extfieldsofstudy: Medicine Biology

text:
NDR2 Acts as the Upstream Kinase of ARK5 during Insulin-like Growth Factor-1 Signaling*

ARK5 is a tumor progression-associated factor that is directly phosphorylated by AKT at serine 600 in the regulatory domain, but phosphorylation at the conserved threonine residue on the active T loop has been found to be required for its full activation. In this study, we identified serine/threonine protein kinase NDR2 as a protein kinase that phosphorylates and activates ARK5 during insulin-like growth factor (IGF)-1 signaling. Upon stimulation with IGF-1, NDR2 was found to directly phosphorylate the conserved threonine 211 on the active T loop of ARK5 and to promote cell survival and invasion of colorectal cancer cell lines through ARK5. During IGF-1 signaling, phosphorylation at three residues (threonine 75, serine 282, and threonine 442) was also found to be required for NDR2 activation. Among these three residues, phosphorylation of serine 282 seemed to be the most important for NDR2 activation (the same as for the mouse homologue) because its aspartic acid-converted mutant (NDR2/S282D) induced ARK5-mediated cell survival and invasion activities even in the absence of IGF-1. As in the mouse homologue, threonine 75 in NDR2 was required for interaction with S100B, and binding was in a calcium ion- and phospholipase C-γ-dependent manner. We also found that PDK-1 plays an important role in NDR2 activation especially in the phosphorylation of threonine 442. Based on the results of this study, we report here that NDR2 is an upstream kinase of ARK5 that plays an essential role in tumor progression through ARK5.

ARK5 also called as KIAA0537/NUAK1 is a member of the AMPactivated protein kinase (AMPK) 2 catalytic subunit family and is closely involved in tumor progression, e.g. in colorectal cancer and multiple myeloma, upon stimulation with insulin-like growth factor (IGF)-1 (1)(2)(3)(4). ARK5 is unique among AMPK catalytic subunit family members in that its activation is directly regulated by AKT through the phosphorylation of a serine residue on the regulatory domain (1). AKT-activated ARK5 promotes tumor invasion and metastasis through induction of membrane type-I matrix metalloproteinase (2), and it protects tumor cells from cell death caused by nutrient starvation (1,5) and activation of death receptors, such as tumor necrosis factor-␣ (5), tumor necrosis factor-related apoptosis-inducing ligand receptor (5), and FAS/CD95 (6), as a result of an inactivation of caspase-6 (6). We recently detected a stage-dependent increase in the amount of ARK5 mRNA in clinical specimens from patients with colorectal cancer (3), and we have demonstrated that expression of ARK5 mRNA is strongly correlated with a poor prognosis of multiple myeloma (4). Thus, ARK5 appears to be an important factor for tumor progression.
Serine/threonine protein kinase AMPK acts as the metabolic sensor in a wide variety of species, for example in mammalian cells, insect cells, and yeast, and its activation has usually been observed in cells exposed to metabolic stress, such as hypoxia and nutrient starvation, and in skeletal muscle cells during exercise (7)(8)(9)(10). In addition to metabolic stress, AMPK activation has also been observed in cells stimulated with leptin (11) and adiponectin (12), and thus its molecular regulation has been the focus of diabetes and obesity research (11,12). In addition to their role in these disease states, we recently reported that AMPKs and related kinases play an essential role in tumor progression (1,(13)(14)(15). AMPKs, especially AMPK-␣1, have been reported to be activated by the upstream kinase through the phosphorylation of the threonine residue on the active T loop. Calmodulin-dependent protein kinase kinases (CaMKKs) have been reported to be the upstream kinases that activate AMPK-␣ (16,17). Another kinase was recently purified as a result of yeast studies, and serine/threonine protein kinase LKB1 has been identified as its mammalian homologue (18 -21). It has also been reported that LKB1 acts as the master kinase of all AMPK catalytic subunit family members in cells under metabolic stress (22). We and other research groups, however, recently reported LKB1-independent phosphorylation of AMPK catalytic subunit family members in cells stimulated with growth factor (23) or with 5-amino-4-imidazolecarboxamide ribose (24).
The same as other AMPK family members, ARK5 contains a highly conserved active T loop, suggesting the presence of an upstream kinase that phosphorylates the threonine residue in the catalytic domain. Activation of ARK5 by growth factors, as well as metabolic stress, is important to understand the mechanisms of ARK5 regulation under physiological and pathological conditions. In the current study, we therefore attempted to identify the upstream kinase of ARK5 during IGF-1 signaling. Based on the result of a bioinformatic analysis using the active site of CaMKKs, we identified a protein kinase as a candidate and demonstrated that the kinase acts as the upstream kinase of ARK5 during IGF-1 signaling. Here we report NDR2 as a novel ARK5 kinase that acts in cells during IGF-1 stimulation.

