extfieldsofstudy: Biology Medicine

text:
De novo DNA demethylation and noncoding transcription define active intergenic regulatory elements

Deep sequencing of mammalian DNA methylomes has uncovered a previously unpredicted number of discrete hypomethylated regions in intergenic space (iHMRs). Here, we combined whole-genome bisulfite sequencing data with extensive gene expression and chromatin-state data to define functional classes of iHMRs, and to reconstruct the dynamics of their establishment in a developmental setting. Comparing HMR profiles in embryonic stem and primary blood cells, we show that iHMRs mark an exclusive subset of active DNase hypersensitive sites (DHS), and that both developmentally constitutive and cell-type-specific iHMRs display chromatin states typical of distinct regulatory elements. We also observe that iHMR changes are more predictive of nearby gene activity than the promoter HMR itself, and that expression of noncoding RNAs within the iHMR accompanies full activation and complete demethylation of mature B cell enhancers. Conserved sequence features corresponding to iHMR transcript start sites, including a discernible TATA motif, suggest a conserved, functional role for transcription in these regions. Similarly, we explored both primate-specific and human population variation at iHMRs, finding that while enhancer iHMRs are more variable in sequence and methylation status than any other functional class, conservation of the TATA box is highly predictive of iHMR maintenance, reflecting the impact of sequence plasticity and transcriptional signals on iHMR establishment. Overall, our analysis allowed us to construct a three-step timeline in which (1) intergenic DHS are pre-established in the stem cell, (2) partial demethylation of blood-specific intergenic DHSs occurs in blood progenitors, and (3) complete iHMR formation and transcription coincide with enhancer activation in lymphoid-specified cells.

Until recently, our knowledge of genome function has been focused on protein-coding genes. Yet, analyses of evolutionary constraint across vertebrates and eutherian mammals reveal millions of bases in the human genome that have undergone purifying selection, of which only a fraction are within known protein-coding sequences (The Encode Project Consortium 2007). Many (;40%) are bundled into conserved units as small as tens of nucleotides that are scattered across vast intergenic space (Lindblad-Toh et al. 2011). Interestingly, most of these do not coincide with sequence features characteristic of protein-coding or structural elements, but instead, suggest a regulatory function based, for example, on an enrichment of transcription factor binding motifs (TFBS).
Combined profiles of modified histones and DNase accessibility have charted chromatin states across diverse cell types from fly (Kharchenko et al. 2011), human , and mouse (Shen et al. 2012). These have exposed numerous putative cisregulatory elements, most notably enhancers and insulators. The activity of such elements is frequently specific to cell type and context, and therefore a synthesis of developmentally diverse, tissue-specific genomic data sets is required to detect and interpret regulatory function.
Enhancers and other distal cis-regulatory elements are transcription-factor (TF) docking sites for long-distance gene regulation. They help to establish alternate gene expression programs that in turn guide cell fate decisions and control cellular phenotypes (Bulger and Groudine 2011). For many TFs, stable occupancy at target sites relies on a DNA sequence free of cytosine methylation and nucleosome interference. Further, recent studies addressing single receptor models propose significant interplay between DNA methylation, transcription factor binding, and the activity of enhancers (Stadler et al. 2011;Wiench et al. 2011).
DNA methylation itself is thought to be a critical component of the mechanisms that define and stabilize cell-type identity and developmental state. In a typical mammalian genome, methylation is the default state, with 70%-80% of all CpG sites modified. Unmethylated CpGs frequently occur in areas of high CpG density, so-called CpG Islands (CGIs), which often overlap gene promoters. Recently, we described an unbiased, empirical model for detecting hypomethylated regions (HMRs) based on clustering of largely unmethylated CpG sites in the genome , independent of predefined CGIs. Using this approach, we identified a new class of intergenic and intronic HMRs (iHMRs) that differ in several ways from those observed near promoters, including size and sequence composition. With few specific exceptions, promoter HMRs (pHMRs), whether coinciding with CGIs or not, are shared (constitutive) across diverse cell types. For this reason, their presence does not specifically flag genes that are transcriptionally active . In contrast, many iHMRs tend to be either stem cell specific or ''de novo'' demethylated in specific differentiated cells. Thus, we sought to understand the role of hypomethylation at these sites in relation to different regulatory activities. In particular, we asked whether iHMRs designate select classes of regulatory elements, and whether their component features are prognostic of gene activity.
Here, we show that differential comparisons of HMR profiles across multiple cell types can provide detailed information about the presence and status of regulatory elements in individual cell types. Using HMR profiles obtained from bisulfite sequencing (BSseq) of five different human and three chimpanzee cell types, we defined sets of constitutive and cell-type-specific iHMRs. The cells represented both embryonic (H1 ESC) (Lister et al. 2009) and adult somatic stem cell stages (hematopoietic stem and progenitor cells, HSPCs) in addition to differentiated states from two divergent hematopoietic lineages (B lymphocytes, the GM12878 lymphoblastoid cell line, and neutrophils) . We superimposed the HMR profiles on available ChIP-seq and DNase-seq data sets from The ENCODE Project Consortium (2012), finding a remarkably precise, highly cell-type-specific overlap between HMR calls and modified histone peaks. This enabled us to clearly distinguish enhancer-, insulator-, and promoter-like iHMRs, and their respective methylation dynamics, during differentiation from stem to mature cells. Furthermore, using CAGE and RNA-seq data sets from ENCODE, we find a strong connection between the methylation status of iHMRs and transcription of noncoding RNAs at the intergenic site. We show that transcription at iHMRs originates from defined sequence elements, and gives rise to distinct classes of RNAs that reflect the iHMR's regulatory activity. Next, we compared the human methylation profiles with data corresponding to orthologous blood cell types from chimpanzee. Methylation states at HMRs are generally conserved between human and chimp, with variation depending on the HMR type and divergence of the underlying sequence elements. Overall, enhancer iHMRs are the most variable between species, individuals, and developmental states. Our data indicate that progressive demethylation and transcription together define the most active enhancer elements in mature cells. Thus, HMRs reveal accurate cues to the activity of regulatory elements and RNA polymerase II genome wide, providing a strong framework to analyze methylation changes during development. Our integrated analysis of HMRs, and in particular iHMRs, can therefore determine cell-type-specific regulatory centers, including signatures of differentiation.

Results
Recently, using genome-wide DNA methylation data sets we have shown that intergenic domains of hypomethylation (HMRs) are more widespread and more variable during cellular differentiation than had been previously appreciated . In fact, HMRs occur throughout the genome, differing in their sequence context (e.g., CpG density) and their dynamic behavior during development. Because of this, HMRs, unlike CGIs, cannot be predicted by sequence characteristics alone and must instead be identified empirically using cell-type-specific methylation profiles. Here, using whole-genome BS-seq data, we compared sets of HMRs in H1 ESCs and primary human B cells. Differential HMR analysis revealed three broad categories of sites with different features: (1) shared, (2) specific, or (3) shared and significantly expanded in length in one cell type ( Fig. 1A; Supplemental Fig. S1A-D). These broad classifications alone permit some initial inferences regarding the potential behavior of the HMR. For example, HMRs overlapping gene promoters (''pHMRs'') have high CpG density and are predominantly shared between the two cell types (shared or both shared and expanding in one cell type relative to the other). Most intergenic and intronic HMRs (iHMRs), on the other hand, have lower CpG density than canonical CGIs (Supplemental Fig. S1A-D) and are often cell-type specific. The exceptions are CTCF-bound iHMRs, which are predominantly shared between the cell types. Examples of a shared, expanding pHMR and an intergenic iHMR for two B cell-associated loci are shown in Supplemental Figure S2.
Overall, the number of cell-type-specific iHMRs is fourfold higher in the mature cells (5106 in H1 ESC vs. 20,556 in B cells), indicating that lineage-specific loss of methylation at intergenic sites occurs during differentiation. We have previously found an enrichment for binding motifs of lymphoid-specific TFs at B cell iHMRs ). In addition, recent work has shown that binding of some transcription factors is directly linked to intergenic hypomethylation (Stadler et al. 2011). Therefore, we hypothesized that these iHMRs designate a lymphoid lineagespecific class of distal regulatory elements.
iHMRs discriminate a class of highly active, conserved DNase hypersensitive sites DNase I hypersensitivity sequencing (DNase-seq) is the established method for determining chromatin accessibility genome wide, and hence for identifying putative regulatory elements. Since a correlation between DNase sensitivity and methylation levels at some CpG sites has been reported (Thurman et al. 2012), we investigated the relationship between iHMRs and intergenic DNase hypersensitivity sites (DHS) in H1 ESCs and the GM12878 lymphoblastoid cell line (LCL, using data from LCLs as a proxy for primary B cells; see Methods and Supplemental Fig. S8). A majority of iHMRs overlaps significant DHS peaks in mature and stem cells (57% and 84%, respectively) ( Fig. 1B), and at least weak DHS signal enrichment can be detected in almost all iHMRs (Fig. 1C). In contrast, DHSs far out-number iHMRs, and only some (11%-33%) DHSs were hypomethylated ( Fig. 1B; Supplemental Fig. S6A). CpG density is a strong predictor for the methylation state of a DHS, with low CpG density correlating with high methylation levels ( Fig. 1D) (Spearmen r = À0.17; P = 1.6 3 10 À133 ). On the other hand, DHSs with higher CpG density show bimodal methylation levels. Among these high CpG sites, hypomethylation occurs specifically at those DHS positive for CTCF binding (68% vs. 31% without CTCF; P = 3.6 3 10 À185 , Fisher's exact test) or histone modifications, such as H3K4me2, associated with active regulatory states (Fig. 1E). Hypomethylated DHSs also show on average higher sequence conservation than those that do not overlap HMRs ( Fig.  1F) (P = 2.5 3 10 À229 ; Wilcoxon test).
Using MNase-seq data from LCLs, we observed nucleosomedepleted regions matching the length of the iHMRs (Supplemental Fig. S1E). This depletion is dynamic and cell-type specific, as H1 ESCspecific iHMR sites are not depleted for nucleosomes in LCLs. Together, these data suggest that a specific subset of important DHSs are de-novo demethylated during lymphoid lineage specification, and that loss of methylation is associated with local changes to nucleosomes.

