extfieldsofstudy: Biology Medicine

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Structure of the periplasmic adaptor protein from a major facilitator superfamily (MFS) multidrug efflux pump

Highlights • Periplasmic adaptors are key to MFS-dependent tripartite pump efflux.• We present the structure of Aquifex aeolicus EmrA, an MFS pump adaptor.• The adaptor has an extended 127 Å long α-helical coiled-coil and disordered β-barrel loop.• The inner membrane transporter interacting membrane proximal domain is absent in EmrA.• This extends the modular view of adaptors, mediating diverse pump assembly.

Introduction
Tripartite efflux pumps expel a wide range of noxious molecules, including antibiotics, metals, detergents and bile salts from Gram-negative bacteria such as Escherichia coli and Pseudomonas aeruginosa, and they are major drivers of the increasing threat of multiple antibiotic resistance [1,2]. Efflux substrates bind to an inner membrane (IM) transporter, e.g. in E. coli an ATPase like MacB or a proton antiporter like the RND (resistance nodulation division) AcrB or MFS (major facilitator superfamily) EmrB, and are delivered to the outer membrane (OM)-anchored TolC exit duct, the entrance to which projects into the periplasm [1][2][3]. In all pumps, an essential third component is the periplasmic adaptor (e.g. E. coli AcrA or EmrA), shown by extensive in vivo cross-linking and multidomain docking of the E. coli AcrA-AcrB-TolC RNDdependent pump to establish and stabilise interactions with both TolC and AcrB [4][5][6]. Structural analyses have indicated that variation among pumps is underpinned by a flexible, linearly arranged, multidomain adaptor architecture [5,[7][8][9][10][11], in which three b-sheet domains -lipoyl, b-barrel and membrane proximal -interact with the 70 Å periplasmic extension of RND IM transporters, while a fourth domain, an a-helical hairpin, establishes extensive coiled-coil interactions with the periplasmic a-helical barrel of TolC [1,4,5]. The recent finding that the a-hairpin is completely absent from an adaptor of the spirochete Borrelia burgdorferi emphasizes that adaptor variation can be substantial in effecting assembly of different pumps [10].
The structure of the closely conserved TolC exit duct has been known for 14 years [3], but an understanding of EmrAB-TolC assembly and operation requires structural information for the IM and periplasmic pump components. Here we present the structure of MFS adaptor EmrA from A. aeolicus, revealing features that appear specific to the MFS efflux pumps.

Expression of soluble Aquifex aeolicus EmrA protein
To produce native, soluble Aquifex aeolicus EmrA (aaEmrA), E. coli SoluBL21 cells bearing pET24-aaEmrADTM (see Supplemental data for cloning methods) were grown at 30°C in 2xTY medium containing 50 lg ml À1 kanamycin to A 600 0.6, when 0.5 mM IPTG was added for 16 h at 18°C. Cells harvested by centrifugation were resuspended in 50 mM Tris pH 7.4, 400 mM NaCl, 5% glycerol, 10% MgCl 2 supplemented with EDTA-free protease inhibitor (Roche). Cells were broken by two 30 000 psi passages through a cell disruptor. After centrifugation at 150 000Âg for 1 h at 4°C, supernatant was incubated with Profinity IMAC resin (Biorad) and 4 mM imidazole for 1 h at 4°C. Resin was washed in buffer A (25 mM HEPES pH 7.5, 400 mM NaCl, 4 mM imidazole) containing 0.1% Triton X-100, then in buffer A alone. Protein was eluted in 25 mM HEPES pH 7.5, 200 mM NaCl and 400 mM imidazole. Imidazole concentration was reduced to 10 mM using an Amicon 10kDA molecular weight cut-off concentrator (Millipore). Final protein concentration was 7 mg ml À1 . Selenomethionine-incorporated EmrA was produced using a metabolic inhibition protocol [28]. E. coli SoluBL21 (DE3) (Genlantis) cells bearing pET24-EmrADTM plasmid were grown at 37°C to A 600 0.5 in M9 minimal media supplemented with 50 lg ml À1 kanamycin, 0.2% glucose, 2 mM MgSO 4 , 0.1 mM CaCl 2 and 0.001% thiamine. At this point 100 mg L À1 threonine, lysine and phenylalanine, 50 mg L À1 leucine, isoleucine and valine and 60 mg L À1 selenomethionine were added and cells grown for a further 45 min. 0.5 mM IPTG was then added for 16 h at 18°C. Protein was purified as native with the addition of 1 mM TCEP to all buffers. Incorporation of 2 selenomethionine residues was confirmed by mass-spectrometry. The ecEmrA protein lacking the TM domain was produced from plasmid pET24-ecEmrADTM and purified as native aaEmrA.

Crystallisation, structure determination and analysis
Crystallisation of aaEmrA was conducted using sitting drop vapour diffusion at 15°C in CrysChem 24 well plates (Hampton Research). Drops were formed by mixing 2 ll of protein solution with 2 ll of crystallisation reagent (100 mM MES pH 6.5, 100 mM MgCl 2 , 10% isopropanol, 8% PEG4000) and equilibrated against 500 ll. Crystals grew to maximum size (0.8 mm Â 0.2 mm Â 0.2 mm) in seven days, and were cryoprotected by stepwise addition of cryoprotectant (100 mM MES pH 6.5, 50 mM MgCl 2 , 5% isopropanol, 10% PEG4000, 42.5% MPD), before being looped and flash-frozen in liquid nitrogen. Diffraction data were collected at 100 K on beamline I24 (Diamond Light Source, UK). X-ray data sets were indexed and integrated using XDS [29] and scaled using Aimless in the CCP4 suite [30]. Crystallographic phases for the 3.5 Å selenomethionine dataset were obtained by the Single-wavelength Anomalous Dispersion (SAD) method, with two selenium sites identified using ShelxD [31]. Sites were refined and an initial 3.5 Å resolution density modified map created using Autosharp. Density modification was then performed in Resolve using the isomorphous 2.85 Å high-resolution data, maintaining the same 'free' reflection list and using the initial SeMet sites and electron density map from the SAD data. An initial model of EmrA was built with AutoBuild in Phenix. The structure was completed with iterative rounds of manual model-building with Coot [32] and refinement in Phenix [33]. Structure validation was assisted by Molprobity [34] and Procheck [35]. The final model encompasses one molecule of EmrA (residues 27-321 and 343-373), 6 water molecules, 2 isopropanol molecules and a magnesium ion, with no Ramachandran outliers. The atomic coordinates and structure factors have been deposited in the Protein Data Bank (http:// www.rcsb.org/), PDB ID: 4TKO. Root mean square deviations (RMSDs) were calculated over C a atoms aligned using superpose in the CCP4 suite [36]. The colour scheme of conserved residues in Supplemental Fig. 4 was generated with CONSURF [37,38]. Figures were prepared using PyMol [39].

Crystallisation and structure determination of Aquifex aeolicus EmrA
Sequence analysis of EmrA proteins (360-423 amino acids in length, Supplemental Fig. 1) reveals the N-terminus consists of a short (6-32 amino acid), unconserved cytoplasmic domain preceding a TM helix, analogous to the AcrA lipid attachment, that anchors the adaptor in the IM. To facilitate structural studies, we cloned and expressed EmrA from E. coli and 5 homologues, engineered to lack the TM helix (see Supplemental Methods). Most either failed to crystallise or otherwise produced non-diffracting crystals, but we were able to solve the structure of Aquifex aeolicus EmrA (aaEmrA) from a construct encompassing residues 26 onwards of the 374 residue mature sequence. Native aaEmrA crystallised in space group I4 1 22 with a 78% solvent content and one molecule in the asymmetric unit. Phasing using molecular replacement with adaptor homologues was unsuccessful. Selenomethionine derivatised aaEmrA crystallised as native and phases were calculated using a 3.5 Å resolution single-wavelength anomalous diffraction (SAD) dataset (Table 1) and the positions of two methionine residues and an initial density modified map were determined. These were used as starting points to calculate the electron density map of an isomorphous 2.85 Å resolution dataset into which an atomic model could be built ( Table 1). The final refined model contains residues 27-321 and 343-373 with an R work and R free of 20.8 and 24.8, respectively. Residues 26, 322-342 and 374 could not be built due to poorly defined electron density.

EmrA lacks a membrane proximal domain present in adaptors from distinct pumps
The structure contains three linearly arranged domains, an ahelical hairpin, a lipoyl domain and a b-barrel domain (coloured blue, green and yellow in Fig. 1). While this domain organisation is similar to other structurally characterised adaptors, most obviously aaEmrA lacks the membrane proximal (MP) domain identified to date in all 6 RND and ATP-binding cassette (ABC) adaptor crystal structures [7][8][9][10][11]40]. Therefore, distinctively for periplasmic adaptors, the b-barrel is proximal to the IM, anchored via its N-terminal TM helix. Sequence alignment (Supplemental Fig. 1) of EmrA homologues from 13 diverse bacteria indicates the lack of an MP domain is not unique to A. aeolicus. Some EmrA adaptors have non-conserved C-terminal extensions (19 and 41 residues in V. cholerae VceA and ecEmrA, respectively) but these extra residues are insufficient to form an MP domain, which are usually 84-108 residues long. In fact, the C-terminal residues of ecEmrA are predicted to form a short a-helix [15], not the usual b-roll topology of MP domains. In addition, the N-terminal TM helix is directly attached to the b-barrel domain in all EmrA homologues, positioning this domain closest to the membrane and indicating the extra C-terminal residues do not form a fourth EmrA domain adjacent to the membrane.
In contrast to EmrA, the membrane attachment site in RND and ABC adaptors is located at the MP domain (e.g. an N-terminal lipoyl group in RND AcrA or an N-terminal TM helix in ABC MacA). In RND-dependent tripartite pumps, the MP domain is known to make extensive interactions with the periplasmic extension of IM transporters [5,41]. This MP domain would not be required for EmrA-EmrB interactions in MFS pumps as there is no such periplasmic extension of the IM EmrB transporter.