MATERIALS AND METHODS
Cell Lines and Culture-Human colorectal cancer cell lines HCT-116, DLD-1, and SW480 were purchased from ATCC and maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum (Sigma). For transfection, cells were seeded into a 24-well plate at 5 ϫ 10 4 /well, and transfection was performed with TransFast transfection reagent (Promega Corp., Madison, WI). The transfection yield was assessed with a green fluorescent protein expression vector, and the yields in the current study were 97.3% for HCT-116 cells, 98% for DLD-1 cells, and 98.6% for SW480 cells.
Preparation of Recombinant NDR2-MBP-NDR2 (wild type and mutants) containing the tobacco etch virus protease site, was prepared by generating PCR product with KIAA0965 plasmid as template and then subcloning it with TOPO-TA cloning pCR2.1 vector. After sequencing, the plasmid was digested with XbaI and HindIII, ligated into pMal-c2X (New England Biolabs, Inc., Beverly, MA), and transformed into pBL21(DE3) GC-rich version Escherichia coli. MBP-ARK5/CD was purified with amylose resin, and the recombinant protein was eluted with maltose.
RNA Extraction and RT-PCR-Total RNA extraction from cell lines and tissue samples was performed with ISOGEN (Wako Pure Chemical Co., Ltd.) for RT-PCR. The extracted RNA (100 ng) was reverse transcribed to first strand cDNA with avian myeloblastosis virus reverse transcriptase (Takara Biochemicals) and oligo(dT) 16 primer, and then PCR was performed with LA PCR kit version 2.1 (Takara Biochemicals).
siRNA Preparation-siRNA was prepared with HiScribe siRNA transcription kit purchased from New England Biolabs Inc. Each PCR product (LKB1, 1851-2099 bp; CaMKK-2a, 4476 -4695 bp; CaMKK-2b, 1564 -1805 bp; NDR1, 2717-2895 bp; NDR2, 1620 -1853 bp) was subcloned by ligation into pCR2.1-TOPO vector (Invitrogen). After sequencing, the vectors were digested with BamHI and XhoI and religated into 28i-pLITMUS vector. The siRNA was prepared by in vitro transcription with T7 RNA polymerase. The efficacy of the siRNA was assessed by the disappearance of the target factor in Western blotting and/or RT-PCR procedure. As a control for the siRNA, double-stranded RNA prepared by the in vitro transcription of empty 28i-pLITMUS vector was used as recommended in the instruction manual.
Immunoprecipitation and Western Blotting-Antibody against HA was mixed with 1 mg of protein from each cell extract prepared by lysing cells with 0.1% Nonidet P-40 containing PBS for 30 min. After mixing, protein G-Sepharose was added and incubated for 2 h. The immunoprecipitates were washed six times with 0.1% Nonidet P-40 containing PBS and then heated in SDS sample buffer for Western blotting.
Proteins were prepared for Western blotting analysis by lysing cells for 30 min with 1% Nonidet P-40 containing PBS. All procedures were carried out at 4°C. Supernatants were collected by centrifugation at 15,000 rpm for 15 min, and their protein concentrations were determined with a BCA protein assay kit (Pierce) and bovine serum albumin as the standard. Sample proteins separated by SDS-PAGE were transferred onto nitrocellulose membranes by a semidry blotting system. The membranes were blocked at room temperature for 1 h with PBS containing 5% (w/v) skim milk, washed with a mixture of PBS and 0.05% Tween 20 (Sigma, Tween-PBS), and then incubated overnight at room temperature with antibody diluted with PBS. After washing with Tween-PBS, the membranes were incubated with 2000-fold diluted biotinylated horseradish peroxidase-conjugated anti-rabbit IgG antibody. The membranes were then washed with Tween-PBS and developed with the ECL system (Amersham Biosciences).

