 . In our study, we evaluated the prognostic value of DDX17 in HCC and explored its effect on HCC migration and invasion. Besides, we went deeper insight into the molecular mechanism of DDX17 in HCC progression, which unveiled the interaction between DDX17 and Klf4 in regulating HCC metastasis.

DDX17 is a potential biomarker for HCC according to TCGA
We first utilized TCGA database to access DDX17 mRNA expression and its association with HCC prognosis. As shown in Fig. 1a, DDX17 was remarkedly overexpressed in HCC compared to normal tissue at transcriptional level (p < 0.001). Then, we further evaluated its association with clinical pathological characteristics. As shown in Fig. 1b-e, although there was no significance between DDX17 expression and tumor size and M stage, DDX17 was correlated to tumor stage and lymphatic metastasis. Since the samples of M1 stage, N1 stage, and stage IV from TCGA were small, so that it was not sufficient to demonstrate correlation between DDX17 and stage progression, N metastasis. But potent correlation between DDX17 expression and overall survival of HCC patients was found (Fig. 1f, p = 0.03). Therefore, we assumed that non-significance between DDX17 and tumor size or distant metastasis was probably due to lack of large samples, which requires further research to validate.

DDX17 is upregulated in HCC specimens and predicts poor clinical outcomes in HCC patients
In order to validate that correlation between DDX17 expression and HCC progression, IHC was performed to evaluate the DDX17 protein expression in 105 human HCC specimens and substantially overexpressed DDX17 protein was found in HCC specimens compared to adjacent normal tissue (Table 1, p < 0.001) and results also showed that the expression of DDX17 was increased significantly as HCC progressed to more advanced stage (Fig. 2a, b). Moreover, the association between DDX17 expression and clinical pathological characteristics are shown in Table 2. The over-expression of DDX17 was strikingly correlated to tumor size (p = 0.03), nodal involvement (N stage) (p = 0.019), distant metastasis (M stage) (p = 0.037), tumor differentiation (p = 0.0012), and American Joint Committee on Cancer (AJCC) stage (p < 0.001). However, there were no associations between DDX17 expression and patient age and gender.
Besides, Kaplan-Meier curves with a log-rank test for DFS and OS were performed to elucidate the relationship between DDX17 expression and patients' survival in HCC in TMA with 105 patients. Our results revealed that high expression of DDX17 was associated with shorter DFS compared with lower DDX17 expression (Fig. 2c, p = 0.001). Besides, high expression level of DDX17 was associated with a trend towards poor OS (Fig. 2d, p < 0.001). In addition, further OS analysis was performed according to tumor stage, and results manifested that patients who were in stage I-II or stage III-IV, with higher DDX17 expression had worse outcome than those with lower DDX17 expression (Fig. 2e, f). The obtained results revealed DDX17 was a potential prognostic marker for HCC.

DDX17 promotes HCC migration and invasion in vitro
To explore the effect on HCC migration and invasion, we constructed lentivirus-mediated DDX17 shRNA stable cells including SMMC7721 and HepG2 cells, and DDX17 plasmid was transfected transiently into both cells, which was confirmed by Western blotting (Fig. 3a). Then the migration and invasion assays were performed to investigate whether suppression or upregulation of DDX17 was capable of altering HCC cells' migratory and invasive abilities. As shown in Fig. 3b-d, in DDX17 overexpressedcondition both SMMC7721 and HepG2 cells presented potentiating migratory and invasive capacities remarkedly, which however were blunt strongly after knockdown DDX17 in HCC cell lines.