EmrA
The aaEmrA anti-parallel, two-stranded, a-helical coiled-coil domain is 168 residues and 127 Å long with 11 heptad repeats per helix. The N-terminal helix consists of residues 77-160, and the C-terminal helix residues 165-244 (Fig. 1). The coil is non-ideal due to positively charged residues in its hydrophobic core, i.e. K84, K98, R130, R182 and K119 reside in normally hydrophobic A and D heptad positions. In addition, the heptad repeat pattern is disrupted by a four residue insertion in the N-terminal helix (residues 112-115) and a three residue insertion in the C-terminal helix (residues 210-212), approximately 70 Å from the tip of the structure, and close to the centre of the coiled-coil. This disrupts the knobs-into-holes packing, resulting in a weakened inter-helical interaction in this region of the coil. Heptad shifts, also called ''stutters'' and ''stammers'', are usually found in a-fibrous proteins with an extended coiled-coil structure such as myosin and fibrinogen, where it is suggested they may serve as points of flexibility for extremely long coiled-coils [42,43]. The shifts here likely result in relative flexibility in this region, evident as high C a B-factors over the centre of the aaEmrA coiled-coil (an average of 124 Å 2 over residues 102-125 and 200-222 compared to 90 Å 2 over the entire a-hairpin and 71 Å 2 over the whole structure, Supplemental   Fig. 2A). The coil also contains a large number of lysine residues (21% over coil residues 77-244 compared to 14% over the full structure) resulting in a positive charge distribution (the electrostatic charge over the aaEmrA surface is presented in Supplemental  Fig. 2B), particularly evident in the centre of the coil (residues 119-136 and 184-202 contain 31% lysine residues) close to the heptad phase shifts. An alignment of structurally characterised adaptors to date (Supplemental Fig. 3) highlights the differences in the length of adaptor a-hairpins. The 127 Å long coiled-coil of aaEmrA is at least twice as long as the a-hairpins of the multidrug RND adaptors AcrA and MexA (58 Å and 47 Å long, respectively [11,40]), the heavy metal efflux RND adaptor ZneB (52 Å long [7]) and the drug efflux ABC adaptor MacA (67 Å long [9]) (Fig. 2). The length of the aaEmrA a-hairpin is even more pronounced when compared to the 27 Å long a-hairpin of the heavy metal efflux adaptor CusB, which contains an unusual three-helix bundle [8], and the recently published structure of BesA, an RND adaptor from the Spirochaete B. burgdorferi, which has no a-hairpin at all [10]. CusB therefore remains the only characterised adaptor that forms a more elaborate threestranded coiled-coil instead of the typical two-stranded coils found in all other adaptors, including EmrA. Primary sequence analysis of EmrA homologues (Supplemental Fig. 1) suggests the minimum EmrA a-hairpin length is 118 residues (e.g. ecEmrA), while coils similar in length to aaEmrA are predicted in pathogenic V. cholerae VceA ($146 residues/$115Å long) as well as Thermodesulfobium narugense ($130 residues/$95Å long).
Structural alignment of the aaEmrA adaptor a-hairpin with those from MexA, ZneB, AcrA, CusB and MacA results in low RMSDs (1.22-1.70 Å), with the hairpins superposed to the top half of the aaEmrA a-hairpin (residues 116-206) (Fig. 2). The a-hairpins of MexA and AcrA are known to bind to the cognate OM exit duct TolC [1], suggesting there is close structural topology in the TolC interacting region of adaptors, including EmrA. It is therefore likely the pseudo three-fold symmetry created by the coiled-coil interactions of TolC with RND adaptors [4,5] are also key to EmrA-TolC interactions, and suggests EmrA may function similarly to RND adaptors in stabilising TolC opening [1,5,44].

Closely conserved b-barrel and lipoyl domains
The lipoyl and b-barrel domains are the only two domains that are conserved in structurally characterised adaptors from different pumps and organisms (EmrA lacks an MP domain and Borrelia BesA lacks an a-hairpin, see Supplemental Fig. 3). The lipoyl domain is structurally similar in all adaptors including EmrA (RMSDs 1.24 Å, 1.19 Å, 1.70 Å, 1.04 Å, 1.22 Å, 1.29 Å for AcrA, BesA, CusB, MacA, ZneB and MexA, respectively). Likewise, the EmrA b-barrel domain (Fig. 3A) shares structural topology with the b-barrel domains of adaptors BesA, MexA, AcrA, CusB, ZneB and MacA (RMSDs 1.54 Å, 2.27 Å, 2.04 Å, 2.08 Å, 1.34 Å and 1.33 Å, respectively) (Fig. 3B). However, the aaEmrA b-barrel contains 21 disordered residues, unobserved due to a break in the electron density (residues 322-342). Modelling of this region indicates it can form a 23 Å long loop, approximately the same length as the b-barrel strands (Fig. 3C). In other adaptors the equivalent loop is less than half the size, typically 7 residues long, highlighted in the b-barrel superposition in Fig. 3D. Alignment of EmrA proteins from diverse bacteria reveals a number of highly conserved residues in this long loop (Fig. 3E), including strictly conserved residues F327, P331, G337 and K341 (aaEmrA numbering). The loop is one of the most conserved regions in the EmrA structure and is not seen in any of the other EmrA b-barrel loops (Supplemental Fig. 4) or in other adaptors, indicating it may have a functional role in MFS pumps.  (grey, 3LNN), E. coli CusB (purple, 3OOC) and E. coli MacA (yellow, 3FPP). Right, adaptors aligned with the RMSDs (in Å) of each adaptor ahairpin to EmrA, calculated using superpose in the CCP4 suite, indicated below the respective structure (number of aligned residues in brackets). N-and C-terminal residues are labelled.

No EmrA ligand binding detected by isothermal titration calorimetry or co-crystallisation
The initial observation that adaptor b-barrel topology is also found in domains involved in ligand binding prompted the idea adaptors may have a role in substrate engagement [40]. Indeed, this seems to be the case for the heavy metal efflux pump adaptors ZneB and CusB in which crystal structures have revealed Zn and Cu ions bound in the flexible linker region between the b-barrel and membrane proximal domain [7,8]. Substrate binding to ecEmrA lacking the TM helix was previously inferred from changes in the degree of iodide-induced quenching of intrinsic protein fluorescence upon addition of hydrophobic substrates nalidixic acid, CCCP or DNP [15]. Here, we used isothermal titration calorimetry (ITC) to investigate drug binding to constructs of ecEmrA or aaEmrA lacking the TM domain. The titration of 1 mM DNP and 10 mM nalidixic acid to 0.1 mM ecEmrA or aaEmrA did not result in a detectable interaction between substrate and adaptor (Supplemental Fig. 5). Neither could we observe any difference density consistent with bound substrates in co-crystal trials of 0.5 mM CCCP and 0.5 mM DNP with aaEmrA (data not shown). If EmrA does facilitate drug transfer through the tripartite pump by binding substrates as suggested [15], it is not detectable by ITC or crystallography.
3.6. Implications of the EmrA adaptor structure for assembly of the MFS-dependent pump Tripartite efflux pumps span both inner and outer membranes, providing a continuous seal for drugs to bypass the periplasm. Extensive in vivo cross-linking analyses have shown that in AcrAB-TolC a 170 Å long periplasmic seal can be provided by close fit of IM and OM components (AcrB and TolC, respectively) [6,45,46] stabilised by interactions with the adaptor (Fig. 4, left) [1,4,5]. In EmrAB-TolC, the inner membrane MFS component does not contain significant periplasmic structure, so it follows the seal would be provided by the adaptor and TolC a-barrel. If six EmrA molecules are modelled into a ring, formed by lipoyl and b-barrel interactions, and close side-to-side packing of the lower EmrA ahairpin regions (Fig. 4, right) that are equivalent to the upper TolC a-barrel, small gaps between b-barrel domains can only be sealed by modelling the disordered 21 b-barrel residues. In addition, the TolC-interacting regions of the a-hairpins naturally diverge in the . (E) Sequence alignment of EmrA homologues. Alignment was performed over the whole sequence, but for clarity only the region covering the disordered loop is shown. Sequences are from Aquifex aeolicus (aaEmrA), Escherichia coli (ecEmrA and ecEmrK), Vibrio cholerae (vcVceA), Stenotrophomonas maltophilia (smEmrA), Rhodobacter capsulatus (rcEmrA), Salmonella Typhimurium (stEmrA), Pseudomonas aeruginosa (paEmrA), Thermodesulfobium narugense (tnEmrA), Cupriavidus metallidurans (cmEmrA), Burkholderia pseudomallei (bpEmrA), Haemophilus influenza (hiEmrA), Neisseria gonorrhoeae (ngFarA). Strictly conserved residues are boxed in white on a red background and highly conserved residues are boxed in red on a white background. Secondary structure is indicated above and coloured by domain as Fig. 1. adaptor hexamer, precluding a tip-to-tip TolC-EmrA interaction. Only by docking the TolC open form [44] into the EmrA hexamer's upper half could we form a sealed periplasmic efflux channel, with a 225 Å periplasmic spanning distance (Fig. 4, right). While this length is similar to the cryo-electron microscopy model of the AcrAB-TolC complex formed by non-natural linked fusion proteins [47], it is longer than the in vivo data driven model of AcrAB-TolC (170 Å, Fig. 4, left) and the in vitro bipartite MexA-OprM cryotomography model (210Å) [1,5,48]. In our speculative EmrAB-TolC model the EmrA a-hairpin tips interact with the TolC periplasmic entrance coils, similar to previous in vivo and in vitro observations of adaptor-TolC coiled-coil interactions in RND-dependent tripartite machineries [4,5]. EmrA-TolC interactions would be aided by inter-domain movement around the adaptor's flexible linker regions [5,10,11,49], previously identified as key to tripartite pump assembly [1,5]. This hexameric ring of adaptors is also observed in the co-crystal structure of an adaptor-transporter complex (CusB-CusA [41]) and the in vitro cryo-electron microscopy based model of an AcrAB-TolC complex formed by linked fusion proteins [47], suggesting the seal in these systems could also be provided by a ring of 6 adaptors, stabilised by interactions through the b-barrel and lipoyl domains. As for adaptor-transporter interactions, the single TM helices of EmrA would seem likely to form substantial contacts with the predicted 14 TM helices of EmrB. Indeed, the putative ring of b-barrels from the six EmrA adaptors would have an internal diameter similar to the size of monomeric structurally characterised 14 TM MFS adaptors (oligopeptide transporters PepT So [25] and PepT St [23]), suggesting the single TM helices of the 6 adaptors could interact with monomeric EmrB by forming a 20-helix bundle in the IM. While we require further experimental evidence to support the model, this arrangement would contrast with the 'dimer-of-dimers' suggested by electron microscopy of purified EmrA and EmrB [26].