Composite features of HMRs reflect a diversity of regulatory states
To investigate what classes of genomic elements give rise to iHMRs and their potentially diverse regulatory functions, we relied on ChIP-seq against modified histones, DNase-seq, and related techniques, to classify iHMRs based on their chromatin signature (Heintzman et al. 2007). We gathered an extensive catalog of chromatin state data sets from the ENCODE project (The ENCODE Project Consortium 2012) in H1 ESC and LCLs. Globally, we defined four major classes of iHMRs in H1 ESCs, based on different combinations of CTCF occupancy, DNase hypersensitivity, RNA polymerase II (RNA Pol II) binding, and histone modifications ( Fig.  2A). Their respective chromatin states resembled the typical signatures of insulators (''CTCF''), enhancers (''Enhancer-like''), active promoters (''Promoter-like''), and bivalent elements (''Bivalent'') ). This classification was further supported by a specific enrichment of ENCODE enhancer and promoter predictions (Yip et al. 2012) in their respective iHMR groups (Supplemental Fig. S1D, P < 1.6 3 10 À110 ; Fisher's exact test). iHMRs overlapping sequencedefined CpG islands (outside of annotated gene-promoter regions) are mostly shared between cell types (97% compared with 68% for non-CGI iHMRs; P < 2.2 3 10 À16 ; Fisher's exact test). In H1 ESCs, CGI-overlapping iHMRs were found to be either Promoter-like, with levels of H3K4me3 and RNA Pol II binding reminiscent of pHMRs at annotated genes, or in the bivalent state, with high levels of H3K4me2 and H3K27me3. Enhancer-like iHMRs, on the other hand, are mostly cell-type specific and lie outside of annotated CGIs ( Fig. 2A). This classification strategy could also be applied to other cell types. In LCLs, a less prominent class of bivalent iHMRs was present and, instead, we observed a large group of ''silent'' iHMRs marked by H3K27me3, with little or no H3K4methylation and lower DNase hypersensitivity (Supplemental Fig.  S1F). A second class of ''promoter-like'' iHMRs in a putatively ''inactive'' state (no H3K27ac and RNA Pol II) was also observed. As predicted, B cell-specific iHMRs (especially in the ''enhancer'' class) show highly cell-type-specific chromatin states, while shared iHMRs (mostly in the ''CTCF'' and ''promoter'' classes) also share many histone marks between cell types (Supplemental Fig. S1F).
Meta-profiles of chromatin states confirmed distinct patterns of histone modifications specifically within the region identified by hypomethylation at all iHMR types (Fig. 2B). In all cases, H3K4me2 covers the entire hypomethylated region, along with a central narrower peak of DNase hypersensitivity. H3K4me1 enrichment corresponds with the boundaries of enhancer and promoter-like iHMRs, while promoter-like iHMRs also contain a central, sharp peak of H3K4me3. In contrast, the bivalent iHMRs lie within broader domains of H3K27me3, where H3K4 methylation specifically marks the core hypomethylated region. The functional significance of these iHMR classifications is supported by enrichments for pluripotency factors at stem cell enhancers (POU5F1 and NANOG) and polycomb proteins (SUZ12) at bivalent sites ( Fig. 2C). Histone acetyltransferase EP300, like RNA polymerase II, is abundant at both enhancer and promoter-like iHMRs, but not present at transcriptionally silent CTCF and bivalent sites. Overall, different types of iHMRs identify regions displaying different chromatin states, suggesting a diversity of regulatory activities associated with hypomethylation in specific developmental contexts.

Coordinated changes at iHMRs and nearby pHMRs can impact the activities of associated genes
Because CpG dense gene promoter HMRs are typically preestablished in embryonic stem cells and shared between different cell types, tissue-specific activation of genes is often difficult to predict from the methylation states of their promoters. Previously, we showed that pHMRs expand upon lineage-specific gene activation during adult blood cell maturation. Illustrating this pattern, differential H1 ESC and B cell methylation shows asymmetric spreading of hypomethylation at pHMRs outside of the constitutive CpG-rich core region ( Fig. 3A; Supplemental Fig. S3A,B). The direction and extent of differential hypomethylation differs greatly between genes, but in each case tracks very closely with cell-type-specific spreading of H3K4me2 at these promoters ( Given that most differential methylation occurs distal to gene promoters, and that many of these regions show features of regulatory elements, we asked whether iHMRs are informative about the regulation of nearby genes. First, we observed that expanding pHMRs are more proximal to cell-type-specific iHMRs (P = 2.2 3 10 À17 ; Wilcoxon test), but not to CTCF iHMRs (P = 0.77; Wilcoxon test) (Fig. 3C). This suggested that demethylation of enhancers might be involved in the activation of these genes. Indeed, both enhancer iHMR hypomethylation and promoter pHMR expansion together are more predictive of higher gene expression changes than either on its own. A total of 44% of genes with an expanded promoter HMR and a nearby (<25 kb) iHMR show elevated expression (fold change >2), compared with 23% with an expanded pHMR, but no nearby (>100 kb) iHMR and 12% without a significant expansion of the pHMR, but an iHMR closer than 25 kb. This effect is diluted with iHMR distance from the gene (Spearman r = À0.21; P = 2.93 3 10 À125 ) (Fig. 3D).
Breaking this overall pattern, in H1 ESCs a subset of expanded pHMRs occurs at silent genes ( Fig. 3E; Supplemental Fig. S3) (P = 2.6 3 10 À26 ; Wilcoxon test). In those cases, high levels of H3K27me3 cover the pHMR, spreading along the expanded pHMR region, while H3K4me2 remains confined to only the constitutively hypomethylated core region (Supplemental Fig. S3E). Consistently, many of these bivalent pHMRs co-occur with nearby bivalent iHMRs (Fig. 3F), as defined in Figure 1. In B cells, the majority of bivalent iHMRs observed in H1 ESCs remains hypomethylated and are generally resolved into either an active (high H3K4 methylation and RNA Pol II binding) or a silent (some H3K27me3 enrichment and low H3K4 methylation) chromatin state ( Fig. 4A; for examples, see Supplemental Fig. S4). While silencing mostly occurs with H3K27 methylation, a smaller subset of H1 ESC bivalent iHMRs are silenced by DNA methylation in B cells. These sites are completely devoid of DNase hypersensitivity and all of the studied histone modifications. These described changes all tend to be coordinated between the iHMR and an associated pHMR. Illustrating these patterns, Figure 4B depicts examples of multiple shared and specific iHMRs near the annexin A2 receptor gene ANXA2R, including a cluster of bivalent H1 ESC iHMRs, that become active in LCLs, as indicated by exchange of trimethylated H3K27 for acetylated H3K27.
Transcribed, cell-type-specific iHMRs mark likely active enhancers Many fully methylated regions in H1 ESCs lost DNA methylation in the mature B cell, and we referred to these as de novo demethylated iHMRs. We sought to understand the timing of these changes during cell-fate specification, so we compared the methylation levels in B cells at these iHMRs with other human primary hematopoietic cell types, including hematopoietic stem and progenitor cells (HSPCs) and a nonlymphoid blood cell (neutrophils) (Supplemental Fig. S5A). iHMRs shared between B cells and H1 ESCs also show equally low methylation levels in the other blood cell types. iHMRs not present in H1 ESCs, however, show a large population of lineage-shared sites, which have equally low methylation levels in all three blood cell types, as well as a smaller fraction of B cell iHMRs only partially hypomethylated in the other blood cells. As expected, these iHMRs are enriched for lymphoidspecific TFs such as NFKB, ELF1, EGR1, EBF1, and PAX5 (data not shown) and are proximal to genes involved in lymphocyte development (using GREAT) (McLean et al. 2010).
In accordance with previous observations in HSPCs , this suggested that B cell-specific regulatory elements become partially hypomethylated early during hematopoietic differentiation and maintain this intermediate methylation state in blood sister lineages, while undergoing additional hypomethylation specifically in B cells. Consistent with this, the ''silent'' class of demethylated B cell iHMRs (Supplemental Fig.  S1E) is potentially active in other blood cells. These lymphoidspecific iHMRs often are already DNase hypersensitive in stem cells, but not yet hypomethylated, i.e., they are shared DHSs with differential HMRs between H1 ESC and B cells (Supplemental Fig.  S6B). This potentially primed state in the stem cell population is characterized by intermediate DNase hypersensitivity, but an almost complete absence of the studied histone marks, except for a slight H3K4me1 enrichment (Supplemental Fig. S6C). In the blood lineage, these sites become partially hypomethylated, while in B cells, a subset acquires the signature of potentially active regulatory elements and is fully hypomethylated. The rest remain ''silent'' in B cells, showing H3K27me3 enrichment.
To better understand these progressive methylation changes between cell-types, we searched for associated regulatory events. We observed that some de novo iHMRs showed evidence of being transcribed (see example in Fig. 4B). Transcription at some enhancer sites has recently been described as a marker of an active enhancer state (so-called eRNA) (Kim et al. 2010), which prompted us to investigate whether intergenic transcription was linked more generally to cell-type-specific iHMR hypomethylation. We indeed found that transcriptional activity is not an exclusive property of pHMRs at gene promoters or ''promoter-like'' iHMRs. In H1 ESCs, over 44% of intergenic enhancer-like iHMRs possess CAGE tags, which represent the transcription start site (TSS) of capped transcripts. In LCLs, transcription occurs at those iHMRs with the most B cell-specific hypomethylation (Fig. 4C) (P = 7.4 3 10 À35 ; Wilcoxon test). Compared with the other lineage-shared iHMRs, B cell-specific transcribed intergenic HMRs also display higher levels of several histone modifications, particularly H3K27ac (P = 1.8 3 10 À293 ; Wilcoxon test), suggesting a strong enrichment for active lymphoid enhancers ( Fig. 4D; Creyghton et al. 2010). Similarly, among iHMRs, which were bivalent in H1 ESCs, transcription in LCLs marks those regions that lose H3K27me3 (Supplemental Fig.  S5B). Importantly, this iHMR transcriptional activation is correlated with the activation of nearby genes (Supplemental Fig.  S5C,D). Together these data suggest that iHMR transcription is linked to highly cell-type-specific demethylation of previously primed loci, which may function as cell-type-specific active enhancer elements (see Fig. 7 below and Discussion).