DDX17 interacts with Klf4 and alters Klf4 target gene expression
Our previous study found loss of Klf4 accelerated HCC progression by activating EMT process via TGF-betasignaling pathway as well as regulation of Vitamin D 11,18 . Besides, previous studies have shown that the transcriptional activity of Klf4 could be regulated by its binding partners 19 . As DDX17 could act as co-activator or coinhibitor of TFs, therefore, we wondered that whether DDX17 could be a regulator of Klf4 transcriptional activities. Therefore, we utilized CO-IP in HEK293T cells and SMMC7721 cells. Results revealed DDX17 and Klf4 were physically associated (Fig. 4a). To further validate their binding, GST-pull down assay was performed using purified GST-PGEX or GST-Klf4, and results showed that GST-Klf4 specifically interacted with DDX17 (Fig. 4b).
Altogether, these results strongly supported the interaction of DDX17 and Klf4. To further investigate whether DDX17 affected the expression of Klf4 target genes responsible for tumor metastasis, we checked the expression of E-cadherin and MMP2, whose promoter or enhancer element is regulated by Klf4 17,20 . As shown in Fig. 4c, E-cadherin displayed lessened protein expression, while the protein expression of MMP-2 was upregulated after DDX17 upregulation. On the contrary, in DDX17delpeted condition, the protein expression of E-cadherin was significantly elevated, while the protein expression of MMP-2 was remarkedly dropped (Fig. 4c). Furthermore, in order to demonstrate whether alteration of Klf4 target genes was ascribed to changes of transcriptional activities, the mRNA expression levels of E-cadherin and MMP-2 were quantified by qRT-PCR. As shown in Fig. 4d, e, the mRNA expression of E-cadherin and MMP-2 presented the same trend as the protein expressions after overexpression and suppression of DDX17 in HCC cell lines. To further explore whether DDX17 directly regulated the promoter activity of Klf4 target genes leading to transcriptional and translational level changes of Klf4dependent genes, we cloned -521 to -136 base pair region of the MMP-2 promoter, which was rich in GT elements requiring Klf4 to bind on the promoter. Results from luciferase report assay revealed that the MMP-2 promoter activity was remarkedly elevated after DDX17 was overexpressed (Fig. 4f). On the contrary, the MMP-2 promoter activity was prominently decreased after knockdown of DDX17 both in HepG2 and SMMC7721 . a Expression level of DDX17 in hepatocellular carcinoma was elevated in HCC tumor tissues compared to normal tissue. b Expression of DDX17 presented more upregulated in T2, T3 stage compared to that in T1. c Expression of DDX17 in HCC patients with N1 stage was remarkedly increased compared that in N0 stage. d There was no significant difference of DDX17 expression between M1 stages and M0 stage in HCC patients. e Expression of DDX17 was elevated gradually as HCC progressed to stage II and stage III (The tumor stage was according to AJCC. T: Tumor, representing the extent of the primary tumor; N: lymph node, representing the presence or absence of regional lymph node metastasis; M: Metastasis, representing the presence or absence of distant metastases). f OS curve of HCC patients based on DDX17 expression (p = 0.03) cell lines (Fig. 4g). Consequently, DDX17 modulated the expression of Klf4-dependent target genes via directly regulating its promoter activity.

DDX17 regulates the expression of E-cadherin and MMP-2 by inducing dissociation of Klf4 on chromatin
To gain deeper mechanism underlying DDX17mediated alteration of E-cadherin and MMP-2 expressions, we depleted the expression of Klf4 by shRNA in HepG2 and SMMC7721 cells, and then performed transient overexpression of DDX17. Results showed that the changes of E-cadherin and MMP-2 expressions in transcriptional and translational levels caused by Klf4 knockdown could not be further altered by DDX17 overexpression (Fig. 5a, b). As shown in Fig. 5c, MMP-2 promoter activity also presented the same trend as the protein and mRNA expressions. This highlighted that DDX17 modulated E-cadherin and MMP-2 expressions via inhibiting Klf4 transcriptional activity. Besides, DDX17 overexpression and Klf4 depletion were both able to enhance migration and invasion of HCC cells, however, DDX17 overexpression was incapable of further enhancing these abilities once Klf4 was knockdown (Fig. 5d). Furthermore, we constructed DDX17 shRNA and DDX17 shRNA + Klf4-shRNA stable HCC cell lines. Results showed loss of DDX17 could not modulate expression and MMP-2 promoter activity when Klf4 is also suppressed (Fig. 5e, f). Besides, knockdown of DDX17 indeed lost its inhibiting role in HCC progression once Klf4 is depleted ( Fig. 5g; Supplementary Fig. 1a). Thus, our data suggested that DDX17 regulated HCC metastasis in a Klf4-dependent manner.
To explore further the relationship between DDX17 and Klf4, we generated Klf4 mutation, whose three zincfingers were mutant (Fig. 6a). Besides, CO-IP was performed in HEK293T cells, and results showed that DDX17 could hardly interact with mutant Klf4, which manifested that DDX17 occupied the zinc-finger domain of Klf4 thus regulated Klf4 target gene expression. Taken together, these data powerfully implicated that DDX17 regulated Klf4-mediated target genes through suppressing zinc-finger activity of Klf4. Since DDX17 was incapable to bind to Klf4 zinc-finger activity and inhibited its transcriptional activity, we wondered whether DDX17 could bind to Klf4 at DNA. Thus, Re-ChIP was performed using MMP2 promoter. However, the Re-ChIP experiment showed that the DDX17 and Klf4 did not co-localize on promoter of MMP2 (Fig. 6b). Moreover, to determine how DDX17 regulated the targets of KLF4 on chromatin, we overexpressed DDX17 in SMMC7721 cells, and determined the binding of KLF4 on the promoter of MMP2. Surprisingly, we found that the KLF4 were highly enriched in the promoter of MMP2, while DDX17 overexpression significantly reduced the binding of KLF4 on the promoter of MMP2 and vice versa (Fig. 6c, d). As a control, we detected the binding of Klf4 on a nonregulated region that was not reduced upon DDX17 silence or overexpression (Fig. 6e, f). Together, these results suggested that the DDX17 directly interact with KLF4 on chromatin and might contribute to the dissociation of KLF4 on chromatin. However, since Klf4 was also able to regulate gene expressions relevant to cell proliferation, CCK-8 and clone formation assay were performed. Results showed that DDX17 knockdown also inhibited HCC cell proliferation ( Supplementary  Fig. 1b-d), and results showed that loss of DDX17 remained weak ability to inhibit HCC cell proliferation although the majority of its ability disappeared in Klf4depletion condition. This suggested that DDX17 inhibits other Klf4-target gene expression by inhibiting Klf4 transcriptional activity, but it is more complicated. Besides, this result implied that DDX17 may regulate Klf4-target gene expression in large scale, which needs further study.