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Silencing of LINE-1 retrotransposons contributes to variation in small noncoding RNA expression in human cancer cells.

Noncoding RNAs are key players in the maintenance of genomic integrity, particularly in silencing the expression of repetitive elements, some of which are retrotransposable and capable of causing genomic instability. Recent computational studies suggest an association between L1 expression and the generation of small RNAs. However, whether L1 expression has a role in the activation of small RNA expression has yet to be determined experimentally.; Here we report a global analysis of small RNAs in deep sequencing from L1-active and L1-silenced breast cancer cells. We found that cells in which L1 expression was silenced exhibited greatly increased expression of a number of miRNAs and in particular, members of the let-7 family. In addition, we found differential expression of a few piRNAs that might potentially regulate gene expression. We also report the identification of several repeat RNAs against LTRs, LINEs and SINE elements. Although most of the repeat RNAs mapped to L1 elements, in general we found no significant differences in the expression levels of repeat RNAs in the presence or absence of L1 expression except for a few RNAs targeting subclasses of L1 elements. These differentially expressed small RNAs may function in human genome defence responses.

INTRODUCTION
Small RNAs have recently emerged as being important in the control of gene expression in almost all biological processes. Many different types of small RNA, including microRNAs (miRNAs), endogenous siRNAs (endo-siRNAs), small nucleolar RNAs (snoRNAs) and more recently, Piwi-interacting RNAs (piRNAs), are expressed in a variety of tissues and exert diverse effects on cellular functions [1][2][3]. The different classes of small RNAs are generally distinguished by their mode of biogenesis, protein partners and/or biological function [4]. miRNAs are generated from a single cleavage event of a short hairpin pre-miRNA by the Dicer enzyme, and are loaded onto Argonaute proteins and then guide translational repression of mRNAs. Some miRNAs can also promote degradation of mRNAs by deadenylation [5]. Endo-siRNAs are a class of small interfering RNA with sequences that are complementary to other endogenous mRNAs [6]. They are often generated from multiple Dicer cleavages of long precursor dsRNAs or generated from genomic sources that include the folding back of inverted repeats, sites of bidirectional transcription, and read-through antisense transcripts of retrotransposons and pseudogenes [7,8]. Recent advances in high-throughput small RNA sequencing have resulted in the identification of a growing number of small RNAs in animals. These include many repeat-associated siRNAs (originally termed rasiRNAs), which are produced from retrotransposonderived dsRNAs in Drosophila germline cells. These repeat-associated siRNAs are now known as piRNAs [9], since they are bound by Piwi proteins and are produced by a Dicer-independent mechanism. piRNAs have been shown to play a critical role in silencing retrotransposons as well as having a role in the assembly of heterochromatin structures to silence gene expression [10,11].
The human genome contains large numbers of repetitive sequences and retrotransposable elements both within and between genes. These repeat elements, accounting for nearly 45% of the genome, are grouped into four classes: LINEs (long interspersed elements), SINEs (short interspersed elements), LTRs (long terminal repeats) and DNA transposons. The most active retrotransposons are the LINE-1 or L1 elements [12] which are capable of replicating through their own reverse transcriptase and endonuclease enzymes and inserting DNA copies at new genomic locations. This can often lead to deleterious effects in the genome, such as through the insertion of L1 DNA copies into protein-coding regions of genes, abolishing their function. They may also negatively affect genome integrity because of their ability to create dsDNA breaks during retrotransposition [13]. A recent study surveying human tumors and comparing their genomes to those of adjacent normal tissues suggested that tumors have high frequencies of L1 insertions and dsDNA breaks that are not present in normal tissues [14,15]. The activation of L1 elements has also been implicated in the genomic insertions of ~8000 processed pseudogenes [16] and the retrotransposition of nonautonomous elements, such as retrogenes, Alu and SVA elements [17], which can affect genomic structure in a multitude of ways. Thus, to protect genome integrity, cells normally employ a number of defense strategies, with epigenetic mechanisms, such as silencing by small RNAs, DNA methylation, or histone modifications, playing key roles in keeping L1 elements in check.
Several studies carried out in somatic cells of Drosophila and in mouse oocytes have shown that endo-siRNAs regulate retrotransposons and protein-coding genes that are complementary to the endo-siRNAs [6,18], similar to the function of piRNAs in germline cells [10]. Interestingly, recent evidence shows that piRNAs are also expressed in human cancer cells and their altered expression plays a role in the development of cancer [19]. In relation to this, we recently identified a subset of L1-specific siRNAs that are differentially expressed in a wide range of breast cancer cells compared to normal cells [20]. Overexpression of these siRNAs markedly silenced endogenous L1 expression through increased DNA methylation of the L1 promoter. These findings reveal a role for small RNAs in humans and indicate that depletion of L1-specific siRNAs is most likely to be one of the reasons for the activation of L1 elements in cancer cells. However, the expression status and roles of other small RNAs such as miRNAs and piRNAs have largely remained unknown. Several studies performed in human cancer cells have reported that blocking the retrotransposition of L1 elements not only controls L1 activity but also alters the expression of many genes involved in the proliferation and differentiation of cancer cells [21]. This raises an important question of how the silencing of L1 activity contributes to the regulation of these genes.
Nearly 25% of human promoters have been reported to contain retrotransposon-derived sequences including many cis-regulatory sequences [22], which are involved in specific patterns of gene expression. Interestingly, a significant number of miRNAs and other small RNAs are also derived from repetitive sequences [23,24], which may in turn regulate the expression of other cellular genes harboring repetitive sequences in their transcripts. Through computational searches, two recent studies identified a number of small RNAs, including miRNAs and snoRNAs [25,26], which are derived from the retrotransposition activity of L1 elements. Many of these small RNAs are surrounded by sequence features that are typical of L1 elements, such as target site duplications (TSDs) and poly-A tails at their 3' ends. These analyses propose the involvement of L1 retrotransposition in generating large numbers of small RNAs in the human genome [27]. Moreover, these studies also suggest the existence of many tissue-specific retrotransposon-derived small RNAs that may be a driving force for creating new regulatory elements involved in the control of gene expression [25,26]. Despite these extensive bioinformatic analyses, the existence of such a mechanism needs to be experimentally verified. In light of the accumulating data suggesting a connection between L1 elements and the generation of small RNAs, we investigated this relationship using high-throughput deep sequencing. To investigate whether the expression of L1 elements was correlated with small RNA expression, we analyzed the profile of small RNA expression with respect to the activation or silencing of L1 elements using a published L1-specific endogenous siRNA sequence that specifically silences L1 expression in human cells [20]. Here, we show that rather than generating small RNAs, L1 expression globally reduces the expression of small RNAs. Conversely, silencing of L1 expression up-regulates the expression of a number of miRNAs and piRNAs, sometimes very markedly.

Generation of L1 silencing in breast cancer cells
To determine whether the expression of L1 elements in human cells plays a role in controlling the level of small RNA expression, we used a human T47D breast cancer cell line because our recent studies showed high levels of L1 retrotransposition and concomitantly low levels of L1-specific siRNAs in these cells [15,20]. To silence L1 expression we used a published L1-specific siRNA sequence (endo453) [20], that can specifically silence endogenous L1 element expression by targeting the L1 5'-UTR promoter region (at nucleotide positions 453 to 475). In this assay, we stably transfected T47D cells with an shRNA construct (mimicking the L1-specific endo453 sequence) whose expression was driven by constitutively expressing the U6 promoter. A scrambled endo-siRNA sequence with no homology to any known genes was used as a control ( Figure 1A). As assessed by qRT-PCR www.impactjournals.com/oncotarget   analysis, we achieved L1 silencing efficiencies of 72 ± 4.6% (p=0.001) and 78 ± 5.8% (p=0.003) for ORF1 and ORF2 mRNA, respectively ( Figure 1B). The effectiveness of the endo453 sequence was further confirmed by western blotting of whole-cell lysates from the endo453, scrambled or parental cells using anti-ORF1 antibodies ( Figure 1C). Human embryonic carcinoma NTera.2D1 cells, which express high levels of L1 protein, served as a control. The resulting assays confirmed that the relative expression of L1 mRNA and protein was significantly reduced in endo453 cells compared with scrambled or the parental T47D cells. Together, these results show that the endogenous expression of human L1 elements can be efficiently silenced by an artificially transfected L1specific siRNA sequence.

Deep sequencing of small RNAs
To investigate whether the activation or silencing of L1 elements plays any role in contributing to small RNA expression, we used the Illumina GAIIx high-throughput sequencing platform to sequence small RNA libraries from the endo453 (L1-silenced) and scrambled (L1-active) cells. Since L1 expression was controlled by the introduction of exogenous (synthetic) endo453, the introduced sequences were bioinformatically removed from the files of Illumina reads prior to mapping in order to identify unbiased expression of small RNAs in the L1 silenced library. We then used the miRanalyzer small RNA mapping pipeline [28] to map the reads to miRBase and to identify and annotate all species of miRNAs present in the libraries [29]. In order to identify expressed piRNA sequences, we aligned the reads to the piRNABank database (human.tar. gz) [30] using CLC Genomics Workbench (version 5.5.1) (http://www.cicbio.com). Finally, we mapped the reads to the deepBase repeat-associated small RNA database (Human_sense_rasRNA_deepBase.fasta, Human_ antisense_rasRNA_deepBase.fasta) [31] to detect repeatassociated RNA also using CLC Genomics Workbench. An overview of the sequencing and mapping results for the two libraries is given in Table 1.
The distribution of lengths of the L1-silenced small RNAs was similar to that of the L1-active library, with peaks at 22-23-nt ( Figure 2A). After removal of singleton reads and reads of less than 17-nt from each library by the miRanalyzer pipeline, there were 21,266,358 and 22,745,765 reads in the L1-active and L1-silenced libraries, respectively. Surprisingly however, 8,076,212 sequence reads from the L1-silenced sample mapped to known mature miRNAs compared to 3,882,279 in the L1-active sample. This large increase in numbers of reads mapping to known miRNAs in the L1-silenced library was unexpected. However, we found that a small number of very highly differentially expressed miRNAs were responsible for most of the discrepancy ( Figure 2C). In particular, in the intermediate miRanalyzer file of mapped reads at the step prior to normalization (by default named as microRNA.diffexpr in the miRanalyzer pipeline), 1,855,633 reads mapped to let-7a in the L1-silenced library compared to 149,428 reads in the L1-active library, accounting for ~41% of the difference in the unnormalized counts of the mapped sequence reads. This large difference remained even after normalizing the read counts in both libraries using the DESeq package which is incorporated into the standard miRanalyzer pipeline [32]. DESeq uses a median-based scaling method to normalize read counts rather than scaling based on the total read counts in each library. The key steps are as follows: first a reference sample is constructed by calculating the geometric mean Blue symbols denote piRNAs. (b) Bland-Altman plot of normalized read counts for piRNAs in L1-silenced cells versus L1-active. www.impactjournals.com/oncotarget over all samples of the counts for each mapped miRNA species. Next, for each sample, the quotient of the mapped read count of each miRNA species to that in the reference sample is calculated. The quotients for each sample are sorted in numerical order and the median quotient becomes the scaling factor for the sample. This is a robust method of normalizing read counts when a relatively few highly differentially expressed small RNA species might bias the normalization of the library sizes if a global scaling method based on total read counts in each library was used [32,33].