Features of transcription at iHMRs reveal positional cues in the primary sequence
We analyzed RNA-seq data sets (Djebali et al. 2012) to further explore the nature of transcription at iHMRs. Consistent with our functional classification scheme, different classes of iHMRs generate distinct types of RNA transcripts (with the exception of the mostly silent CTCF iHMRs) (Fig. 5A-C). All transcripts are less abundant overall than annotated long noncoding RNAs and mRNAs. Capped transcripts from bivalent iHMRs and enhancer iHMRs are even less frequent in the steady-state RNA population than transcripts derived from promoter-like iHMRs (Fig. 5A) (P = 2.6 3 10 À10 ; Wilcoxon test). Bivalent iHMRs are enriched for polyadenylated transcripts at levels comparable to annotated transcripts, while RNAs from enhancer-like iHMRs are mostly, but not exclusively, nuclear and non-poly(A)+ compared with RNAs from promoter-like iHMRs (P = 7.2 3 10 À6 ; Wilcoxon test) (Fig. 5B,C), consistent with previous descriptions of eRNA (Kim et al. 2010). The distinction between enhancer-like and promoter-like transcribed iHMRs is also reflected in their primary genomic sequence. ARTS, a sequence-based promoter prediction tool (Sonnenburg et al. 2006), which was trained on annotated gene-promoters, assigns significantly lower promoter scores to enhancer-like iHMRs than promoter-like iHMRs (Fig. 5D) (P = 1.3 3 10 À262 ; Wilcoxon test).
To study the relationship between transcription and iHMR features in more detail, we used peaks of CAGE tags to define transcription start sites of capped RNAs within enhancer-like intergenic HMRs. Meta-profiles of RNA and histone data depicted well-defined start sites for these transcripts (Fig. 5E) with CAGE tags, marking the 59 end of the RNAs, preferentially situated at the center of the iHMR, and RNA-seq coverage, marking the transcript bodies, following the direction of transcription.
This raised the question of whether specific DNA elements signal the action of RNA polymerases inside iHMRs. To answer this, we searched for features that coincided with the observed TSS positions. Histone modifications reveal a clear positional signal, with peaks of H3K4me1, H3K27ac, and DNase hypersensitivity  centered slightly upstream of the TSS (Fig. 5F). Similarly, TATA box binding protein (TBP) and TBP associated factors (TAF) are enriched at these loci, peaking immediately upstream of the TSS (Supplemental Fig. S7A) together with RNA Pol II. Given the welldefined start sites, we looked for the TATA box sequence motif within enhancer-like iHMRs. The TATA box is an ancient motif that provides a precise positional cue for initiation by RNA Pol II at a fixed distance from the TSS (Lenhard et al. 2012). We found enrichment for TATA matches in transcribed enhancer-like iHMRs ( Fig. 5K; Supplemental Fig. S7B). These matches occur around 20 bp upstream of the eRNA TSS and show evidence of increased evolutionary conservation at these positions (Supplemental Fig. S7C,D).
Interestingly, the distribution of guanines and cytosines is asymmetric around these TSSs (Fig. 5G). This GC skew begins slightly upstream of the CAGE peak and is codirectional with transcription. GC skew allows the formation of thermodynamically stable R-loop structures, and may protect the DNA sequence from methylation (Ginno et al. 2012). This was an unexpected property of enhancer iHMRs, which has previously only been associated with constitutively hypomethylated promoter CGIs rather than with iHMRs that are comparatively CpG poor and dynamically demethylated during differentiation.
Despite overall DNA sequence-based promoter prediction scores being low at enhancer-like iHMRs (Fig. 5D), a distinct peak at the TSS position is seen, which further supports the notion that well-defined sequence features control iHMR transcription (Fig. 5H). The specific TSSs, corresponding to chromatin and conserved sequence features, suggests a link between transcription and the regulatory function of at least some iHMRs, rather than iHMR transcription being a purely random by-product of proximity to high concentrations of RNA Pol II at the promoters of regulated genes.
A close link between intergenic transcription and iHMR establishment is also supported by the observation that DHSs with CAGE tags are significantly more likely to be hypomethylated (Fig. 5I) (P = 9.8 3 10 À144 Fisher's exact test). Furthermore, the presence of a TATA box motif in the DNA sequence of a DHS is predictive of hypomethylation ( Fig. 5J) (P = 5.2 3 10 À20 ; Fisher's exact test). Interestingly, among the classes of iHMRs defined in Figure 1, the TATA box distinguishes the cell-type-specific subclass of enhancer iHMRs that are transcribed (Fig. 5K). This suggests that recruitment of TBP (Supplemental Fig. S6D), RNA Pol II, and transcription are not purely a consequence of hypomethylation, but instead are directed by sequence features that possibly play a causal role in the establishment of hypomethylation at these sites.

Cross-species and within-species comparison of iHMRs reveals evolving set of putative enhancers
Comparative HMR and chromatin-state analysis between cell types revealed distinct classes of intergenic elements. To test for functional conservation of these iHMRs, we investigated the concordance of methylation states between corresponding blood cell types in chimpanzee and human. In adult blood cells, 77% of human-shared iHMRs also overlap iHMRs in chimpanzee (Fig. 6A). A subset of B cell-specific human iHMRs is also specifically hypomethylated in chimp B cells, but not other blood cell-types (P = 7.2 3 10 À287 ; x 2 test). Human-specific iHMRs show lower sequence conservation than those shared with chimp (P = 2.3 3 10 À248 ; Wilcoxon test) (Fig. 6B), suggesting that species-differential iHMRs may be explained in part by divergence of functional regulatory sequences. Next, for each class of human B cell iHMRs (defined in Supplemental  Fig. S1E), we measured the methylation state in chimp. Notably, the methylation state of enhancer-like iHMRs is significantly less conserved in chimp compared with other classes (Fig. 6C) (P < 1.3 3 10 À150 , Fisher's exact test). At human transcribed iHMRs containing a TATA box, conservation of the TATA motif predicts conservation of hypomethylation in chimp (Fig. 6D) (P = 0.004, Fisher's exact test), consistent with a role for transcriptional signals in establishment and maintenance of iHMRs. Consistently, this effect is not seen at CTCF iHMRs, which are for the most part transcriptionally inert.
Since a substantial fraction of cell-type-specific iHMRs showed variability between species, we investigated whether iHMR methylation levels might also be variable in human populations. Using targeted BS-seq data from the whole blood of 44 human individuals (N Plongthongkum, KR van Eijk, S de Jong, T Wang, JH Sul, MPM Boks, RS Kahn, HL Fung, RA Ophoff, and K Zhang, in prep.), methylation levels at individual CpG sites within HMRs were assessed. Variable CpGs, (i.e., sites with variation in methylation levels between individuals, not caused by a SNP disrupting the CpG site itself) are enriched specifically within B cell-specific enhancerlike iHMRs compared with pHMRs or other classes of iHMRs (Fig.  6E) (P < 1.5 3 10 À9 ; Wilcoxon test). Notably, iHMRs conserved with chimp also show reduced methylation variation among human individuals (Fig. 6F) (P = 1.1 3 10 À7 ; Wilcoxon test), suggesting more robust hypomethylation and possibly stronger selective constraint on the methylation state. Together, these data show that intergenic loci, specifically putative enhancers, with methylation states that dynamically change during differentiation are also the most likely to vary both between individuals and between species.

Discussion
Patterns of DNA methylation, and specifically localized hypomethylation, distinguish developmental lineages and the different cell types within them Bock et al. 2012). However, we and others have shown that the degree of differential methylation between cell types is modest among CpG dense promoters (Stadler et al. 2011;Bock et al. 2012). Instead, the most celltype discriminatory patterns are found in intergenic space, especially outside of CpG Islands. These cell-type-specific iHMRs cannot be predicted by simple sequence characteristics and must instead be identified empirically using cell-type-specific methylation profiles. The importance of this unbiased approach to identify functional regulatory elements has recently been confirmed by comparative methylation analysis across vertebrates (Long et al. 2013).
The extensive sampling of regions with lower CpG density is necessary to capture the diversity of putative, cell-type-specific regulatory elements that might be impacted by DNA methylation. Here, the integration of numerous genome-scale data sets revealed that HMRs fall into distinct functional groups with differing methylation dynamics and regulatory behaviors during development. From this analysis, four dominant classes of intergenic hypomethylated regions emerged: enhancer-like, promoter-like, bivalent, and insulator, related to chromatin patterns found genome wide ). However, it bears mention that substructure is apparent within each category. This is because each group displays a range of enrichment for distinctive histone marks and other features, resulting in somewhat diffuse cut-offs between clusters and revealing the complexity within groups to be even higher than depicted. This complexity of regulatory elements in the noncoding genome has only recently become visible, and the link between DNA methylation and the diverse element types and chromatin states has not previously been deeply addressed on a genome-wide scale.
Our analyses investigated a number of long-held beliefs about the relationship between DNA methylation and regulatory domains and also revealed new and unexpected insights. For example, DNase hypersensitivity is believed to be a universal signature of active cis-regulatory elements and has been shown to be strongly negatively associated with DNA methylation (Thurman et al. 2012). Yet, we observe a class of shared DHS sites that are fully methylated in stem cells and lose methylation in differentiated cells. This suggests that while permissive DHS sites are already set up in embryonic stages, they can still be methylated, until specific regulatory events, including histone modifications and ncRNA transcription, occur during differentiation.
As summarized in Figure 7, we found that one of the primary distinctions between different types of iHMRs is the tendency to be either constitutive throughout development or de novo demethylated in mature cells. This characteristic separates enhancerlike iHMRs from other classes. Our data suggest that these iHMRs are the most cell-type specific and the most dynamically regulated during development. Based on our comparison of the different cell types in this study, we may suggest a timeline of enhancer iHMR formation in which three major steps are observed. First, DHS are pre-established in the embryonic stem cell, but remain methylated. Hematopoietic regulatory elements are partially demethylated during blood-cell commitment, a process that may be initiated at even earlier stages of differentiation than the HSPC stage assessed here. Lastly, lymphoid enhancers become transcribed and completely demethylated specifically in B cells, reaching their celltype-specific fully active state (Fig. 7). As most specific iHMRs in adult somatic lymphoid cells are not hypomethylated in the stem cell, this would imply that demethylating mechanisms, whether passive or active, are at work in differentiating cells. This hypothesis is supported by a recent observation that hydroxymethylcytosine marks enhancers in differentiating neural progenitors in mouse (Serandour et al. 2012), though it remains to be seen whether this association extends to other cell types. Alternatively, molecular interactions that protect these regions from maintenance methylation may also play a role. Demethylation associated with TF binding could be one such mechanism, since it was recently shown that CTCF binding is both necessary and sufficient to create distal regions of low methylation resembling iHMRs (Stadler et al. 2011). Our data support this model, since we observe nucleosome displacement and increased frequency of TF occupancy at sites that become hypomethylated in lymphoid cells.
The state of DNA methylation at distal regulatory sites also allowed us to infer the activity of nearby genes and perhaps even more accurately detect links between iHMR methylation and the methylation state at the gene promoter itself. Previously, ''active'' enhancers were identified by histone marks, i.e., H3K4me1 and H3K27ac, which distinguish distal enhancers from proximal promoters. Here, we show that iHMRs specifically mark active elements that are linked to both pHMR expansion and differential gene expression. Most of these highly active iHMRs also harbor TSS for noncoding transcripts. In fact, our data indicate that transcription is a hallmark of active iHMRs, and recent evidence suggests a putative functional basis for this phenomenon, since disruption of some enhancers, as well as siRNA knockdown of eRNAs, interferes with the expression of eRNAs and enhancer-regulated genes (Ling et al. 2004;Wang et al. 2011;Melo et al. 2013). iHMR transcripts originate from well-defined, position-specific, but generally weak promoter sequence elements in the iHMR, in patterns that separate iHMR types and CGIs. Close examination of iHMR TSSs reveals conservation of transcriptional sequence signals embedded within the iHMR. We observed strand asymmetry (CG skew) in iHMR transcripts, reminiscent of features linking transcription to protection from methylation at promoter CGIs (Ginno et al. 2012). These characteristics suggest that, in addition to simple TF binding, transcription may be involved in establishing or maintaining hypomethylation at dynamic iHMRs. Indeed, we observed that even low CpG density intergenic regulatory regions with transcriptional activity are strongly hypomethylated. Accordingly, while many lymphocyte-specific iHMRs already begin to lose methylation in the early stages of the blood lineage, the complete loss of methylation at iHMRs correlates with increased noncoding transcriptional output and enhancer activation. Whether or not disruption of these RNAs or their transcription alters enhancer methylation states remains to be shown.
iHMRs that show bivalent histone marks in stems cells are typically constitutively hypomethylated. Like enhancer iHMRs, their activity appears tightly coordinated with expanded pHMRs of nearby genes. We found an unexpected degree of coincidence between pairs of bivalent, expanded pHMRs and iHMRs. Previously, ''permissive'' H3K4me1-marked enhancers have been paired with polycomb-repressed promoters (Rada-Iglesias et al. 2011, Taberlay et al. 2011, but the extent of bivalently marked iHMRs and their widespread co-occurrence with pHMR counterparts had not yet been seen genome wide. Patterns of methylation at many iHMRs are conserved in chimpanzee in a manner reflected by increased sequence conservation and reduced variation at the epigenetic level within the human population. In contrast, a subset of human-specific enhancer-like iHMRs show increased methylation variation within the human population. This epigenetic variation may be relevant to inter-individual differences in gene regulation and to disease susceptibility (Akhtar-Zaidi et al. 2012;Toperoff et al. 2012). Loss or gain of TF-binding events resulting from sequence divergence may account for cross-species differential methylation at constrained elements (MacArthur and Brookfield 2004;Prabhakar et al. 2008;Wilson and Odom 2009;Schmidt et al. 2010). Indeed, only 40% of human B cell-specific iHMRs overlap iHMRs of the orthologous cell type in chimpanzee compared with constitutive, shared iHMRs of other cell types. This may signify that cell-type-specific iHMRs depend on sequence features with a higher turnover rate than constitutively hypomethylated regions, including CGIs. In this context, we found that loss of a TATA box at enhancer iHMRs predicts loss of hypomethylation in chimp, suggesting a role for TBP and the transcriptional machinery, in addition to the function of other specific TFs, in the establishment of these iHMRs. These comparisons may thus serve to distinguish those enhancer iHMRs that fit an ''enhanceosome'' model, whereby exclusion of a single binding event can wipe out enhancer function in one species (possibly by loss of hypomethylation), or a ''billboard'' model in which bindingsite rearrangements within the enhancer are tolerated as long as the sum of TF interaction is constant (Arnosti and Kulkarni 2005;Lusk and Eisen 2010).