RESULTS AND DISCUSSION
Phosphorylation of Threonine 211 Is Essential for ARK5 Activation during IGF-1 Signaling-We have reported direct ARK5 activation by AKT through phosphorylation of serine 600 in the regulatory domain during IGF-1 signaling in tumor cells (1). Although phosphorylation of the serine residue is essential for ARK5 activation, the phosphorylation of a serine or threonine residue on the active T loop in the catalytic domain is also suspected of being necessary for ARK5 activation. Because phosphorylation of threonine 211 by LKB1 during metabolic stress has recently been reported (22), in the present study we investigated whether phosphorylation of threonine 211 is essential for ARK5 activation during IGF-1 signaling.
Phosphorylation of threonine 172/174 on AMPK-␣1/␣2 has been reported to be essential for activation (16,22,25), and multiple alignment of the catalytic domains of AMPK-␣1, AMPK-␣2, maternal embryonic leucine zipper kinase (MELK), SNF-1/AMPK-related kinase (SNARK), and ARK5 revealed that threonine 211 and the surrounding amino acid sequence of ARK5 is well conserved within AMPK catalytic subunit family members (Fig. 1A). To determine whether phosphorylation at threonine 211 is essential for activation of ARK5, we investigated the effect of alanine substitution of the threonine residue by a cell invasion assay because we previously found that ARK5 activation is crucial for IGF-1-induced tumor invasion of colorectal cancer cell lines (2). As previously reported, human colorectal cancer cell line SW480

NDR2, an Upstream Kinase of ARK5
intensely expressed ARK5 mRNA, and expression was weaker in cell lines HCT-116 and DLD-1 (Fig. 1B). Unfortunately we and others have not yet been able to produce antibody for phosphorylated ARK5 (ARK5/ Thr(P)-211 and ARK5/Ser(P)-600). However, we recently reported that the phosphorylation status of ARK5 is detectable by Western blotting using commercial antibodies against threonine 172-phosphorylated AMPK-␣ (for Thr(P)-211) and AKT substrate antibody (for Ser(P)-600) (2,26,27). Using these antibodies, we investigated the phosphorylation status of ARK5 (wild type and T211A mutant) in cells before and after IGF-1 treatment. Although phosphorylation of both threonine 211 and serine 600 in wild-type FLAG-ARK5 was observed upon stimulation with IGF-1, only serine 600, but not threonine 211, of mutated FLAG-ARK5 in which threonine 211 was converted to an alanine residue was phosphorylated during IGF-1 signaling (Fig. 1C). Interestingly IGF-1 accelerated the invasion activity of HCT-116 and DLD-1 cells when wild-type ARK5 was transfected; however, the invasion was significantly less or even suppressed by T211A mutant expression as well as by S600A mutant expression (Fig. 1D). In addition, SW480 cells exhibited strong invasion activity upon stimulation by IGF-1, and it was also suppressed by T211A or S600A mutant expression (Fig. 1E), suggesting that the phosphorylation of threonine 211 is essential for ARK5 activation.
Identification of NDR2 as a Novel Upstream Kinase of ARK5 during IGF-1 Signaling-In the present study, we found that threonine 211 must be phosphorylated during ARK5 activation upon stimulation with IGF-1. In addition, our current investigation showed that cell extracts from IGF-1-exposed HCT-116 cells phosphorylate threonine 211 of ARK5 as shown in Fig. 2A. Because only wild type, but not threonine 211 mutants (T211D and T211A), was phosphorylated by cell extracts of IGF-1-treated cells ( Fig. 2A), we suggest that these cell extracts contain the upstream kinase of ARK5 that is activated during IGF-1 signaling. Two serine/threonine protein kinases, CaMKK-2 and LKB1, have been identified as upstream kinases phosphorylating a threonine residue on the active T loop in the catalytic domain of the AMPK catalytic subunit family (16, 17, 19 -21). It has also been reported that both protein kinases exhibit a well conserved region in the catalytic domain (18). We first investigated whether other unknown protein kinases carry a similar sequence using a BLAST search (versus protein sequences listed in the National Center for Biotechnology Information library) and identified NDR1 (also called STK38) and NDR2 (also called STK38L/KIAA0965) as candidates for the upstream kinase of ARK5. Interestingly NDR2 mRNA was strongly expressed in SW480 cells that exhibited ARK5mediated tumor invasion (Fig. 2B). By contrast, mRNA expression of LKB1, CaMKK-2a, CaMKK-2b, and NDR1 was very weak in SW480 cells. To identify the kinase responsible for ARK5 activation induced by IGF-1, we constructed a series of siRNAs. As shown in Fig. 2C, each siRNA suppressed expression of each target factor. As shown in Fig. 2D, when HCT-116 cells were exposed to IGF-1, the cell extracts phosphorylated the recombinant protein of human ARK5 catalytic domain (rARK5/CD). Interestingly complete suppression of the IGF-1-induced rARK5/CD phosphorylation resulted when the siRNA for NDR2 was introduced into HCT-116 cells (Fig. 2D). Although a decrease in phosphorylation by about half occurred when siRNA for NDR1 was introduced, the siRNA for other factors did not suppress it at all (Fig. 2D). In addition, cell extracts from IGF-1-exposed HCT-116 cells phosphorylated the recombinant protein of human AMPK-␣1 catalytic domain (rAMPK/CD), but none of the siRNA tested in this study suppressed it (Fig. 2D). By contrast, siRNA for LKB1 and CaMKK-2a completely and partially suppressed AMPK-␣1 phosphorylation by glucose starvation, but the others did not suppress it at all (Fig. 2D). We recently reported that AMPK-␣1 is phosphorylated in an LKB1-independent manner during IGF-1 signaling and that LKB1 plays an essential role in AMPK-␣1 phosphorylation only in cells exposed to metabolic stress, e.g. glucose starvation (21,23). Thus, the results of the present study are consistent with our previous observations.
In addition to the cell-free assay, we also discovered that NDR2 plays a critical role in ARK5 activation by using a tumor invasion assay system.