Differentially expressed miRNAs
To identify miRNAs that were differentially expressed between active and L1-silenced cells, we mapped the reads from both libraries against miRBase (version 16) using the miRanalyzer pipeline [29], which also includes normalization and calculation of fold changes in the R/Bioconductor DESeq software package (version 1.6.0) [32]. To determine the rankings of differentially expressed miRNAs, we used the GFOLD (generalized fold change) algorithm (version 1.0.7) [34]. The GFOLD algorithm takes into account the observation that the fold changes of expressed species with low read counts are less reliable than those of species with high read counts and estimates a fold change value for each species based on the posterior distribution of the raw fold change. The analysis revealed numerous differentially expressed and highly expressing miRNAs in the L1-silenced versus the L1-active cells (Supplementary Table S1). Overall, 23 miRNAs were up-regulated in the L1-silenced cells based on a GFOLD (0.01) threshold of 2. The most abundantly expressed miRNAs were the let-7 family members; let-7a, let-7b, let-7c, let-7e and let-7f were all up-regulated in the L1-silenced cells, in some cases, very markedly ( Figure 2C). This result is consistent with published miRNA-profiling studies in many human cancer cells in which down-regulation of let-7 family members has been reported more commonly than their up-regulation [35]. In addition to the let-7 family members, large changes were also seen in the expression of miR-196a, miR-30d, miR-103, miR-425, miR-191 and miR-200c ( Figure  2D). Although these miRNAs were expressed at high levels in both libraries, the differences in their expression between the two libraries helped to account for most of the difference in the total number of mapped reads between the two libraries.
The canonical miRNA biogenesis pathway generates an miRNA duplex that consists of an miRNA and miRNA* strand. Several miRNA* species have been recently found in humans and some of them have been implicated in differentiation of cancer cell states [36]. In the current study, we identified three typical miRNA* species (miR-149*, miR-625* and miR-9*) that were exclusively expressed in the L1-silenced cells with normalized read counts ≥ 10 (Supplementary Table S1). In addition, we found three miRNA*s (miR-664*, miR-505* and miR-32*) that were differentially expressed between the two cell types with at least a 5x fold change. Several studies have described miRNA biogenesis by an alternative miRNA-processing pathway that uses the intron-splicing machinery instead of the Drosha/Dgcr8 endonuclease to generate miRNA precursors (called miRtrons) from intronic sequences [37]. One of the most highly differentially expressed was miR-664, a typical miRtron whose miRNA-664 (4 vs. 31) and miR-664* (25 vs. 346) sequences flank the edges of its host intron in a pattern reminiscent of miRtron miRNA precursors. The host gene of the miR-664 intron has been identified as RAB3GAP1, and the sequence of miR-664 overlaps with an annotated snoRNA, ACA38B [27]. PhastCons scores, which reflect probabilities of selective maintenance through evolution, extend to the edges of the miRNA hairpin (data not shown), suggesting that the miR-664 locus is highly conserved in the human, chimpanzee and mouse lineages. Moreover, we also found a number of newly identified miRNAs that were expressed only in the L1-silenced cells although at low levels, including miR-3662, miR-3174, miR-3146, miR-3192 and miR-3605-5p (Supplementary Table S1).

Differential expression of piRNAs
piRNAs are a distinct class of small RNAs that interact with Piwi proteins and are involved in epigenetic and post-transcriptional silencing of retrotransposons and other genomic repeats in germline cells. Unlike miRNAs, which are in the size range of 21-23 nt, piRNAs are 24-31 nt in length. piRNA sequences do not have a precursor with a stem-loop and are not processed by Dicer. While there have been few reports of the presence of Piwi proteins in human cancers [38], one recent study did confirm the expression of piRNAs in human gastric cancer cells [39]. To identify differentially expressed piRNAs and other small RNAs in addition to miRNAs, we selected the adapter-trimmed reads in each library with lengths greater than 23-nt using the filter function in CLC Genomics Workbench and mapped this subset of reads from each library against the human piRNABank database [30]. After normalization of the reads with DESeq, a total of 77,732 reads in the L1-active and 70,869 reads in the L1-silenced cells matched 314 and 340 piRNA sequences, respectively, after deleting reads with a normalized mapped read count of 1 (Table 1). We found that the expression levels of most piRNAs were relatively constant in the L1-active and silenced libraries. However, we found a number of differentially expressed piRNAs in the L1-silenced versus the L1-active cells ( Figure 3A). Overall, 22 and 26 piRNAs were up-and down-regulated, respectively in the L1-silenced cells based on the GFOLD thresholds of 2.0  Table S2).
The most highly differentially expressed piRNAs as assessed by GFOLD were hsa_piR_000586 (fold change = 22x), hsa_piR_001101 (14x) and hsa_piR_020450 (16x) which were all up-regulated in the L1-silenced cells ( Figure 3B). The extent of down-regulation of piRNA expression in the L1-silenced cells was not as marked as that of up-regulation. For instance, the most downregulated piRNA in the L1-silenced cells as assessed by GFOLD was hsa_piR_020582 (fold change = -10.35x). Intriguingly, an NCBI BLAST search with the hsa_ piR_000586 DNA sequence (28-nt) showed a match with 100% identity to a C/D box snoRNA residing in an intronic region of the Mortalin/GRP75 gene, an oncogene that is overexpressed in human cancers [40]. The E-value was 8x10 -7 . BLAST searches with the sequences of hsa_ piR_001101 and hsa_piR_020450 also gave hits to C/D box snoRNAs residing in the introns of genes. These observations suggest that such piRNAs may have a role influencing gene expression. Although the functions of piRNAs are quite diverse and largely unknown, some of the differentially expressed piRNAs observed here may be involved in regulating cell activities and further studies in this area should reveal the functions of these piRNAs.

Differential expression of repetitive RNA
Repeat-associated RNA refers to a class of noncoding small RNAs that includes transcripts from genomic repeats. Although it is unclear how they are processed and their biological functions are largely unknown, repeat-associated small RNAs map to repetitive elements such as LINEs, SINEs and LTRs. Like siRNAs, most repeat-associated small RNA sequences are in the size range of 18-23-nt. Until recently, all repeat-associated small RNAs were grouped together based on their genomic locations as rasRNAs [31]. While a number of earlier studies dismissed repeat-associated small RNA expression as genomic noise, one recent study identified functions for repeat RNA in the cell cycle and in DNA repair [41]. To identify differentially expressed repeat-associated small RNAs in addition to miRNAs and piRNAs, we mapped subsets of the small RNA libraries (i.e. reads < 24 nt in length from both the L1-active and L1-silenced libraries) against the human deepBase database [31]. The deepBase database contains human repeat RNAs that map to known repeat elements as well as large numbers of reads mapped to unique loci in the human genome. Reads were mapped against the human rasRNA antisense and sense fasta files downloaded from deepBase (Human_antisense_ rasRNA_ deepBase.fasta and Human_ sense_rasRNA_deepBase. fasta as of July 2011). As with mapping against miRBase and piRNABank, the mapped read counts of each class of repeat RNA were normalized using the R/Bioconductor DESeq package [32]. The mapping procedure identified high levels of expression from many distinct repeat loci in both libraries. We analyzed the reads mapping to repeat RNA by first considering reads mapping to specific genomic loci, followed by grouping reads based on their repeat family and class.
Using Unix command-line tools and Perl and R scripts (available on request), we extracted the RepeatMasker [42] annotation of each mapped deepBase read (contained in the header of each read in the deepBase fasta files) and grouped the mapped reads by RepeatMasker class/family name. Read counts belonging to each class and family were summed up with R scripts to obtain the total reads belonging to each class and family of repeat element. After grouping reads into their repeat class (LINE, SINE etc), we found that for the LINE class, sense and antisense expression were very similar in the L1-active cells (ratio: 1.02, Table 1c), while in L1silenced cells, LINE antisense expression was 24% of that of sense expression. At present, the relevance of this discrepancy is unclear but it is likely to be a result of L1 silencing. At the level of individual genomic loci (i.e., after annotating reads with their deepBase accession), we found differentially expressed sense and antisense loci. The Bland-Altman plots ( Figure 4A-B) show that the sense loci most likely to be significantly down-regulated in the L1-silenced cells are hgur000839476 (belonging to the L1PB4 family) and hgur000013038 (belonging to the L1M7 family). There were also numerous differentially expressed loci with low levels of expression including repeats from the L1M7, L1MC5, L1MEd, L1MC1, LTR33 and ERVL-B4-int families ( Figure 4A-B). For reads mapping to individual antisense loci, the loci most likely to be significantly down-regulated in the L1silenced cells include hgur000890658, hgur000013075 and hgur000890657 (all belonging to L1M5) while hgur000142641 and hgur000142640 (belonging to MIRc) are up-regulated in L1-silenced cells ( Figure 4C-D).
The number of sense and antisense reads in each repeat RNA family and their relative fold changes between the two libraries are summarized in Figure 5A-D. In some cases, there was no RepeatMasker name for a particular deepBase entry and these reads are listed in the "Unknown" category and breakdowns of these are shown in Figures 5B and 5D. When grouped according to their RepeatMasker family, a large majority of the sense and antisense reads mapped to either L1 elements or an "Unknown" family lacking a RepeatMasker family annotation. For the sense reads, mapping to the L1 family and the "Unknown" category (comprising mainly 5S RNA, small cytoplasmic RNA (scRNA), and LSU-rRNA_ Hsa) dominates the other families. Notably, 131,177 reads in the L1-active and 76,825 reads in the L1-silenced libraries mapped to active L1 elements compared to 1103 and 1345 reads, respectively, against the inactive LINE L2 family ( Figure 5A). The high proportion of repeat RNAs expressed against L1 elements is not unexpected as L1 is the only known active retrotransposon in the human genome and, as expected, L1 expression was markedly repressed in the L1-silenced cells. The most interesting finding for the "Unknown" group of the sense reads is the marked increase in reads mapping to the HY1, HY3, HY4 and HY5 categories of scRNA in the L1-silenced cells (with fold changes ranging from 2.6x to 4.0 x), and the marked increase in reads mapping to tRNA and the U3, U8 and U17 snoRNA (small nucleolar RNA) species ( Figure  5B). A similar pattern was also seen for the antisense reads except that the "Unknown" group was much smaller. There was an increase in the numbers of antisense reads mapping to L2 elements (but still < 10% of the values for L1) and MIR elements in the L1-silenced cells ( Figure  5C). The up-regulation of scRNA and snoRNA expression in the L1-silenced cells is consistent with a generalized upregulation of rRNA biogenesis and protein synthesis as the L1-silenced cells assume a more differentiated phenotype.
A summary of the small RNA mapping results for the L1 active and L1-silenced libraries is given in Figure 6A-B. As shown in Figure 6A, the expression of piRNAs and repeat-associated small RNA families is at a relatively low level compared to miRNA expression. In mammals, repeat-associated small RNAs have not yet been investigated thoroughly. However, some of the endo-siRNAs that are known to originate from retrotransposons in germline cells have also been shown to be expressed in somatic cells of Drosophila and their biogenesis is at least in part, Dicer-dependent [43]. Thus it is plausible that some human repeat-associated small RNAs might exist as remnant copies from germline and embryo cells. At present it remains unclear whether they act as functional siRNAs in controlling repetitive elements in humans, similar to the action of Drosophila endo-siRNAs in somatic cells [6]. Because our knowledge of the functional and mechanistic roles of repeat-associated small RNAs is at an early stage, further studies focusing on the regulatory mechanisms of repeat RNA expression will shed light on these questions. Nonetheless, our studies have identified that in addition to miRNAs and piRNAs, repeat RNAs are indeed expressed in breast cancer cells and some of the repeat RNAs identified could potentially regulate the expression of LINEs and other retrotransposons in the human genome.