GM12878 methylation data
Lymphoblastoid cell line (LCL) methylomes were generated from genomic DNA purchased from Coriell (cat #NA12878) according to the methods described previously ). Four flow cell lanes of bisulfite-converted fragment libraries were sequenced Figure 7. Model of iHMR behavior at a B cell specifically expressed gene. Shared DHS sites are pre-established in the embryonic stem cell. Hypomethylation at the CpG Island gene promoter (right) and at a CTCF iHMR (left) is constant during development. The enhancer-like iHMR (middle) is fully methylated in H1 ESC. In blood-specified progenitors (HSPCs), it becomes partially demethylated but remains inactive, i.e., lacks H3K4 methylation and RNA transcription. In the B cell state, where the gene is expressed, the promoter HMR expands beyond the core CGI region, and the iHMR becomes fully hypomethylated. The enhancer-like iHMR displays an active enhancer chromatin state (H3K4me1, H3K27ac). It is bound by TBP and RNA Pol II at specific sequence elements (including the TATA box), which initiate eRNA transcription within the iHMR.
on the Illumina Hiseq platform to obtain paired-end 100-bp read lengths and ;103 coverage of the human genome.

Methylation data
Whole-genome bisulfite sequencing data for human B cells, Neutrophils, and HSPCs were taken from Hodges et al. (2011) (GEO accession number GSE31971), and data for H1 ESCs was obtained from Lister et al. (2009). Methylation data for chimpanzee-derived hematopoietic cells Flow cytometry and DNA extraction were performed according to previously described methods . Briefly, peripheral blood was collected from healthy female donors and pooled. After isolation by Ficoll gradient, mononuclear cells were fixed and stained with antibodies against the following human cell surface markers (eBioscience): anti-CD34 conjugated to PE-Cy7, anti-CD38 conjugated to APC, anti-CD45 conjugated to PE, anti-CD19 conjugated to PE, and anti-CD235a (Glycophorin) conjugated to PE. For lineage depletion, either a combination of PE-conjugated antibodies against CD45, CD19, and CD235a, or a commercially available human hematopoietic lineage cocktail was used.

Computational analysis
Mapping BS-seq reads was performed with methods described by Smith and colleagues using tools from the RMAP package (Smith et al. 2009). Mapping statistics for BS-seq libraries generated herein are provided in Supplemental Table S1. Hypomethylated regions were called for each cell type using the Hidden Markov Model described in Hodges et al. (2011). For H1 ESCs, two replicates were available and only HMRs called in both replicates were used (Suplemental Fig. S8A). The methylation level of genomic regions (HMRs, DHS) was computed as the mean ratio of converted to unconverted calls at all CpGs in the interval. Expanding promoter HMRs were detected as an uninterrupted run of at least five significantly differentially methylated CpG sites next to a shared HMR (for details on differential methylation calls see Hodges et al. 2011).
We used ChIP-seq or RNA-seq from LCLs (GM12878) as a proxy for B cells. Comparing methylation levels at iHMRs, we confirmed that B cell iHMRs are strongly shared with LCLs, which hence make a good proxy for B cells (Supplemental Fig. S8B,C). For B cell HMR analysis relying on LCL data, only those HMRs with an average methylation level <20% in the GM12878 bisulfite data were used. We did not use HMR calls from the GM12878 methylome itself since, as an immortalized cell line, it contains a number of features differing from primary cells, including very large domains of hypomethylation and ''fuzzy'' HMR boundaries (Supplemental Figs. S8C,S9).
Human genome version 19 and gencode (version 7) gene annotations were used, defining any HMR within 250 bp of a gene's 59 end as a TSS HMR, any HMR over 1 kb from any gene as intergenic, and an HMR within a gene that does not overlap any exon as intronic. CpG Island calls were obtained from the UCSC Genome Browser track ''CpG Islands'' based on Gardiner-Garden and Frommer (1987). HMRs were considered shared between two cell types if HMRs called in each data set independently overlap by at least 1 bp.
Conservation of HMRs between human and chimp: HMRs were called mapping BS-seq data to the chimpanzee genome (version panTro3 from the UCSC Genome Browser) and lifted over to the human genome (hg19) using the UCSC Genome Browser liftOver tool (version 1.28 with the ''panTro3ToHg19'' and ''hg19ToPanTro3'' chains) to assess overlap with human HMRs. Only those human HMRs were considered for analysis that had a unique corresponding position in the chimpanzee genome (based on liftOver to the chimpanzee genome and back resulting in only the original position).
All ChIP, MNase, and DNase-seq signal tracks were taken from ENCODE (The ENCODE Project Consortium 2011) (Broad histone ChIP, Duke DNase-seq, Stanford MNase-seq) and are scored as fold enrichment over the average genomic read density. TF-binding calls are based on ENCODE uniform peak calls (SPP) (The ENCODE Project Consortium 2011). DNase HS sites are based on Duke DNase HS calls in ENCODE. Enhancer and (novel) promoter predictions were taken from the ENCODE elements analysis (Yip et al. 2012) and are based on supervised classification of ENCODE histone-modification and TF-binding data. All ENCODE data sets are available at http://encodeproject.org/ENCODE/ downloads.html.
Likely evolutionarily conserved positions were determined using the phastCons mammalian constraint score (from the UCSC track ''Vertebrate Multiz Alignment & Conservation'') with a cutoff of 0.9 on the posterior probability of conservation.
Gene expression data was taken from the ENCODE transcriptome group for GM12878 and H1 ESCs (UCSC ENCODE RNAseq Track). RNA-seq (UCSC CSHL Long RNA-seq) and CAGE (UCSC Riken CAGE) reads in each HMR (based on their 59 end) were counted and normalized (RPKM: reads per kilobase and million mappable reads for long RNA-seq and RPM for CAGE). Replicates were pooled and for each HMR the highest value of any RNA type [Poly(A) + or Poly(A) À , whole cell, nuclear, or cytoplasmic] was used. Expression changes are computed as log((1 + RPKM1)/(1 + RPKM2)). To avoid confounding effects from host genes, eRNA analysis was limited to intergenic iHMRs (>1 kb from any gene).
CpG density was computed as the ratio of the observed over expected CpG count based on the local CG-density. GC-skew was computed as (G À C)/(G + C). Both were computed in 50-bp sliding windows. The sequence-based promoter prediction scores were generated using the SVM model implemented in ARTS (Sonnenburg et al. 2006) at single-nucleotide resolution on both strands of the human genome. The predictions are based on a combination of features, including position-specific core promoter motifs and local sequence features, e.g., k-mer stats. For details, see Sonnenburg et al. ( 2006). The scores are the raw SVM outputs (À5 to 5), with positive scores indicating a stronger promoter prediction. TATA-box motifs were called as exact matches to 59-TATAAA-39 within the HMR on either DNA strand.
Clustering of HMRs into chromatin groups was performed using hierarchical clustering (using the hclust function of R) with a Manhattan distance and UPGMA linkage on the log-transformed ChIP signal. HMRs with any missing data (unmappable regions) or input control signal more than threefold different from the genomic average were filtered out. The trees were cut at a branch height resulting in 20 groups. Only the major groups, containing at least 10% of the intergenic HMRs, were used for further analysis. Clustering of B cell iHMRs was based on chromatin data from GM12878 as well as the chromatin state of these sites in H1 ESCs, as shown in the heatmap (Supplemental Fig. S1E). A comprehensive BED file listing the coordinates of iHMRs and their functional annotations are provided in Supplemental Tables S2 and S3 for H1 ESCs and B cells, respectively.
Histone profiles at ''meta-HMRs'' were computed by aligning all selected HMRs at their 59 and 39 ends, evaluating the signal inbetween in 250 equal-sized steps and taking the (95th percentile) trimmed mean at each position. For TSS centered plots, the CAGE read density within an HMR was smoothed using a 25-bp sliding window, and the point of highest signal was marked as the TSS peak. All boxplots show the fifth, 25th, 50th, 75th, and 95th percentiles.
CpG methylation variation in the human population was assessed using targeted bisulfite sequencing data from N Plongthongkum, KR van Eijk, S de Jong, T Wang, JH Sul, MPM Boks, RS Kahn, HL Fung, RA Ophoff, and K Zhang (in prep.) in whole-blood samples of 44 individuals. Methylation at individual CpGs within HMRs was assessed in 78,605 regions targeted by padlock probes (BSPP) and compared across individuals. For each CpG site targeted by a probe, the standard deviation of methylation levels between individuals was assessed. Sites with an SD >0.1 were considered variable. The variability of each HMR was then scored as the ratio of probes containing variable CpG sites to total probes overlapping the HMR.