DDX17 is potently correlated with Klf4 target genes existing in HCC human tissues
To determine the clinical correlation of DDX17 with Klf4 target genes, IHC analysis was performed to detect the expression levels of DDX17, E-cadherin, and MMP-2 in the same set of HCC tissue samples. Within our expectation, the expression of DDX17 presented strong correlation with E-cadherin (p = 0.026) and MMP-2 (p = 0.024) according to Spearman's correlation test (Fig. 6g,  h). Henceforth, these data further supported our obtained results in vitro.

Discussion
DDX17 is overexpressed in colon cancer, lung cancer, and glioma cancer, potentiating tumor cell proliferation and progression 3,7,21 . Moreover, DDX17 exerts its powerful oncogenic effect by acting as co-activator or coinhibitor of TF such as estrogen receptor alpha (ERα) 22 . Recently, DDX17 was demonstrated to interact with β-catenin and facilitate its nuclear accumulation, thus activating β-catenin target genes, and ultimately resulting in acquired resistance of non-small cell lung cancer (NSCLC) cells to gefitinib 3 . However, in our study, we firstly reported that DDX17 was upregulated in HCC and was strongly associated with HCC clinical pathological features as well as prognosis in HCC. Thus, DDX17 was an independent prognostic factor for HCC. We also discovered that DDX17 could promote HCC cells' progression by contributing to the migratory and invasive capacities of HCC cells. These data revealed that DDX17 might take part in indispensable signaling, consequently promoting HCC progression.
Then, our CO-IP assay results confirmed the interaction between DDX17 and Klf4 in HEK293T cells as well as in HCC cells, and GST pull down assay further demonstrated this physical interaction. Moreover, during getting deeper insight into the effects of potent interaction between DDX17 and Klf4 in HCC cell lines, we In accordance with that, DDX17 overexpression lost its function in promoting HCC cell lines metastasis once Klf4 was depleted. These results might reveal that the process of recruiting Klf4 to its target gene's promoter by other regulators was prevented by DDX17. Furthermore, multitudinous evidence has proved that DDX17 usually acts as a positive or negative regulator of TF, such as SOX2, β-catenin, whose transcriptional activity is modulated by DDX17, further regulating TF target genes 6 . Thus, we proposed that transcriptional activity of Klf4 was also inhibited by DDX17. In fact, our CO-IP assay revealed that DDX17 failed to interact with Klf4 once its functional domain-zinc-finger domain was totally deleted. Therefore, DDX17 indeed impeded the process of Klf4-regulating promoter activity of its target genes. In addition, our studies found that the KLF4 and DDX17 did not co-localize on the promoter of MMP2, but we observed that DDX17 overexpression decreased the binding of KLF4 on chromatin, suggesting that the DDX17 might contribute to the dissociation of KLF4 on chromatin and vice versa. Indeed, lines of studies observed that there are many proteins that shuttles between nucleus and cytoplasm, involved in the regulation of transcription by disrupting the bindings of TFs on chromatin, this report is the first to demonstrate that DDX17 is bound to Klf4 at chromatin.
However, our study suggested that other Klf4-target genes also may be regulated by DDX17. Suppression of DDX17 barely regulated HCC cell proliferation after Klf4 was depleted. This result indicates that DDX17 may also regulate Klf4-target genes associated with proliferation, thus MMP-2 and E-cadherin are not the genes exclusively regulated by DDX17. Our hypothesis is that DDX17 may Western blotting was used to access DDX17 expression after transfected with DDX17 plasmid or DDX17-shRNA in SMMC7721 and HepG2. c, d The migratory ability in indicated cells was detected by Transwell assay after DDX17 overexpression and knockdown separately. e The invasive ability in indicated cells was detected by Transwell assay after DDX17 overexpression and knockdown separately. *p < 0.05 regulate plenty of Klf4 target genes and can influence the binding of Klf4 on target gene promoter or enhancer. It was noticeable that DDX17 still remained weak ability to promote HCC proliferation. This indicates that Klf4 may be not the only TF that interacts with DDX17. Because DDX17 could act as regulator of many TFs, we assumed that DDX17 may affect transcriptional activities of Klf4 and other transcriptional factors, followed by alteration of a set of their target genes expression and increased cell proliferation. However, whether all of Klf4 target genes are regulated by DDX17 needs further study to validate.
In summary, our study report for the first time that DDX17 acts as a negative regulator of Klf4 transcriptional activity in HCC and is an independent prognostic factor of HCC, which might be a potential therapeutic target. We decipher that DDX17 interacts with Klf4 and inhibits its zinc-finger activity in HCC, further being recruited by Klf4 to MMP-2 promoter and modulating their expressions, ultimately promotes HCC metastasis, which was depicted in Fig. 6i.