DISCUSSION
In light of the accumulating data reporting a connection between the expression of L1 elements and the generation of retrotransposon-derived small RNAs, we analyzed changes in the expression profile of miRNAs, piRNAs and repeat-associated small RNAs before and after silencing the expression of L1 elements in breast cancer cells, using the published L1-specific endo453 sequence that specifically silences endogenous L1 expression [20]. We found that rather than generating small RNAs, L1 expression globally reduces the expression of small RNAs.
The principal finding was of greatly increased expression of members of the let-7 family of miRNAs, and increased expression (above a GFOLD threshold of 2.0) of several other miRNAs including hsa-miR-196a, hsa-miR-30a/d, hsa-miR-191 and hsa-miR-200c following silencing of L1 expression. We also observed increased expression of a number of specific piRNAs, but overall, a decrease in total piRNA expression in the L1-silenced cells. The most highly up-regulated piRNAs were hsa_piR_000586, hsa_piR_001101 and hsa_piR_020450. Remarkably, these piRNA sequences showed a perfect match to C/D box snoRNAs residing in the introns of protein-coding genes, suggesting that expression of such piRNAs may influence gene expression.
Considering the repeat-associated small RNAs that mapped to repetitive elements, in general we found no significant differences in the levels of the repeat RNAs in the presence or absence of L1 expression except for a few antisense RNAs potentially targeting subclasses of retrotransposons. The antisense repeat RNAs that were most differentially expressed between the L1-active and L1-silenced cells mapped to active L1 elements (L1M5, L1ME3), and the MIRc element. The majority of the repeat RNAs analyzed in this study are 18-23 nt in length suggesting that they are endogenous siRNAs as opposed to longer piRNAs, as would be expected in somatic cells. Since the mechanisms of biogenesis of small RNAs derived from repeat elements are not yet known, it is difficult to say whether these repeat RNAs are functional siRNAs, and answering this question would require a functional analysis. The alternative possibility is that leaky transcription from genomic repeats could produce a range of fragmented small RNA sequences, which may or may not be involved in the control of repetitive elements. It is also unclear why the genome expresses sense and antisense repeat RNAs against the oldest and inactive L2 elements. It seems more likely that the L2-derived repeat RNAs are an exaptation of L2 sequences rather than a defense against them. Interestingly, our study shows that most of the repeat-associated small RNA reads mapped to the L1 family and only a small proportion mapped to SINE and LTR elements. The high proportion of repeat RNAs expressed against L1 elements is not unexpected as L1 is the only known active retrotransposon in the genome.
The expression of L1 elements poses a substantial mutagenic threat to genomic stability. To control L1 expression, cells have developed a variety of silencing mechanisms. Several types of small RNAs, including miRNAs, endo-siRNAs and piRNAs, have evolved as defense mechanisms against L1 activity. However, to date, there has been no report of bona fide human miRNAs that can target L1 elements. siRNAs can arise from dsRNA precursors, which are processed by the RNAi machinery to generate functional siRNAs against genomic repeats and retrotransposons [2,4]. In connection with this, our recent study identified a number of L1-specific siRNAs www.impactjournals.com/oncotarget that can markedly silence endogenous L1 expression [20]. In addition, there are some cases of siRNAs and miRNAs originating from the same repetitive elements. For instance, many members of miR-548 family and siRNAs are derived from inverted-repeats of the MADE1 retrotransposon [23]. piRNAs that repress L1 elements have been mainly found in germlines of a number of organisms but not in human somatic cells. Strikingly, recent reports of the presence of Piwi-class proteins suggest that active piRNAs might possibly exist in human cancer cells [38] and in this study we confirmed the presence of piRNAs in breast cancer cells. Notably, we found a number of differentially expressed piRNAs in L1-silenced cells that might potentially be involved in regulating cellular gene expression.
Recent genome-wide studies have revealed that L1 expression and retrotransposition occurs at a high level in the cancer genome, resulting in large-scale restructuring of the genome [14]. Consistent with this, widespread changes in gene expression have been found due to the expression of L1 elements in cancer cells [21]. Also, computational searches have identified a number of miRNAs and snoRNAs that are flanked by L1 sequences, suggesting that L1 activity could potentially influence the expression of small RNAs [25,27]. However, it is not clear whether the structural and genetic changes caused by L1 retrotransposition might contribute to changes in small RNA expression or whether L1 elements might act as active promoters for small RNA expression. If this was the case, one would expect to observe a positive correlation between L1 expression and small RNA expression. On the contrary, we found that silencing of L1 expression induces the expression of a number of miRNAs. The differential expression of mature let-7 miRNAs in L1-active and L1-silenced cells is an intriguing finding; similar to the differential expression of let-7 seen in normal and cancer cells whereby the expression of let-7 is posttranscriptionally inhibited in cancer cells [44]. Although at present, it is unclear if there is a functional relationship between the expression of let-7 and L1 elements, there is evidence indicating that cancer cells, which normally express high levels of L1 elements, have globally reduced levels of small RNAs, and in particular, miRNAs, compared to their normal counterparts [45].
At this stage, the underlying mechanism of the activation of the small RNAs in L1-silenced cells is unknown. One possibility is that silencing of L1 expression substantially reduces the restructuring of the genome and the transcriptome that occurs in cancer. One could therefore argue that the relative expression of other RNAs should change in L1-silenced cells. Strikingly, it has been shown that inhibition of L1-encoded reverse transcriptase in several cancer cell types modulates cell growth and differentiation by affecting global gene expression [21]. Another recent study has proposed that the L1-encoded reverse transcriptase enzyme in cancer cells could actively be involved in reverse-transcription of small RNAs and mRNAs into cDNA resulting in the formation of RNA-DNA duplexes and thus impairing the formation of miRNAs and the expression of cellular genes [46]. Inhibiting the activity of RT by pharmacological inhibitors restores the normal profiles of small RNAs and subsequently affects gene regulatory networks within the context of cellular growth. Another possibility is that DNA methylation and chromatin accessibility to RNA polymerase II has a role in the transcriptional activation or silencing of some of these small RNAs. A recent study in human cancer cells suggested that many genomic loci producing small RNAs are subjected to DNA hypermethylation, resulting in transcriptional repression of small RNA expression [47]. Defects in miRNA expression have also been attributed to the transcriptional repression of promoters [48]. Thus, as occurs with miRNAs, it is likely that other classes of small RNAs might also be subjected to DNA methylationassociated repression. It would be interesting to further investigate possible changes in methylation patterns in the promoters of the identified small RNA loci, which might allow us to better understand the biological functions of differentially expressed small RNAs in the human genome. Nevertheless, the data presented in this study suggest that unanticipated interactions exist between the repression of L1 elements and the global expression of small RNAs. Although the exact regulatory pathways are unclear, this study proposes that, through direct silencing of L1 elements, the expression of small RNAs, including miRNAs and piRNAs, plays an important function in the maintenance of genomic integrity.

Cells, siRNAs and transfection.
T47D (ATCC HTB-133) human breast cancer cells were maintained in Dulbecco's modified Eagle's medium (DMEM) with 2 mM L-glutamine and 10% FCS under standard cell culture conditions. An shRNA that mimicked 22-nt of the L1-specific endo453 and a scrambled control shRNA were synthesized as described in reference [49] and cloned directly into a pSM2 vector (Open Biosystems) under the control of a U6 promoter. T47D cells were stably transfected with the scrambled control or L1specific shRNA constructs using an Amaxa nucleofector kit, followed by puromycin (0.5 µg/ml) selection for 10 days. Depletion of L1 mRNAs was confirmed by qRT-PCR as described previously [20]. Whole cell lysates were prepared by using MPER reagent (Pierce), following the manufacturer's instructions. Western blot analysis was performed with anti-L1-ORF1p antibodies [15] at 1: 2000 dilution, followed by addition of HRP-conjugated secondary antibodies (Silenus, Australia). The resulting signals were visualized using the ECL chemiluminescence system (Pierce). To confirm protein normalization, the membranes were stripped and re-probed with α-tubulin antibodies (Sigma).