Data access
BS-seq data have been deposited in the NCBI Sequence Read Archive (SRA; http://www.ncbi.nlm.nih.gov/sra) and the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and are available through the following accession numbers: SRP022182 and SRP021118. BSPP data have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE47614.


text:
Bves and NDRG4 regulate directional epicardial cell migration through autocrine extracellular matrix deposition

The Bves and NDRG4 proteins interact to regulate directional cell movement by mediating cell surface fusion of internalized fibronectin for resecretion. This provides the first evidence of Bves/NDRG4 protein function within subcellular trafficking pathways and explains how the Bves complex diversely influences development, cancer, and repair.

INTRODUCTION
Blood vessel epicardial substance (Bves; or Popdc1), an integral membrane protein, is the prototypical member of the Popdc family, which regulates cell migration and adhesion through unknown molecular mechanisms. Bves shares a conserved cAMP-binding Popeye domain but contains no other homologous functional domains with other protein families (Andrée et al., 2000;Froese et al., 2012). First identified in epicardial tissue, Bves is now recognized for its dynamic distribution in a multitude of cell types (Reese et al., somes. These data provide a molecular mechanism for motility substrate delivery that regulates directional cell movement. Taken together, they demonstrate that Bves, through interaction with its binding partners, is essential for cell surface fusion of endosomes in regulation of cell motility and adhesion. Given the diversity of phenotypes observed with Bves loss of function, the present study has broad implications for development, cancer, and repair.

Bves and NDRG4 bind directly through a novel interaction domain
In an effort to determine molecular function, were identified Bves binding partners with a split ubiquitin two-hybrid screen (Iyer et al., 2005) designed for membrane-associated proteins using Bves as bait. Initial results identified a direct interaction between Bves and NDRG4 that passed all stringency selection tests (DualSystems, Biotech, Zurich, Switzerland). Myc-tagged full-length Bves (Bves-myc) coimmunoprecipitated with green fluorescent protein (GFP)-tagged NDRG4 (NDRG4-GFP) from Cos7 cell lysates, biochemically confirming the interaction identified by split ubiquitin screening technology ( Figure 1A). To further define the interaction domain, we analyzed a series of glutathione S-transferase (GST)-tagged Bves constructs in which the C-terminus (GST-Bves357; amino acids 115-357) was successively truncated (Figure 1B,left). Only the GST-Bves357 interacted with NDRG4 ( Figure 1B, right). All other C-terminal truncations failed to pull down NDRG4-GFP. All Bves-GST truncations failed to interact with enhanced GFP expressed in Cos7 cell lysates ( Figure 1B′). These data suggested that the NDRG4 binding domain was likely to be contained within the region between amino acids 300 and 357.

Bves and NDRG4 colocalize and interact in mammalian epicardial cells
Bves and NDRG4 gene products are both enriched in the developing heart tube (Qu et al., 2002;Hager and Bader, 2009). We examined the precise distribution patterns of the two proteins in E14.5 cardiac tissue and determined that Bves and NDRG4 colocalized in both epicardial cells and cardiac myocytes (Figure 2A, epicardium in inset). Furthermore, coimmunoprecipitation of epicardial cell lysates demonstrated that an antibody to endogenous Bves pulls down both Bves and NDRG4 protein, whereas MF-20 antibody, an antisarcomeric myosin heavy chain antibody (Bader et al., 1982) bound to beads, did not ( Figure 2B). Given that subcellular localization of Bves varies depending on whether cells are freely migratory or manner, incorporating into anterior head and somitic structures. With morpholino knockdown of Bves in these same blastomeres, migration of resulting progeny is completely randomized, with cells moving throughout the embryo (Ripley et al., 2006). Randomization of movement after Bves loss of function is a recurring theme observed in both single-cell and sheet epithelium migration models, including human corneal epithelial cells, germ cells of the fly, and myoblasts in murine skeletal muscle regeneration (Wada et al., 2001;Andrée et al., 2002;Ripley et al., 2004Ripley et al., , 2006Osler et al., 2005;Lin et al., 2007;Kawaguchi et al., 2008;Smith et al., 2008;Hager et al., 2010;Kim et al., 2010). Bves function in regulation of cell movement is of immediate relevance, as Bves has recently been identified as a metastasis suppressor in colorectal cancers . Thus Bves is an important mediator of cell adhesion and movement in a variety of settings.
Although Bves has emerged as a strong regulator of cell movement, the lack of identifiable motifs within the protein has made assignment of molecular functions challenging. To circumvent this problem, yeast two-hybrid interaction screens were conducted to identify binding partners and link Bves to a molecular function. Of importance, Hager et al. (2010) demonstrated that Bves interacts with the recycling endosome soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) protein, vesicle-associated membrane protein 3 (VAMP3; cellubrevin), to facilitate trafficking of two canonical VAMP3-dependent cargoes, transferrin and β 1 -integrin, to the cell surface. Previous work demonstrated that loss of Bves function disrupted subcellular localization of cell/cell adhesion molecules such as E-cadherin and occludin (Osler et al., 2005;Russ et al., 2011). Thus we postulated that Bves serves as a pivotal regulator of subcellular trafficking of molecules to the cell surface. Still, understanding of the precise molecular behavior of Bves within subcellular trafficking pathways remains unresolved.
Here using multiple methodologies, we identify a novel interaction between Bves and N-myc downstream regulated gene 4 (NDRG4), which is of interest because NDRG4 influences cell behaviors regulated by Bves, including cell migration and process extension (Zhou et al., 2001;Qu et al., 2002Qu et al., , 2008Nishimoto et al., 2003;Melotte et al., 2009;Schilling et al., 2009;Kim et al., 2010;Williams et al., 2011). Loss of NDRG4 in mouse models depletes intracerebral brain-derived neurotrophic factor through unknown mechanisms, causing spatial learning defects in animals (Yamamoto et al., 2011). Thus it is intriguing that the NDRG protein family has been generally implicated in trafficking of cell surface proteins (Ohki et al., 2002;Okuda et al., 2004;Berger et al., 2006;Kachhap et al., 2007;Choi et al., 2010). Finally, NDRG4, like Bves, is a colorectal cancer tumor suppressor (Melotte et al., 2009). Owing to these functional similarities, the relationship between Bves and NDRG4 was analyzed in greater depth.
We demonstrate that Bves/NDRG4 protein function provides the molecular underpinning for two critical and previously unlinked cell behaviors in epicardial cells. First, our data determine that this interaction is essential for directional cell movement, as its disruption randomizes and accelerates cell migration. Next we demonstrate that Bves and NDRG4 regulate the deposition of internalized soluble fibronectin for resecretion through the autocrine extracellular matrix (ECM) recycling pathway (Sung et al., 2011). Of importance, a series of cell/ECM recombination studies reveals that directional cell movement is, in fact, dependent on fibronectin recycling and that Bves/NDRG4 interaction is essential in linking these two processes. Further, live-cell imaging using total internal reflectance fluorescence (TIRF) microscopy reveals that Bves/NDRG4 interaction mediates cell surface docking of VAMP3-positive recycling endo-failed to colocalize in established epicardial sheets ( Figure 2E, inset). Here NDRG4 localization was primarily cytoplasmic, whereas Bves localized to the cell cortex and plasma membrane. Fluorescence was not apparent in secondary antibody-only negative controls, demonstrating that the signal was the result of anti-Bves and anti-NDRG4 antibody activity (Supplemental Figure S1). Taken together, these data indicate that Bves and NDRG4 interact in epicardial cells. Colocalization in cells of motile morphology suggests that Bves and NDRG4 function in a capacity related to cell migration. within epithelial sheets (Wada et al., 2001;Osler et al., 2005), we examined the subcellular localization of Bves and NDRG4 under varied epicardial cellular conditions. Epicardial cells at low confluence are motile, with a mesenchymal morphology (Wada et al., 2003). In this setting, Bves and NDRG4 colocalized at the cell cortex, in extended cell processes ( Figure 2C, inset and intensity profile), and on intracellular, vesicle-like structures ( Figure 2D, digital zoom and white arrows). In other regions, nonoverlapping distribution of these proteins was also apparent. Of interest, the proteins In the SPOTs assay, 13-mer synthesized peptides from amino acid 115 to the end of the Bves protein were fused to a cellulose membrane, with 10-amino acid overlap between adjacent peptides. GFP-NDRG4 lysate binds to three consecutive Bves peptides on the cellulose membrane, peptides 63-65 (depicted in grid overlay), corresponding to Bves residues 307-316: HHFLRGSSST. (D) A domain from Bves residues 292-331, containing the Bv/N4-MBD identified in C, is cloned into the myc-3tag vector. n ≥ 4 assays/experiment. proteins ( Figure 3, A and B). The average band intensity from three blots normalized to cyclophilin indicated that the NDRG4 ( Figure  3A′), Bves ( Figure 3B′), and pooled oligo (Figure 3, A′ and B′) depletions were significantly and highly reduced relative to standard control treatments. In addition, Bv/N4-MBD was tested for interference with native Bves and NDRG4 binding. Bv/N4-MBD proteins overexpressed in epicardial cells exhibited cytoplasmic localization, consistent with native NDRG4 ( Figure 3C compared with Figure 2D). Single-pixel confocal analysis of all z-planes revealed that Bv/N4-MBD and native NDRG4 proteins colocalized in epicardial cells (Figure 3, D, cells, and E, single-pixel colocalization). Analysis of

Bves and NDRG4 interaction regulates epicardial cell movement
To ascertain whether Bves and/or NDRG4 regulated cell motility in epicardial cells, we disrupted protein expression by two independent assays: 1) delivery of small interfering RNA (siRNA) to deplete cells of either/both proteins, and 2) application of the Bves/NDRG4 minimal-binding domain construct (Bv/N4-MBD) to specifically disrupt the Bves/NDRG4 interaction. Both NDRG4 ( Figure 3A) and Bves ( Figure 3B) oligos depleted each protein, whereas partner protein expression was not significantly affected ( Figure 3, A-B′). As expected, pooling NDRG4 and Bves oligos visibly depleted both FIGURE 2: Bves and NDRG4 colocalize and interact in mammalian epicardial cells. (A) Bves and NDRG4 colocalize in trabecular cardiomyocytes (merge) and epicardial cells (box denotes digitally zoom inset) from E14.5 murine heart sections. Scale, 75 μm. (B) A column cross-linked to Bves antibody pulls down NDRG4 protein from epicardial cell lysate, whereas another antibody, MF-20, does not; n ≥ 4 pull downs. (C) Epicardial cells at low confluence exhibit Bves and NDRG4 colocalization in plasma membrane protrusions (box denotes digitally zoomed inset); graph depicts colocalization in confocal images (D). Punctate, vesicle-like colocalization of Bves and NDRG4 are apparent in highmagnification confocal images of epicardial cells with motile morphology (box denotes digitally zoomed inset; white arrows demarcate vesicle-like structure). (E) Bves/NDRG4 fail to colocalize in confocal images of epicardial cell sheets (box denotes inset of cell borders). Epi, epicardium; Myo, myocardium; optical slices, 1 AU. Colocalizations quantified by ImageJ intensity profiling, Bar, 10 μm or as marked.
migration of epicardial cells back to control levels ( Figure 3G). Of importance, transient coexpression of the Bves truncation mutant (myc-Bves275) lacking the NDRG4 interaction domain failed to rescue the number of randomly migrating cells ( Figure 3G). Finally, overexpression of two separate Bv/N4-MBD plasmid clones significantly increased transwell migration compared with controls ( Figure  3H), corroborating gene depletion studies. Taken together, these data demonstrate that Bves and NDRG4 interaction regulates undirected migration in epicardial cells.