Material and methods
Cell culture and transfection HEK293T and HCC cells (HepG2, Hep3B, and SMMC7721) were cultured as previously described 23 . The small hairpin RNA (ShRNA) was constructed using the following primers: DDX17 shRNA1: CCCAATCTGATGTATCAGGAT; DDX17shRNA2: GACCACAAGTTGATCCAACTA; Klf4 shRNA: GCTCCATTACCAAGAGCTCAT. HA-Klf4 and Flag-DDX17 expression plasmids were obtained from Shanghai Asia-Vector Biotechnology. Mutant Klf4 plasmids were constructed by PCR from HA-Klf4. The MMP2 proximal promoter (−521/−136 bp) was amplified by PCR. Transfection and stable cell lines were performed in detail as previously described 23 .

Co-immunoprecipitation (Co-IP) and immunoblotting
HEK293T and SMMC7721 cells maintained in normal growth medium were transfected with Flag-DDX17 or HA-Klf4 plasmid for 48 h. Transfected cells were and SMMC7721 cells. DDX17 and Klf4 could be reciprocally co-immunoprecipitated. b GST-Klf4 fusion protein was incubated with GST-beads and lysates of DDX17-transfected HEK293T cells. Western blotting was performed to probe the interacting DDX17. c Western blotting was performed to detect protein expression of Klf4 target genes including E-cadherin and MMP-2 in DDX17 overexpressed and depleted conditions. d, e Quantitative real-time PCR (qRT-PCR) was used to validate mRNA alterations of E-cadherin and MMP-2 in indicated cells. f, g MMP-2 promoter activity was detected by luciferase report activity in indicated cells. *p < 0.05 harvested and lysed. Then, Flag-M2/HA beads (Sigma-Aldrich) were used to immunoprecipitate with Flag-DDX17 or HA-Klf4, and the precipitate was washed four times using cell lysis buffer. Finally, the proteins were analyzed by Western blotting.
GST pull-down assay GST fusion protein containing pGEX-Klf4 or pGEX vector was induced with 0.1 mM of IPTG (Sigma-Aldrich). GST or GST-Klf4 was purified by GSTrap™ FF (GE Healthcare). Total cell lysates were incubated with GST beads (Biosciences, Sweden) overnight at 4°C followed by four times washing using washing buffer. Finally, proteins were visible by western blot analysis.

Luciferase reporter assay
To construct rDNA promoter-activated luciferase reporter, −521/−136 bp of rDNA promoter (the transcription start site is represented as +1) was subcloned into PGL3. Luciferase reporter assay was performed in SMMC7721 cell and HepG2 cells. Briefly, cells were seeded in 24-well plates and then were co-transfected with indicated plasmids and the rDNA-promoter luciferase reporter plasmid for 48 h. Luciferase was detected using the Dual-Luciferase assay kit (Promega). Renilla was co-transfected to normalize transfection efficiency.