Small RNA library preparation and sequencing.
Total RNA from control and endo453-transfected cells was extracted using TRIzol (Invitrogen). Low MW small RNA was enriched by adding 50% PEG-8000 and 5M NaCl to a final concentration of 5% and 0.5M, respectively, followed by gel purification in a 15% urea-PAGE gel (Invitrogen). Small RNAs ranging from 18-30 nt were gel purified and ligated to 3' adaptor and 5' adaptor oligonucleotides as described in the Illumina Small RNA Kit. Briefly, the 3'-RNA adaptor (5'/5rApp/ ATCTCGTATGCCGTCTTCTGCTTG/3ddc/), which specifically ligates to RNAs that contain a hydroxyl group at their 3'end, was ligated to 1 µg of low MW small RNAs using T4 RNA ligase (NEB). The resulting products were subsequently ligated to the 5'-RNA adaptor (5'-GUUCAGAGUUCUACAGUCCGACGAUC-3'). cDNA was synthesized with SuperScript II Reverse Transcriptase (Invitrogen) and subjected to 12 cycles of PCR amplification with high-fidelity Phusion Polymerase (NEB) using primers as published by Illumina. Each library was loaded on a single Illumina lane at 20 pM and underwent 36 cycles of sequencing on an Illumina GAIIx Genome Analyzer.

Bioinformatic analysis of small RNAs.
Singleton reads and reads with a 3'-adapter substring <6 nt or trimmed sequence length <17 nt were removed from the analysis carried out by the miRanalyzer pipeline. Trimmed reads were aligned to the miRBase database of human miRNAs (version 16) using Bowtie as implemented in the miRanalyzer pipeline (with the following Bowtie options: minimum score of 30 and minimum identity of 90%) [31]. Many miRNA sequences, especially those belonging to the same miRNA family, have a high degree of sequence similarity and since the read may be short (~ 16 bp), non-unique matches can occur. A non-unique match exists if a read maps with the same quality (i.e. same number of mismatches) to different positions or to more than one sequence in the library. Failure to report non-unique matches may represent loss of information. Therefore, miRanalyzer reports ambiguous matches, listing all miRNAs where matches have been found. The order of mapping against known miRNAs is firstly to mature, then mature-star and precursors/hairpin. Both unique matches (a read matching to a single known microRNA) and ambiguous matches are detected and removed from the input at each step.
The sequential removal is important as otherwise the reads would be detected again in the precursor sequences (hairpins). For further details, refer to: http://bioinfo2.ugr. es/miRanalyzer/miRanalyzer_tutorial.html#x1-110004.1.1 In order to calculate differential expression, the mapped read counts were normalized and fold changes between L1-active and L1-silenced cells were calculated with the R/Bioconductor DESeq package [32] which is integrated into the miRanalyzer pipeline.

Detection and analysis of piRNAs and repeatassociated RNAs.
To identify differentially expressed piRNAs, we selected the trimmed reads in each library with lengths greater than 23 nucleotides using the filter function in CLC Genomics Workbench 5.5.1 and mapped these subsets of reads against the human piRNABank database [30] using CLC Genomics Workbench with default parameters (i.e. maximum mismatches of 2 and strand-specific alignment). Each file of adapter-trimmed Illumina reads was aligned to the uncompressed Human.tar.gz piRNABank fasta file after converting the fasta reads from RNA to DNA. The two files of mapped reads and read counts (for the L1active and L1-silenced libraries) were exported in plain text format from CLC Genomics Workbench and merged in R using the merge() command. The read counts were normalized using the DESeq package in R.
To identify differentially expressed RNAs mapping to repetitive elements, we selected the trimmed reads in each library with lengths less than 23 nucleotides using the filter function in CLC Genomics Workbench. Repeat RNAs were identified and annotated by alignment with the known rasRNA sequences in the deepBase database (version GRCh37) [31]. Alignments were carried out with CLC Genomics Workbench using default parameters (i.e. maximum mismatches of 2 and strand-specific alignment). Each subset of adapter-trimmed Illumina reads was aligned to the Human_sense_rasRNA_deepBase.fasta and Human_antisense_ rasRNA_deepBase.fasta files. The two files containing the mapped reads from the L1-active and L1-silenced libraries were exported from CLC Genomics Workbench and edited in MS Excel to remove extraneous text strings before importing and merging in R with the merge() command. The read counts were normalized using the DESeq package in R. The fasta headers of each sequence (containing the deepBase accession name, genomic coordinates and RepeatMasker name) from the human deepBase.fasta files were extracted and saved to a text file using the Linux grep command. The file containing the normalized reads from both libraries was merged with the file of fasta headers using the R merge() command. The RepeatMasker class and family for each RepeatMasker name were downloaded into a text file from the RepeatMasker [42] website with a Perl script and further processed in MS Excel to remove extraneous columns and html tags before adding the RepeatMasker class and family names to the normalized reads file using the R merge() command. Read counts were accumulated for RepeatMasker classes and families using R. Additional details of the bioinformatics workflow and the Perl and R scripts are available on request.

SUPPLEMENTARY MATERIAL
Supporting information and supplementary tables are available online


s2fieldsofstudy: Biology Medicine

text:
Modulation of Homology-Directed Repair in T98G Glioblastoma Cells Due to Interactions between Wildtype p53, Rad51 and HCMV IE1-72

Human cytomegalovirus (HCMV) is a ubiquitous pathogen capable of causing life threatening consequences in neonates and immune-compromised individuals. HCMV inflicts site-specific double strand breaks (DSBs) in the cellular genome. DNA damage infliction raises the corollary question of virus modulation of DNA repair. We recently reported HDR was stimulated in wt human foreskin fibroblasts (HFFs) during fully permissive infection or expression of the HCMV protein IE1-72 (IE72). These studies have been extended into semi-permissive T98G glioblastoma cells. T98Gs encode a mutant p53, which may contribute to their high baseline rate of HDR. We fully expected HCMV infection to increase HDR in T98Gs, similar to its effects in HFFs. Surprisingly in T98Gs HCMV infection, or sole expression of IE72, decreased HDR by two-fold. Transient expression of wt p53 in T98Gs also reduced HDR by two-fold. Dual transient expression of wt p53 and IE72 restored high baseline HDR levels. GST pulldown experiments revealed that both IE72 and wt p53 bound the important HDR protein, Rad51. We conclude that the expression of certain HCMV proteins can modulate HDR in an infected cell, dependent upon p53 status. We propose a model of the protein interactions explaining this behavior.

Introduction
Human cytomegalovirus (HCMV) is a member of the β-herpesvirus family and is endemic in the human population. HCMV can cause congenital birth defects, primarily of neurological origin [1][2][3]. It also causes severe systemic disease following reactivation in immunosuppressed individuals [4]. The virus' role in oncomodulation of cancers has also recently been the subject of intensive investigation (for review see [5,6]).
One of the direct effects of HCMV infection in human foreskin fibroblasts (HFFs), is the induction of two site-specific breaks at chromosome 1q42 and 1q23.3 in the host cellular DNA [7,8]. The physiological significance of HCMV-induced specific breaks or whether they can be repaired is unknown. However, the proximity of the 1q23.3 breakpoint to two hearing impairment (HI) loci, DFNA49 and DFNA7, and in the vicinity of the myelin protein zero (MPZ) gene [9], prompts speculation on this break's correlation with the hearing loss observed in congenitally infected infants.
Mammalian cells are exposed to both intrinsic (i.e., reactive oxygen species) and extrinsic (i.e., genotoxic chemicals) agents capable of inflicting cellular DNA damage. DNA damage triggers a cascade of complex protein signaling pathways. This combination of pathways constitutes the DNA damage response (DDR). Depending on the type and extent of DNA damage different repair pathways are activated. In the case of double-stranded breaks (DSBs), two repair pathways are principally activated (as reviewed in [10,11]). One is the error free homology-directed repair (HDR) pathway, which commonly involves strand invasion of a sister chromatid. The other pathway utilizes the more error-prone non-homologous end-joining (NHEJ) or single-strand annealing mechanisms.
We recently reported interactions between HCMV and HDR in permissive HFFs [12]. HCMV infection caused a two-fold increase in HDR of an I-SceI induced site-specific DNA DSB, from a baseline level of 4% to a stimulated level of 8%. Expression of the HCMV IE72 protein on its own produced comparable results. Here the same DSB repair assay has found that HCMV regulates HDR in the semi-permissive T98G cells in a surprising and counterintuitive manner, hinging on the p53 status of these cells. A model to explain the interaction of HCMV IE72 with p53 and the HDR machinery is proposed.

Introduction of the pDRGFP Substrate into T98G Cells to Assess HDR
Our earlier study assessed whether HCMV infection affected HDR of an integrated substrate at I-SceI-induced DSBs in fully permissive HFFs. This work found that HDR, as measured by an increase in GFP + cells, was stimulated in HFFs during HCMV infection and, further, that expression of the HCMV protein IE72 on its own also stimulated HDR [12]. The assay utilized a non-functional GFP reporter substrate susceptible to DSBs upon introduction of I-SceI [13,14] (see Figure 1A for diagram of the substrate). If the DSB was successfully repaired by HDR, functional GFP was reconstituted. HDR was scored by microscopic analysis for cellular GFP + . These fibroblast experiments have now been extended into the semi-permissive cell line T98G. Three different stable clones of T98Gs with an integrated pDRGFP substrate were generated. The results obtained using all clones were consistent, therefore here we report only representative studies carried out in Clone 10. with an integrated pDRGFP substrate. (A) Schematic diagram of reporter plasmid pDRGFP as described in [12]. (B) Representative IF staining of Ad-infected Clone 10. Cells were seeded onto glass coverslips and infected with Ad expressing I-SceI (NGUS24i). Coverslips were harvested at 72 h post infection. GFP + cells were scored as HDR competent. Cells were stained with HA-specific Ab to detect HA-I-SceI expressed from NGUS24i. Scale bar = 5 µm for all panels and all figures.
Clone 10 was synchronized in G 0 and then infected with an Adenovirus (Ad) expressing the I-SceI endonuclease (NGUS24i), or an Ad control virus (dL70-3) [15,16] at various MOIs (5, 10, 25, 50, 100, and 200) or were mock-infected. Cells were analyzed for efficiency of I-SceI endonuclease production after NGUS24i infection. The I-SceI endonuclease in this virus is tagged with hemagglutinin (HA), permitting detection of protein expression using an anti-HA Ab by IF. 100% of the cells infected with NGUS24i displayed HA-positivity and no staining was observed in mock-infected cells ( Figure 1B) or dL70-3-infected cells (data not shown).
NGUS24i infection produced GFP + cells. This indicated I-SceI-induced site-specific DNA breaks had been successfully repaired by HDR. In our hands NGUS24i infection effects were greatest using an MOI of 200 and continuous incubation for 72 h prior to harvest. Clone 10 was also examined for spontaneous HDR events. Figure 1B displays representative experiments depicting the absence of any GFP + cells in mock-infected Clone 10. These results demonstrated that the Ad-based enzyme delivery system in conjunction with the integrated pDRGFP substrate worked as well in the T98G cells as had been previously observed in the HFFs [12].