Bves/NDRG4 interaction regulates epicardial cell directional persistence and fibronectin trafficking
Disruption of Bves/NDRG4 interaction increases random cell movement, but the underlying molecular and cellular mechanisms are not understood. To observe the specific motility defect impaired with loss of Bves and/or NDRG4 function, we treated epicardial cells with Bves and/or NDRG4, or standard control (SC) siRNA, plated on glass in complete medium, and imaged for 2 h at a frame rate of 1/min (SC, Supplemental Movie S1; Bves/NDRG4 depleted, Supplemental Movie S2; Bves depleted, Supplemental Movie S3; NDRG4 depleted, Supplemental Movie S4). Distance traveled, velocity, and directional persistence were quantified using ImageJ manual tracking analysis (Figure 4, A and B; demarcated by colored lines overlaid on movies and stills). Depleted cells exhibited significantly increased velocity and path lengths as compared with controls ( Figure 4C and average band intensities indicated that Bv/N4-MBD pulled down NDRG4-GFP significantly more than GFP-vector controls, despite higher expression of GFP-vector per 25 μg of Cos7 lysate (Supplemental Figure S2, A and A′; compare cyclophilin loading control to GFP expression in lysate). In addition, overexpression of Bv/N4-MBD interfered with the native interaction of Bves and NDRG4 protein as assessed by endogenous pull-down assay (Supplemental Figure S2B). Together these data demonstrate that Bves and NDRG4 protein levels can be significantly and predictably reduced with siRNA constructs. In addition, overexpressed Bv/N4-MBD interacts with native NDRG4 and interferes with endogenous Bves/ NDRG4 binding.
With successful depletion of protein levels, we assayed whether loss of Bves and/or NDRG4 affected epicardial cell migration. Knockdown cells were placed within a modified Boyden chamber in which serum chemoattractant was homogeneously distributed on both sides of the filter to promote random motility. siRNA depletion of Bves or NDRG4 significantly increased random movement of epicardial cells ( Figure 3F). This phenotype was rescued by stable coexpression of Bves or NDRG4 DNA lacking the 3′-untranslated region (3′UTR) oligonucleotide target sequences ( Figure 3F). Next we determined whether the Bves/NDRG4 minimal-binding domain regulates undirected epicardial cell movements. First, we expressed Bves siRNAs as in Figure 3F to elevate random migration. Transient coexpression of Bves full-length plasmid completely rescued undirected and defective vesicular trafficking, we tested whether fibronectin accumulated intracellularly in Bves-and/or NDRG4-depleted cells.
Immunofluorescence confocal microscopy revealed that large fibronectin-positive structures are indeed present in the cytoplasm of these cells ( Figure 5A; DiI lipophilic dye labels cell borders). Quantification by object analysis using ImageJ revealed that Bvesand/or NDRG4-depleted cells exhibited a twofold to threefold increase in the number, mean area, and intensity of fibronectin-positive structures compared with controls ( Figure 5, B and C). Similarly, application of Bv/N4-MBD (compared with GFP-vector transfection control) induced retention of Dylight-550 labeled fibronectin, which was supplemented in fibronectin-depleted medium ( Figure 5D). The size, number, and intensity of these internal fibronectin-positive compartments mirrored what was seen with endogenous fibronectin retention after siRNA knockdown ( Figure 5B). Immunostaining demonstrated that fibronectin accumulated in LAMP2-positive endosomal/lysosomal compartments ( Figure 5E) and small Rab11apositive endosomes ( Figure 5F). Together these data demonstrate that disruption of Bves and NDRG4 function impairs endocytic trafficking of fibronectin.

Fibronectin substrate rescues Bves and NDRG4 migration and trafficking defects
It was previously demonstrated that migration defects in cells deficient in autocrine ECM production can be fully rescued by Supplemental Figure S3A). An overlay of the progressive paths of 10 representative cells visually demonstrates that disruption of Bves and/or NDRG4 function randomizes cell movement ( Figure 4D). Of interest, quantitation of the data revealed that Bves-depleted, NDRG4-depleted, or codepleted cells had strikingly poor directional persistence ( Figure 4E). Directional persistence is calculated by dividing the total displacement by the distance traveled; it is measured on a scale of 0 to 1, where 0 is random movement and 1 is movement in a straight line (Harms et al., 2005). Treatment of epicardial cells with Bv/N4-MBD also significantly impaired directional persistence, mimicking siRNA knockdown results. Distance traveled and velocity were also statistically significantly increased, albeit more modestly than with protein deletion (Supplemental Figure S3, C and D).
In addition to impaired cell movements, time-lapse differential interference contrast imaging demonstrated that Bves-and/or NDRG4-depleted cells accumulated large cytoplasmic vacuoles ( Figure 4B, yellow arrow) that originated at the plasma membrane and rapidly traveled toward the nucleus (exemplified in Supplemental Movies S2 and S3). Because vesicular enlargement is generally associated with membrane-trafficking defects (Neufeld et al., 2004), this finding suggested that presence of Bves and NDRG4 proteins is necessary for regulation of vesicular transport. Our previous data demonstrate that vesicular trafficking of exogenous fibronectin present in the environment influences cell movement (Sung et al., 2011). Given the current phenotype of randomized cell movement and codepleted cells were plated on glass coverslips coated with 10 μg/ml fibronectin and assayed by live imaging as in Figure 4 (SC, Supplemental Movie S5; Bves/NDRG4 depleted, supplying high concentrations of exogenous ECM substrate (Sung et al., 2011). Thus, to determine whether Bves and NDRG4 regulate fibronectin recycling, Bves-depleted, NDRG4-depleted,

Bves and NDRG4 regulate autocrine ECM deposition
Our data suggest that Bves and NDRG4 regulate cell movement through fibronectin recycling. To test the functionality of ECM produced by control and Bves/NDRG4-depleted cells, we isolated autocrine-produced, cell-free ECM from each of the cell types by removing confluent cell monolayers with 20 mM ammonium hydroxide (Sung et al., 2011). Fresh Bves/NDRG4-depleted or SC-treated cells were then plated on these cell-free matrices and monitored for directional persistence for 2 h. On ECM produced by control cells, newly plated epicardial cells moved directionally whether cells were treated with SC or Bves/NDRG4 siRNA (Figure 7, A and A′; SC cells, Supplemental Movie S9; Bves/NDRG4-depleted cells, Supplemental Movie S10). This is similar to results obtained from cells plated on exogenous fibronectin substrates ( Figure 6B). In contrast, only control cells moved in a directionally persistent manner on ECM produced by Bves/NDRG4-depleted cells (Figure 7, A and A′; SC cells, Supplemental Movie S11; Bves/NDRG4-depleted cells, Supplemental Movie S12), whereas Bves/NDRG4-depleted cells moved randomly, phenocopying their behavior on matrix-free glass ( Figure 4B).
In this experiment, soluble fibronectin was present in the medium in both experimental conditions. Thus a possible explanation for the efficient migration of control cells on matrix produced by Bves/NDRG4-depleted cells is that control cells can dynamically Supplemental Movie S6; Bves depleted, Supplemental Movie S7; NDRG4 depleted, Supplemental Movie S8). Of interest, Bvesand/or NDRG4-depleted cells migrated with strong directional persistence equivalent to or improved beyond SC levels when plated on a fibronectin substrate, unlike the randomized movements observed on glass (Figure 6, C and E, and Supplemental Figure S3B). In addition, Bv/N4-MBD was applied to epicardial cells, and migration was assessed on fibronectin substrate. Similar to gene-depletion studies, Bv/N4-MBD-induced directional persistence defects were completely rescued by fibronectin substrate (Supplemental Figure S3C). Intriguingly, distance and velocity measurements in NDRG4-depleted and codepleted cells were only partially rescued by plating on exogenous fibronectin, whereas migration distance and velocities were not rescued at all in Bv/N4-MBD-expressing cells (Figures 6C and Supplemental Figure S3D). These data suggest that secretion of matrix dependent on Bves, and NDRG4 is specifically responsible for directional persistence, whereas other migration characteristics either may depend on different secretory cargo or require alternative Bves and/or NDRG4 functions. Overall, exogenously applied fibronectin matrix rescued directionality, suggesting that a key function of the Bves-NDRG4 interaction may be to regulate fibronectin recycling to facilitate directional movement. a directionally persistent manner on matrix produced by SC cells, as dynamic ECM secretion of internalized fibronectin was unnecessary on a well-formed matrix ( Figure  7B). Conversely, in the absence of soluble fibronectin, control cells were unable to migrate persistently when plated on matrix produced in the absence of Bves and NDRG4 function (Figure 7, B and B′; SC cells on SC ECM, Supplemental Movie S13; SC cells on Bves/NDRG4-depleted ECM, Supplemental Movie S14). Thus the presence of fibronectin in the medium is required for control cells to overcome deficiencies in matrix produced by Bves/NDRG4-depleted cells (compare Figure 7, A′ and B′). These data demonstrate that autocrine ECM deposition is necessary for persistent epicardial cell movement. Loss of cellular Bves and NDRG4 function disrupts this process.
We next directly tested whether Bves and NDRG4 regulate fibronectin internalization and/or deposition. Biotin-labeled fibronectin was added to the medium of control and Bves/NDRG4-depleted epicardial cell sheets after 24 h of culture in fibronectin-free medium. After 18 h, cells were removed from the plate as before, and the amount of biotin-labeled fibronectin present in the resulting autocrine-deposited ECM was quantified by streptavidin-horseradish peroxidase (HRP) enzyme-linked immunosorbent assay. Absorbance measurements demonstrated that Bves/NDRG4-depleted epicardial cells deposited fourfold less fibronectin than controls ( Figure 7C). To visualize directly whether epicardial cells internalized fibronectin, we pulsed cells grown in fibronectin-depleted medium with DyLight 550-labeled fibronectin for 1 h (Figure 7, D and E). Under these conditions, internalized DyLight 550 fibronectin localized to vesicular compartments and colabeled with Bves and NDRG4 (Figure 7, D and E, respectively). Taken collectively, our studies demonstrate that internalization of soluble fibronectin into vesicles proceeds in the absence of Bves and NDRG4. Resecretion of this ECM component depends on their function in transport vesicles to promote directionally persistent migration.