Chromatin immunoprecipitation (ChIP) and Re-ChIP assays
ChIP assay was performed on SMMC7721 cells with and without the indicated treatments using a kit according to the supplier's protocol (ChIP-IT High Sensitivity ® Active Motif). Klf4 was immunoprecipitated using anti-Klf4(Abcam). Pre-immune rabbit serum was used as negative control. For chromatin re-immunoprecipitation (re-ChIP) assay, the chromatin immunoprecipitation was eluted from the first ChIP with 10 mm DTT at 37°C for 30 min and diluted 20 times with ChIP dilution buffer (1% Triton X-100, 2 mm EDTA, 20 mm Tris-HCl, pH 8.1, and 150 mm NaCl) and immunoprecipitated with the DDX17 antibody (Proteintech). The immunoprecipitated DNA was uncross-linked, subjected to proteinase K digestion, and purified using QIAquick columns (Qiagen, Valenica, CA). qPCR was performed to analyze ChIP and re-ChIP samples by using the following primers: forward, 5′-TG GCATAATGATGTGGCTGT-3′, and reverse, 5′-TT GGCTGGAAAAGGTGTAGG-3′ that would amplify the fragment encompassing the Klf4-binding site.

Immunohistochemistry
A total of 105 HCC and paired normal tissues arrays were presented by Dr. Yu. Three continuous TMA with Fig. 6 DDX17 regulates the expression of MMP-2 by inducing dissociation of Klf4 on chromatin and its expression is significantly correlated with E-cadherin and MMP-2 expressions in hepatocellular carcinoma tissues. a Brief description for wild type and mutant type of Klf4 and interaction between wild type/mutant type Klf4 and DDX17 was assessed using CO-IP in HEK293T cells. b Re-ChIP assay showed no binding between DDX17 and Klf4 at MMP-2 promoter. c, d The intensity of Klf4 binding on MMP-2 promoter was detected in DDX17 overexpression and knockdown was detected by ChIP-qPCR. e, f The intensity of Klf4 binding on negative promoter was detected by ChIP-qPCR after overexpression and knockdown of DDX17. g Representative images of immunohistochemical staining of DDX17, E-cadherin, and MMP-3 in tumor tissues. h Spearman's correlation analysis of DDX17 and E-cadherin expression, DDX17 and MMP-2 expression. i Model: Diagrammatical representation, elucidating DDX17mediated suppression of Klf4 transcriptional activity in hepatocellular carcinoma. DDX17 binds to zinc-finger domain of Klf4, subsequently inhibits Klf4 target genes including E-cadherin and MMP-2. Decreased E-cadherin expression and increased MMP-2 expression result in potentiating HCC cell migration and invasion, thus conductive to HCC progression. *p < 0.05 30 cases of HCC specimens were freely donated by Dr. Yu. TMA was finished and relevant proteins were scored as previously described 23 .

Transwell assay
Cells migration and invasion were accessed using transwell filter (Corning, USA) as described previously 23 . In brief, cells were fixed using 4% methyl alcohol for 15 min followed by staining via 5% crystal violet solution for additional 15 min after culturing for 48 h. Five fields of view were randomly selected and photographed using an upright Metallurgical Microscope (Leica, Germany).
Cell counting kit-8 (CCK-8) assay CCK-8 assay was performed to detect cell proliferation. Different groups of cells were plated in 96-well plate with 100 μl medium per well. After culture for 6, 24, 48, 72, 96 h, 120 μl medium containing 10 μl. CCK8 (Dojindo, Japan) was added to each well. Cells were cultured for another 2 h in incubator and then the absorbance was measured at a wavelength of 450 nm.

Colony formation assay
Cells were seeded in six-well plates, with a density of 1 × 10 3 cells per well and then cultured for 14 days. Then, the cells were fixed using 4% methyl alcohol for 15 min and stained with 5% crystal violet solution for additional 15 min. Finally, clones were counted to evaluate cell proliferation.

Statistical analysis
All statistical analyses were implemented with the SPSS 23.0 statistical software package. t-text, chi-square text, and Fisher's exact test were used to analyze in experimental groups. Besides, the Kaplan-Meier test was used to analyze the survival rates. Spearman's correlation test was applied to analyze the correlation. p-value < 0.05 in all cases was considered statistically significant.