HDR at I-SceI Induced DSBs Was Downregulated in HCMV-Infected T98G Cells
Previously we showed that in a semi-permissive infection of T98G cells, although all cells take up virus, only a modest percentage (~30%-40%) of cells express IE72 antigen (Ag) in contrast to 100% Ag + cells in a fully permissive infection [17]. Clone 10 was G o synchronized, released from synchronization and then mock-infected or HCMV-infected at an MOI of 10 for 48 h. These cells were subsequently superinfected with NGUS24i or dL70-3 and harvested at 72 h post Ad infection. Cells were stained for the expression of HCMV IE72 by IF to monitor initiation and progression of HCMV infection. Approximately 30% of cells were IE72 + at 120 h post infection (hpi), which agreed with previous experiments [17]. Cells were also scored for GFP + . Representative IF images for these infections are shown in Figure 2A. T98Gs displayed a very high baseline rate of HDR after DSB induction with I-SceI. Ad NGUS24i infection resulted in ~16% GFP + cells (as compared to 4% in HFFs) [12]. No GFP + cells were observed in either dL70-3-infected or mock-infected cells. An average of ~7% GFP + cells were detected in HCMV-infected cells subsequently superinfected with NGUS24i to induce DSBs, an ~2 fold decrease ( Figure 2B). Although not quite reaching a degree of statistical significance, this decrease was consistently reproducible (fold changes of 2.7 and 1.9 in two experiments). Downregulation of HDR due to HCMV infection was not associated with changes in steady state levels of I-SceI expression. Immunoblot analysis found no decrease in I-SceI expression in HCMV-infected T98Gs compared to mock-infected T98Gs ( Figure 2C). Actually, somewhat the reverse was found with slightly higher levels of I-SceI observed in lysates derived from an equivalent number of virus-infected cells. Thus lower expression of the I-SceI enzyme was not the cause of the somewhat paradoxical decrease in HDR.

Transient Expression of IE72 Alone Was Sufficient to Downregulate HDR in T98Gs
A number of studies have suggested that IE72 plays a vital role in fine tuning the host cell, including activating early gene expression needed for viral replication ( [18] and as reviewed in [19]). We had previously shown that expression of IE72 alone was sufficient to enhance HDR in HFFs [12]. Having found that full HCMV infection reduced HDR in T98Gs, we repeated the IE72 expression assays in these cells. Parallel experiments to those above were performed in Clone 10 nucleofected with vector-alone (pSG5) or an IE72-expressing construct (pSGIE72). These cells were then Ad infected. Cells were subsequently scored for IE72 and GFP positivity. Approximately 90% cells were IE72 + (data not shown).
No GFP + cells were observed in vector alone (pSG5), pSG5 + dL70-3-infected (control Ad), IE72 alone or IE72 + dL70-3-infected cells. In close agreement with the previous results an average of ~16 % GFP + cells were detected in cells nucleofected with pSG5 and NGUS24i-infected (I-SceI expressing Ad). In cells nucleofected with the IE72 construct and NGUS24i-infected, an average of 9% GFP + cells were scored, a 1.8 fold decrease ( Figure 3A). This decrease was approximately equivalent to the reduction in GFP + of HCMV-infected cells. Again, although this decrease was not statistically significant, it was consistently reproducible (fold changes of 1.9 and 1.5 in two experiments). Therefore, as was seen in HFFs, transient expression of IE72 alone largely recapitulated the HCMV-infection results, however, with the same perplexing downregulation of HDR in T98Gs.

Expression of wt p53 in T98Gs Caused the Same Decrease in HDR as IE72
T98Gs have a mutagenic profile and harbor a mutation in p53 at R273H, in the DNA binding domain [20]. It is widely accepted that wt p53 suppresses HDR (as reviewed in [21]). Was the mutation in p53 responsible for the elevated levels of HDR (~16%, Figure 2B) in uninfected T98Gs compared to HFFs (~4%, [12])? If so, introduction of wt p53 would be expected to decrease HDR levels in uninfected T98G cells and, similarly, p53 DNA binding domain mutants would not be expected to alter HDR levels. Experiments parallel to those discussed for IE72 were performed in Clone 10. It should be noted that although all the constructs used in this study were GFP-tagged, the wt p53 and the mutant p53 genes were expressed from the weak wt p53 promoter and, as we have previously observed [22], no detectable GFP expression above background was observed in control populations of these cells (data not shown). Clone 10 was nucleofected with vector-alone (pCDNA3-T7) or constructs [22] expressing wt p53, a non-acetylatable p53 mutant (K382R), p53 DNA binding domain mutants (R273H, R175H, R248W, G154V), or a p53 N-terminal multi-site phosphorylation mutant and subsequently Ad-infected. Cells were scored for GFP + ( Figure 3B). Immediately evident was that introduction of even low levels of wt p53 into T98Gs resulted in accelerated cell death, with very few cells surviving until 72 h post Ad infection. The infection protocol was modified to overcome this technical problem. Ad infection MOIs were reduced from 200 to 5. An MOI of 5 still delivered I-SceI enzyme to the entire population, as detected by HA Ab staining (data not shown), however the lower MOI reduced baseline DSB-induced HDR events to a more modest, but still prevalent, ~11%. More importantly, the reduced MOI allowed sufficiently high cell survival for a long enough duration to obtain meaningful population counts. Eight hours post-nucleofection, cells were Ad infected and GFP + was scored at 40 h post Ad infection. Following this modified protocol, control experiments using any of the constructs followed by infection with the dL70-3 control virus showed no GFP + cells (data not shown). Nucleofection with no DNA, or introduction of vector alone (pCDNA3) followed by I-SceI-expressing NGUS24i-infection produced an average ~11% GFP + cells ( Figure 3B). Cells nucleofected with wt p53 or the p53 K382R mutant produced an average of ~6% GFP + , an ~2 fold decrease. This decrease was consistently reproducible and highly statistically significant for both constructs (p value < 0.0001 and 0.0004, respectively). The introduction of the p53 DNA binding domain mutants (R273H, R175H, R248W, G154V), or the p53 N-terminal multi-site phosphorylation mutant yielded no change in the percent of GFP + from the vector alone. Thus, introduction of wt p53 into the T98G cells suppressed the high level HDR allowed by the endogenous mutant p53 [20].

Co-Expression of IE72 and wt p53 Negated Their Individual Effects on HDR
Surprisingly, in the T98G microenvironment, expression of either IE72 or wt p53 reduced HDR, therefore we tested co-introduction of both proteins. Using the modified protocol described above, once again control experiments using dL70-3 virus produced no GFP + cells (data not shown). We observed that nucleofection with no DNA, or introduction of backbone vector (pCDNA3) followed by I-SceI-expressing NGUS24i produced an average ~9% GFP + cells. Introduction of either IE72 or wt p53 alone reduced GFP + to ~5%, an ~2 fold decrease as expected, both of which were statistically significant (p = 0.01 and p = 0.0014, respectively). Much to our surprise, dual introduction of IE72 and wt p53 produced an average of 13.6% GFP + cells, an ~1.4 fold increase from the baseline rate of ~9%, an increase that was only marginally statistically significant (p = 0.043) (fold changes of 1.3, 1.7, 1.5 in three experiments) ( Figure 3C). This suggested that in T98G cells interaction between wt p53 and IE72 negated their individual effects on the HDR machinery.

In Vitro Binding Assays Found Both wt p53 and IE72 Bound Rad51
T98G cells harbor a mutant p53 (R273H mutation) [20]. The mutation abolishes specific DNA binding. The p53 transient expression experiments established that an intact DNA binding domain and phosphorylatable N-terminus were required to decrease HDR in these cells. Previous studies have determined that recombination is controlled, at least in part, by p53 binding to the strand invasion protein Rad51, which modulates Rad51's function (as reviewed in [21]). An intact DNA binding domain in the p53 protein is required for this interaction [23][24][25]. Further, T98G cells mount a DSB response [20] and express high levels of the DSB repair proteins, including Rad51 [20,26]. Previous work had established that p53 could bind both Rad51 and IE72 [27], and that interaction between p53 and IE72 could negate p53's normal DNA binding ability through its core region [27]. This information suggested that the effects noted in the above transient expression and co-expression experiments might be the result of IE72 binding either wt p53 or Rad51. In vitro mixing experiments of radiolabeled Rad51 and pGEX72, GST wt p53 or pGEX-KG (control GST alone) were performed. As can be seen in Figure 4, both p53 and IE72 were capable of binding Rad51, although IE72 appeared to be slightly less avid for the protein. While not excluding other possible explanations, this binding, in combination with the results from the expression experiments, have prompted us to propose a protein interaction model capable of explaining the observed behaviors (see Figure 5 below). This model may have significant bearing on questions entirely unrelated to HCMV-infection in the semi-permissive cell type T98G.   [15,16]) were grown and titrated in replicationpermissive human 293A cells. The MOI of Ad viruses used was established separately depending on experimental procedure (see above for particular details).

Transfections
Lipofectamine reagent (Invitrogen/Life Technologies, Grand Island, NY, USA) was used according to manufacturer's instructions to introduce pDRGFP [13,14] into T98Gs. Following transfection, cells were grown in non-selective medium for 48 h. Subsequently, puromycin (1 g/mL) was added to the media to select for integrated pDRGFP. Cloning rings were used to isolate puromycin-resistant colonies. Colonies were separated and subjected to another cycle of puromycin selection prior to freezing and storage in liquid nitrogen. All colonies were tested to ensure the absence of GFP + without DSB induction. Three stable clones of T98GpDRGFP were tested and produced comparable results in the assays described above. T98GpDRGFP clone 10 (Clone 10) was used for all experiments reported here.