Bves and NDRG4 mediate plasma membrane fusion of endosomal compartments
The present data demonstrate that Bves and NDRG4 mediate a critical cellular behavior, autocrine fibronectin recycling. In addition, Bves was previously implicated in exocytosis of many other cargoes (Hager and Bader, 2009). Because Bves is an integral plasma membrane protein, a likely mechanism regulated by Bves is vesicle docking, potentially via its binding partner VAMP3. Thus we tested whether internalize and resecrete fibronectin present in the medium for use as a motility substrate. To test this hypothesis, we replated cells on matrix deposited by control or Bves/NDRG4-depleted cells. Motility assays were then performed in medium depleted of fibronectin (prepared as described by Danen, 2002). SC-treated cells moved in FIGURE 7: Bves and NDRG4 coregulate fibronectin deposition. Matrix deposition is probed using a cell-free ECM assay, which removes cells but keeps the ECM. Migration of newly plated cells is then tracked as in Figures 4 and 6 on previously produced ECM to test the function of residual matrix. (A) On control-produced ECM, control and Bves/NDRG4-depleted epicardial cells move directionally (A′). On Bves/NDRG4-depleted ECM, control cells move directionally while Bves/ NDRG4-depleted cells have poor directional persistence (A′; *p < 0.01). (B) Control epicardial cells fed medium lacking fibronectin move directionally on control ECM and fail to move directionally (B′) on Bves/NDRG4-depleted ECM (*p < 0.01). This differs from A, in which controls overcome Bves/NDRG4-depleted ECM deficiency. (C) Biotin-labeled fibronectin deposition onto a glass surface is impaired in Bves/NDRG4-depleted cells compared with controls (n = 7 assays, **p < 0.001). (D, E) Epicardial cells internalize DyLight-550-labeled fibronectin (DyLight-FN) after a 1-h pulse. Endogenous Bves (D) and NDRG4 (E) colocalize with DyLight-FN on vesicular structures; boxes in merge denote digitally zoomed region. Optical slices, 1 AU; scale, 10 μm; n ≥ 50 cells/assay. plasma membrane). In control cells, visualized best in real-time movies, VAMP3-GFP vesicles were numerous at the basal surface ( Figure 8, A-A′′; SC, Supplemental Movie S15; quantification in Figure 8C). Alternatively, Bves/NDRG4-depleted cells exhibited significantly decreased presence of VAMP3-GFP vesicles in this region ( Figure  8, B-B′′ and C; Bves/NDRG4 depleted, Supplemental Movie S15). Similar experiments, in which epicardial cells were cotransfected with VAMP3-GFP and vector control or Bv/N4-MBD plasmids, demonstrated significant loss of VAMP3-GFP at the basal surface, with disruption of Bves/ NDRG4 binding ( Figure 8D). In addition, quantification of dwell times (separated into 1-s bins, monitored up to 7 s) indicated that many VAMP3-GFP vesicles resided at the basal surface for prolonged periods in controls, which is indicative of membrane docking (Malarkey and Parpura, 2011). Of importance, the dwell times of vesicles in Bves/NDRG4-depleted cells were significantly reduced compared with controls (between 1 and 3 s, with the majority ≤2 s). This demonstrated that vesicles in Bves/ NDRG4-depleted cells failed to dock at the cell surface ( Figure 8E). Similarly, cotransfection of Bv/N4-MBD and VAMP3-GFP in epicardial cells led to impaired dwell times compared with Myc-3tag vector-transfected controls (most resided at membrane between 1 and 2 s; Figure 8F). Taken together, these data indicate that the Bves/ NDRG4 interaction mediates docking of VAMP3-labeled vesicles.

DISCUSSION
Here we identify a critical protein/protein interaction between Bves and NDRG4 that influences epicardial cell movements. Careful characterization of Bves/NDRG4 interaction using multiple, complementary methodologies revealed an overlapping function. Bves and NDRG regulate directional persistence in migratory cells to mediate directionality of these movements. Cell/matrix recombination techniques determined that Bves and NDRG4 facilitate directional movement by regulating autocrine fibronectin recycling as a motility substrate. Finally, TIRF microscopy revealed that Bves/NDRG4 interaction regulates fusion of cell surface-bound vesicles. Together these findings explain how delivery of plasma membrane-bound and secreted cargoes are impaired with Bves loss of function. We propose that the Bves complex is a pivotal regulator of cell migration and adhesion by mediating cell surface cargo delivery of functional components. In this way, Bves may have broad influence on many cellular behaviors affecting development, repair, and cancer.  Although NDRG4 is known to affect cell migration in cancer and other cell lines (Nishimoto et al., 2003;Melotte et al., 2009;Wang and Hill, 2009) and delivery of intracerebral neurotrophic factors in mice (Yamamoto et al., 2011), the present study is the first to identify the mechanism by which NDRG4 directs this behavior. Collective data suggest that NDRG4 regulates trafficking in multiple contexts. Of interest, other NDRG-family proteins also participate in subcellular trafficking. NDRG1 is required for E-cadherin recycling, as well as myelin sheath maintenance and APO AI/AII lipid trafficking in Schwann cells (Hunter et al., 2005;Berger et al., 2006;Kachhap et al., 2007;King et al., 2011). NDRG2 is necessary for interleukin-10 secretion (Choi et al., 2010). A caveat of the present study is that NDRG1-3 may also participate in Bves-dependent fibronectin recycling mechanisms. Thus we speculate that some overlap may exist, although other NDRG proteins were not identified in our split-ubiquitin screen. This is an exciting avenue for future research, as it would further generalize the Bves trafficking function (Melotte et al., 2010).
Of interest, NDRG4 binds Bves in a domain that is not present in the other Popdc family proteins. The three members of the Popeye gene family contain many alternative splice variants for each family member and are tissue specifically enriched (Hager and Bader, 2009). A diverse array of trafficking events has been credited to Popeye gene products. It is possible that Popeyefamily gene products vary in order to facilitate trafficking of different binding partners, as needed by the cellular context. Indeed, we observed that specifically disrupting Bves/NDRG4 interaction was relevant only to cell directionality and not to migration rate/ distance. Because other Bves binding partners such as guanine nucleotide exchange factor T (Smith et al., 2008) also regulate cell migration, we speculate that the array of splice variants could govern individual cellular behaviors by binding to different Bves interaction partners.

Fibronectin recycling supports cell directionality
The present data demonstrate that autocrine fibronectin recycling supports directionally persistent cell movement. One possible explanation for the requirement of ECM in promoting persistent motility is that spontaneous cell polarization facilitating cell migration is frequently unstable. Thus it might be stabilized by adhesion formation at the leading edge of cells by newly secreted ECM. Consistent with this idea, Bves inhibition results in lamellipodial and cell polarization defects (Hager and Bader, 2009;Hager et al., 2010). At lower matrix concentrations or in a complex ECM environment (e.g., in vivo), direct deposition of ECM at the leading edge of cells might be the most efficient mechanism to stabilize newly formed lamellipodia and thus strengthen cell polarization within a brief time frame. Bves and NDRG4 have emerged as critical regulators linking cell autocrine ECM deposition and directional movement.
Directed cell migration, autocrine ECM deposition, and the epicardium Epicardium produces fibronectin, which is abundant subjacent to its epithelial component, and adherence of epicardial integrin receptors to these substrates is critical for coronary artery formation (Manasek, 1969;Kalman et al., 1995;Kwee et al., 1995;Yang et al., 1995;Sengbusch et al., 2002;Nahirney et al., 2003). The present study identifies a subcellular trafficking mechanism that facilitates fibronectin recycling and links this property to epicardial cell movement. These studies require important follow-up analyses using other in vitro cell systems and in vivo models. Yet we propose that autocrine fibronectin recycling supports de novo matrix production

Bves and NDRG4 regulate overlapping cellular behaviors
Multiple independent studies demonstrated that loss of Bves or NDRG4 functions results in disruption of cell movement. For example, morpholino Bves knockdown impairs lamellipodial formation in Xenopus head mesoderm and NIH-3T3 cells (Smith et al., 2008;Hager et al., 2010), and expression of antisense NDRG4 RNA inhibits neurite outgrowth in PC12 neuronal cell lines (Ohki et al., 2002). We determined that Bves is an essential regulator of persistent directional migration. Depletion of NDRG4 or inhibition of the Bves/ NDRG4 interaction also randomizes cell movement. These findings may explain the previously reported errors in morphogenesis and wound healing seen with Bves loss of function, where directed cell movement is required for gastrulation and wound closure (Ripley et al., 2004(Ripley et al., , 2006. In addition, Bves and NDRG4 are hypermethylated and down-regulated in multiple cancer cell types (Feng et al., 2008;Melotte et al., 2009;Schilling et al., 2009;Kim et al., 2010;Williams et al., 2011), and overexpression of either gene inhibits migration in related cancers (Melotte et al., 2009;Williams et al., 2011). The present data suggest a mechanism through which these gene products mediate directed migration in these contexts. Future studies will identify the specific role(s) for autocrine ECM deposition in these diverse in vivo settings.

Bves and NDRG4 are essential for autocrine fibronectin recycling
Bves is important in the trafficking of cell/cell and cell/matrix adhesion molecules to the cell surface through interaction with diverse binding partners (Hager and Bader, 2009). Here we determined that Bves and NDRG4 specifically mediate movement of fibronectin via resecretion of endocytosed ECM. The initial uptake of fibronectin is not inhibited with loss of Bves or NDRG4 function. Indeed, large fibronectin and LAMP2 (late endosome) and Rab11 (recycling endosome) double-positive compartments are found in the cytoplasm of Bves-and/or NDRG4-depleted cells. This suggests that Bves and NDRG4 function downstream of internalization to promote fibronectin secretion from either late or recycling endosomes. Vesicle fusion assays demonstrate that inhibiting Bves/NDRG4 interaction specifically disrupts VAMP3-positive vesicle docking to the cell surface. Furthermore, Bves/NDRG4 disruption results in a reduction in biotin-labeled fibronectin moving from the medium to the insoluble matrix. Together these data confirm that cargo delivery requires Bves-and NDRG4-mediated vesicle fusion.
Of interest, Bves binds to VAMP3, a vesicle-SNARE protein regulating endosome-plasma membrane tethering. Bves does not regulate VAMP3 expression levels (Proux-Gillardeaux et al., 2005;Hager et al., 2010). This interaction was important for β1-integrin trafficking and cell-substrate adhesion. Our new data show that both Bves and NDRG4 regulate delivery of VAMP3-labeled vesicles. Collective studies suggest that fibronectin and its integrin receptors are recycled in the same endosomal pathway to promote efficient cell motility. Of note, other groups observed that fibronectin bound to α5β1-integrin traffics into late endosomal/ lysosomal compartments, demonstrating that they are cotrafficked in other systems (Lobert et al., 2010;Dozynkiewicz et al., 2011). Of importance, in studies on cortactin, fibronectin trafficked through a late endosomal/lysosomal compartment before exocytosis (Sung et al., 2011). Together these data implicate Bves and binding partners as critical components of the fibronectin/β1-integrin exocytic mechanism. Although autocrine ECM deposition has been observed in cancer cells, mouse embryonic fibroblasts, and, now, epicardial cell lines, further studies in additional models are required to generalize this mechanism.
LSM150 inverted confocal microscopes, (Carl Zeiss, AG, Jena, Germany), and the Delta Vision Platform (Applied Precision, Issaquah, WA). All microscopy was done in conjunction with the Cell Imaging Shared Resource and Epithelial Biology Center Imaging Resource of Vanderbilt University Medical Center.

Split ubiquitin screen
A split ubiquitin screen of murine Bves (NM_024285) against a mouse heart library was performed as previously described (Hager et al., 2010). Murine NDRG4 transcript A (NM_001195006.1) was cloned into the pPR3-N vector and passed all nutrient drop-out selection tests against Bves.

Coimmunoprecipitation, GST pull down, and native immunoprecipitation
For coimmunoprecipitation, Bves, cloned into the pCMVmyc-3tag-4A vector (Agilent Technology, Santa Clara, CA), and NDRG4, cloned into the pEGFP-C1 vector (BD Biosciences), were exogenously expressed in COS-7 monkey kidney cells (ATCC, Manassas, VA) with Lipofectamine 2000 (Invitrogen Life Technologies). Lysates were harvested as previously described (Hager et al., 2010) and coimmunoprecipitations performed per manufacturer's instructions using Profound c-Myc Tag IP/Co-IP Application Set (Thermo Scientific Pierce, Rockford, IL). Bves-GST truncation pull downs of NDRG4-GFP expressed in COS7 cells were performed as previously described (Smith et al., 2008). For native immunoprecipitation, lysates from 10-cm plates of epicardial cells were pooled in M-PER Mammalian Protein Extraction Reagent (Thermo Scientific Pierce). Using BS 3 , 20 μg of Bves SB3 or MF-20 antibodies were cross-linked to protein G Magnetic Dynabeads (Invitrogen Life Technologies). Dynabeads were washed in PBS-T, pH 7.4, and 250 μl of epicardial cell lysate was applied and rocked at 4°C for 2 h. Bound proteins were eluted in 1× Laemmli buffer and probed with NDRG4 antibodies via Western blotting, which was exposed with standard methodology (Hager et al., 2010).

SPOTs analysis
A SPOTs membrane designed to the Bves C-terminus (residues 115-358) was synthesized (Sigma Genosys, Woodlands, TX), with each 13-mer peptide having a 10-amino acid overlap with neighboring peptides. NDRG4-EGFP or EGFP vector control lysates were exogenously expressed in COS7 cells, and lysates were collected using M-PER buffer. Standard bicinchoninic acid assays (Thermo Scientific Pierce) determined total protein concentration, and the SPOTs membrane was prepped and incubated in lysate as previously described (Kawaguchi et al., 2008). The membrane was incubated with GFP antibodies overnight at 4°C. Antibody binding was visualized using standard chemiluminescence methodology, and the membrane was regenerated using dimethylformamide as previously described (Kramer and Schneider-Mergener, 1998). The region surrounding the minimal-binding Bves DNA sequence (BC132044.1; residues 292-330) was cloned into the pCMV-3tag-4a vector (#240198; Agilent Technologies), and in-frame ligation was confirmed via PCR, restriction enzyme dropout of the insert, and sequencing (in conjunction with the Vanderbilt University DNA Sequencing Facility). Overexpression of 10 μg of two separate clones was performed with nucleofection as described later. Expression efficiency and colocalization were ascertained by analyzing images gathered with the Delta Vision platform and confocal microscopes.
to mediate epicardial sheet migration during the complex tissue interactions that regulate heart development. Thus, although the epicardium is noted for synthesis of ECM components, the mechanism of matrix deposition identified here cannot be overlooked in ongoing analyses of cell movement in development, disease, and repair.

MATERIALS AND METHODS Quantification
For the assays described here, statistics were performed in Excel 2011 (Microsoft, Redmond, WA). The error bars represent SEM. Standard Student's t tests were used to determine significance. All compared images were equivalently adjusted in Photoshop CS5 (Adobe, San Jose, CA). For intensity profile quantification, the Im-ageJ straighten program was used to follow structures at a width of five pixels; these pixels were averaged for fluorescence intensity, and each channel was plotted together in Excel versus distance (micrometers). Progressive paths of epicardial cells after gene manipulation were gathered using ImageJ Manual Tracking and images were compiled in Photoshop.

Mice
C57Bl/6J mice were kept in 12-h light/dark cycles with ad libitum access to standard chow diets and water. Five wild-type dams were mated naturally, and plug dates were recorded. Dams were killed at embryonic day (E) 14.5, and embryonic hearts of offspring were fixed in paraformaldehyde overnight at 4°C. Hearts were cryoprotected in a sucrose gradient and then flash frozen in OCT (Tissue-Tek Sakura Finetek, Torrance, CA). Frozen hearts were sectioned in 10-μm slices. Hearts were rehydrated in phosphate-buffered saline (PBS), permeabilized with 0.25% PBS-TX100, blocked in 10% normal goat serum, and incubated with Bves and NDRG4 antibodies using standard methodologies. Images were captured with Leica TCS-SP5 confocal microscope system (Leica Microsystems, Solms, Germany). All experiments performed with murine models followed Institutional Animal Care and Use Committee guidelines.

siRNA knockdown and rescue
Oligos were designed against the 3′UTR of Bves and NDRG4 (Sigma Custom Oligos, St. Louis, MO; Supplemental Figure S4). Nucleofection using the Nucleofector II Device (Lonza, Basel, Switzerland) was optimized in epicardial cells with a Cell Line Optimization Kit (Lonza VCO-1001N). Briefly, 5 × 10 5 epicardials in nucelofection solution L using nucleofector program A-033 had the highest transfection efficiency of pMAX-GFP DNA (>70%) and highest viability (>80%). Using this protocol, we applied three pooled Bves and/or NDRG4 siRNA oligos to epicardial cells at 100 nM each, as well as standard control oligos (SIC001; Sigma). Cells were incubated for 48 h and collected, and protein concentration was normalized to expression of the housekeeping gene cyclophilin via Western blot. Rescue constructs of Bves and NDRG4 pCMV-Myc-3tag-4 vector (Agilent Technology) lacked the siRNA target sequences and were stably expressed in epicardial cells via nucleofection, followed by 3 wk of G418 treatment. Cells were clonally sorted at the Nashville VA Medical Center Flow Cytometry Core, expanded, and then assayed with a Boyden chamber, as described earlier. Alternatively, Bves full-length or Bves-275 truncation constructs in pCMV-Myc-3tag-4 vector were transiently cotransfected with siRNA and assayed.

Live-imaging assays and analysis
Epicardial cells siRNA depleted for SC, Bves, and/or NDRG4 plasmid or treated with GFP-vector and CMV-myc-3tag-4 vector or Bv/ N4-MBD were incubated for 24 h in standard medium and then split (5000 cells) into glass-bottom, 35-mm dishes (MatTek, Ashland, MA) either uncoated or coated in 10 μg/cm 2 fibronectin (F4759; Sigma) and incubated for 2 h. To monitor cell movements, differential interference contrast images were acquired with the DeltaVision Platform (Applied Precision) in a temperature-, humidity-, and CO 2controlled weather station. Images were taken every 60 s with autofocus every 30th image. The movies were analyzed for distance, velocity, and directional persistence using ImageJ Cell Tracker software.

Cell-free ECM assays
Control or Bves/NDRG4-knockdown epicardial cells were seeded at 1 × 10 6 on MatTek dishes or 5 × 10 5 /well of a four-well glass chamber slide (Thermo Scientific) and grown to confluence (48 h). A cell-free ECM was prepared by applying 20 mM ammonium hydroxide to the cells with vigorous rocking for 5 min. The cellular debris was washed away with H 2 O (1×) and PBS (3×) for 5 min each, again with vigorous rocking. As a measure of properly produced ECM, directional persistence was compared between control cells in complete medium, Bves-and NDRG4-knockdown cells in complete medium, and control cells in fibronectin-depleted medium as a negative control, using the described live-cell directional persistence assay.

Fibronectin medium depletion and fibronectin internalization/deposition assays
To deplete the medium of fibronectin, FBS was purified over a disposable column using Gelatin Sepharose 4B beads (GE Healthcare, Fairfield, CT) according to the manufacturer's protocol and assayed for fibronectin depletion via Western blot. Gene-depleted cells maintained on glass MatTek dishes were fibronectin starved for 24 h. DyLight550 NHS ester-(Thermo Scientific Pierce) or sulfo-NHS-LC-LC-biotin-labeled fibronectin was incubated at 10 μg/ml for times specified in the text and as previously described (Sung et al., 2011). Cells were washed 2× with complete medium and 3× with PBS for 5 min each and then were processed for immunofluorescence as before. For DiI labeling experiments, epicardial cells seeded at very low confluence on glass or 10 μg/cm 2 fibronectin were covered with 200 μl of Fast DiI (Molecular Probes; 5 μl DiI/1 ml 10% FBS DMEM), incubated for 35 min at 37°C, fixed with Formalin for 25 min, and processed for antibody labeling as described. For biotin-labeled deposition experiments, confluent fibronectin-starved cell sheets on 35-mm plastic dishes (Corning, Corning, NY) were fed biotin-FN for 48 h and a cell-free assay was performed as described earlier.
Exposed ECM was conjugated to streptavidin-HRP overnight at 4°C (Thermo Scientific). The plates were washed with Tris-buffered saline/Tween 20, pH 8.0, 4× for 10 min each. Cells were exposed to TMB substrate (Thermo Scientific Pierce) for 25 min. Reactions were stopped with 2 M sulfuric acid and absorbance read per manufacturer's protocol.

Vesicle fusion docking assay
Epicardial cells transfected with standard control siRNA, Bves/ NDRG4 siRNA, CMV-myc3tag-4 vector plasmid, or Bv/N4-MBD plasmid and VAMP3-GFP plasmid were plated on MatTek dishes for imaging. TIRF microscopy was performed using a Nikon TiE inverted microscope (Nikon,Tokyo, Japan) equipped with a Nikon TIRF illuminator and a 100×/1.49 numerical aperture objective in combination with a 1.5× Optivar (75 nm/pixel). Images were acquired at 30 frames/s using a Hamamatsu ImageEM-CCD camera driven by MetaMorph software (Molecular Devices, Sunnyvale, CA). ImageJ software was used to quantify the number or particles per movie frame, and SpeckleTrackerJ plug-in was used to quantify particle dwell times.