Molecular Cloning
The pCDNA3-T7 vector was modified by insertion into its polylinker of a BamHI-NotI fragment containing DsRed2 (from pDsRed2-N1vector; Clontech Laboratories, Mountain View, CA, USA) to produce pCDNA3-DsRed. The wt p53 promoter-wt p53 coding sequence was cleaved from pCLNCX-p53pro-p53GFP [22] at the KpnI and AgeI sites. Finally, this fragment was inserted upstream of DsRed2 in the polylinker of pCDNA3-DsRed. This ultimately produced the pCDNA3-p53pro-p53DsRed construct. Dual transient expression studies with wt p53 and IE72 used 3 µg each of pCDNA3-p53pro-p53DsRed and pSGIE72. Alternatively, one of these constructs (3 µg) was transfected along with the corresponding control backbone of the other construct (3 µg).

DSB Repair Assay
The DSB repair assay was carried out as described previously [12]. The results reported were carried out in mock-or HCMV-infected Clone 10.

Virus Infection
Clone 10 was synchronized in G o by serum starvation for 3 d. Cells were then washed with PBS, trypsinized and re-plated at a density of 5 or 10 × 10 5 cells/60-mm dish containing glass coverslips. After allowing 2 h for attachment, the cells were mock-or HCMV-infected. Twenty-four hours later the virus or mock inoculums were removed (unless otherwise noted). After an additional 24 h, cells were washed once with PBS and subsequently superinfected for 30 minutes in pre-warmed media. Super-infection was with control Ad dL70-3 or with I-SceI-expressing Ad NGUS24i [12]. Cells were then re-fed with complete media. Cells were fixed and permeabilized 72 h later unless otherwise noted. Coverslips were scored for GFP + cells and IE72 + cells.

Immunofluorescence (IF)
Cells were seeded into dishes containing glass coverslips. Coverslips were collected and processed at indicated times pi, as described previously [30]. 300-500 cells per coverslip were counted and scored for GFP + and either HA + or IE72 + cells in each experiment. All experimental results represent the average of at least two independent experiments. Error bars represent +/− one standard deviation (SD). Statistical analysis was performed using unpaired, two-tailed student t-tests.
Assays were performed using one of the 5 mL aliquots thawed on ice. Fifty L of a 10 mg/mL lysozyme solution was added and the cell suspension was incubated on ice for 30 min. The suspension was sonically disrupted to lyse the cells. Debris was pelleted at 10,000 rpm for 5 min and the supernatant transferred to a new tube. Sixty L of a 50% glutathione agarose bead slurry (vol/vol in NETN) was added and rocked at 4 °C for 1 h. Beads were then washed in 500 L NETN + PIs three times prior to final re-suspension in 500 L NETN + PIs. Fifty L of this bead suspension (25 L bead equivalents) were incubated with 20 L Laemmli reducing sample buffer (2% SDS, 10% glycerol, 100 mM DTT, 60 mM Tris pH 6.8, Bromophenol blue dye, aprotinin and leupeptin (2 g/mL each)), and then boiled for 5 min. The resulting supernatant was loaded onto a 12% SDS-PAGE gel. The gel was stained with Coomassie dye to visualize the GST fusion proteins and to determine equivalent protein amounts for use in the following binding reactions. Roughly equivalent amounts of protein, as estimated from the Coomassie staining, were used in the reactions (approximately 30 L beads of pGEX72 and pGEX-p53 and 7.5 L of pGEX-KG were used in the reactions shown in Figure 4).
Pet24d-Rad51 [34] was in vitro translated using the Promega quick-coupled TNT reaction kit per manufacturer's instructions. In each binding assay, five L of translated Rad51 (or just TNT reaction mixture) was incubated while rocking for 1 h at 4 °C with the GST fusion proteins in a 500 L reaction of NETN + PIs. After incubation, 40 L of a 50% Protein A Sepharose slurry (vol/vol in NETN) was added to each tube (to increase the bead volume). Ten percent of the total volume (54 L) was removed to serve as the input fraction (no washes of these beads followed). The beads in the remaining sample were pelleted and then washed four times with 500 L of buffer (the first two washes in NETN, the last two in RIPA (150 mM NaCl, 1% Nonidet P-40, 0.5% deoxycholate, 0.1% SDS, 50 mM Tris (pH 8), 5 mM EDTA). After the final spin, 15 L of urea sample buffer was added to the beads and the samples were boiled for 5 min. The supernatants were run on a 12% SDS-PAGE gel. Gels were dried and exposed to Kodak X-omat film.

Conclusions
A cell's ability to repair insult to its DNA is a process that is essential to its survival. The concept that virus infection may alter this capability has begun to be investigated. In theory a virus might benefit from manipulation of the repair processes, while simultaneously (and likely corollary) degrading the integrity of a host's genome. Existing studies have examined a variety of different repair pathways in the context of expression of a single viral protein and have assessed that protein's capability to influence the repair of exogenously introduced damage in the cellular DNA [35][36][37][38][39][40][41][42][43][44][45][46]. The effects on repair during a complete infection have also been examined [12,[47][48][49][50][51][52][53][54][55][56]. A handful of these studies have looked specifically at HDR [12,35,37,45,49]. The very large majority of these studies found viral protein expression (or full infection) decreased cellular repair capabilities.
Two cellular proteins, p53 and Rad51, are exceedingly important in the context of HDR. It is widely accepted that wt p53 regulates HDR, primarily through interactions with Rad51 (as reviewed in [21]). In vitro reactions have found p53 binds Rad51 and, by means of this interaction, inhibits strand exchange and branch migration of recombination intermediates [57]. An intact core DNA binding domain of the p53 protein is required for direct binding between the p53 and Rad51 proteins (and inhibition of the latter's activities) [23][24][25]. It has also been shown that following induction of damage wt p53 inhibits transactivation of the Rad51 promoter, thereby acting as a repressor of Rad51 transcription and protein expression [23]. It has also been shown that a phosphorylatable N-terminus is important for regulation and binding of Rad51 [58]. Many tumors (and cell lines derived from them) have so called -hotspot‖ mutations in p53, including the T98G cells used in this study [20]. Reintroduction of wt p53 into these tumor cells reduced HDR via decreasing Rad51 activity [59].
Multiple viruses have been shown to interact with the Rad51 protein. Epstein-Barr Virus (EBV), a herpesvirus, and SV40 have both been found to require the presence of Rad51 within their viral replication centers. Knockdown of Rad51 in infected cells dramatically decreased viral replication [60,61]. The opposite was true for HIV; stimulation of Rad51 activity inhibited HIV replication, primarily due to an inhibitory effect of Rad51 on the virus' Integrase enzyme [62].
The context of the T98G cellular environment is important for consideration of HDR in these cells. Under normal stress T98Gs can complete HDR and have normal levels of Rad51 [20]. Baseline levels of HDR in T98G cells after insult are quite high, perhaps in part due to the large proportion of these rapidly dividing cells being in the S/G2 phases of the cell cycle, during which HDR is most common [25,59]. However, the higher rate of HDR may also be attributable to the presence of the mutation in the p53 DNA binding domain (R273H) within these cells [20], which prevents p53's normal binding to, and regulation of, Rad51.
The model in Figure 5 is proposed to explain the dichotomy of results between the T98G results reported here and our earlier HFF study. In wt cells p53 normally inhibits Rad51 activity. In the model, the prediction is that the mutant p53-containing T98G cells, where p53 cannot bind Rad51 [23][24][25], would have high baseline recombination rates, as seen. The model further predicts that, introduction on their own (indicated by the arrow in the figure) of either wt p53 or IE72 (which we have shown can bind Rad51 in vitro) into these cells would decrease HDR by their binding to and inhibiting Rad51. Co-expression of both wt p53 and IE72 did not inhibit Rad51. We propose in these experiments wt p53 and IE72 bound to one another, inhibiting normal p53 activity [27]. This seems a reasonable assumption, given previous reports that IE72 binds directly to p53 and inhibits core domain binding ability [27]. If the introduced wt p53 and IE72 were interacting and p53's normal activity was inhibited, Rad51 would again be free to cause the high recombination rates observed. Also in our model, in wt cells p53 normally inhibits Rad51, establishing baseline HDR activity levels. HCMV-infection of wt fibroblast cells causes p53 to become tightly associated with the viral replication centers [63]. We believe in an infected cell this viral manipulation of p53 prevents the normal binding of p53 and Rad51. The disruption of this normal condition in turn frees Rad51 to increase recombination in both the viral and cellular DNA. Experiments in wt cells found that expression of the single HCMV protein IE72 was sufficient to increase HDR levels [12]. The proposed model attributes the increase in HDR in wt cells to IE72 binding to wt p53, thereby inhibiting the ability of wt p53 to bind and regulate Rad51.
The proposed model offers a mechanistic explanation for our previously reported results in wt HFFs [12]. The focus of the earlier HFF study was the recognition of viral manipulation of a cellular repair mechanism to its own ends in the context of a fully permissive infection. The experiments performed in T98G cells were expected to corroborate that data. Fortuitously, the particular microenvironment of the mutant glioblastoma cells allowed elucidation of what we believe to be the protein interactions responsible.
The results of this study may have implications for the initiation of glioblastomas. It has been reported that in excess of 90% of glioblastomas contain HCMV genetic material [64,65]. A latently HCMV-infected neural progenitor/stem cell which reactivated could, and most likely would, express IE72. We have found that expression of IE72 in a wt cell was capable of increasing unscheduled recombination, a hallmark of genetic instability and perhaps the first step toward hyperproliferation and gliomagenesis. The common perception of HCMV as a relatively harmless pathogen belie not only its capacity to cause serious birth defects, inflict direct DNA damage and downregulate repair of the cellular genome, but perhaps its capability to initiate an often lethal and devastating cancer. We have experiments underway to determine if the protein interactions found in this study are capable of intitiating oncogenic effects in tissue culture experiments.

Figure 5.
A model for the interactions between wt p53, Rad51 and IE72 in HFFs and T98Gs. Interactions between these three proteins and the ramifications to HDR depend completely on the cellular microenvironment and the presence or absence of wt p53. See text for description.