NDR2, an Upstream Kinase of ARK5
As shown in Fig. 2E, tumor invasion activity of SW480 cells stimulated with IGF-1 was suppressed by introduction of the siRNA for NDR2 but not by introduction of the others. Based on these results, we concluded that NDR2 is a novel upstream kinase of ARK5 during IGF-1 signaling, and because no involvement of NDR2 in AMPK-␣1 phosphorylation was observed in this study, NDR2 seemed to be a specific kinase for ARK5.
NDR2 Is Activated in a PDK-1-dependent Manner during IGF-1 Signaling-NDR2 was also identified by an analysis of cDNA synthesized from human brain at the KAZUSA DNA Research Institute (Japan), and they numbered it KIAA0965 (GenBank TM accession number AB023182). Regulation of the cell cycle and morphogenesis have been reported to be the roles of the NDR family AGC kinases (28). To investigate the mechanism of NDR2 activation during IGF-1 signaling, as shown in Fig. 3A we constructed its mammalian expression plasmid with HA tag (HA-NDR2) and MBP fusion protein produced in E. coli (MBP-NDR2). Because the amino acid sequence revealed that NDR2 contains a PDK-1 docking motif on its C-terminal side (amino acids 438 -443) on which PDK-1 can bind and phosphorylate the target factor, just like in the case of AKT activation by PDK-1, a mutant lacking the PDK-1 docking motif was also constructed in mammalian expression vector (Fig. 3A), and the HA-tagged wild-type NDR2 and PDK-1 docking motif (PDM)-deleted mutant (HA-NDR2/⌬PDM) were successfully expressed in HCT-116 cells (Fig. 3B). As shown in Fig. 3C, our current results showed that IGF-1-stimulated cell extracts induced the phosphorylation of the PDM region in NDR2. In addition, the phosphorylation was suppressed when cells were pretreated with LY294002 but not with U0126 (Fig. 3C), suggesting that the intracellular signaling is mediated by LY294220-sensitive factors, such as phosphatidylinositol 3-kinase and PDK-1. To further investigate an interaction between  20 M U0126 were exposed (ϩ) or not exposed (Ϫ) to 100 ng/ml IGF-1, cell extracts were mixed with MBP-NDR2, and then PDK-1 docking motif-phosphorylated NDR2 (pPDM) and total NDR2 (tNdr2) were detected by Western blotting with anti-MBP antibody after purification on an amylose resin column. D, cells transfected with expression vector containing wild-type or threonine 442-mutated (Ala-converted, TA; Asp-converted, TD) HA-NDR2 (Ndr2-T442) were exposed to medium containing (ϩ) or not containing (Ϫ) 100 ng/ml IGF-1, and immunoprecipitates with anti-HA antibody (HA-IP) were blotted with antibody against PDK-1, phospho-PDM, or HA. IB, immunoblot; Ab, antibody. E, expression vector containing wild-type or deletion mutant (⌬PDM) HA-NDR2 was introduced into HCT-116 cells, and immunoprecipitation with anti-HA antibody was performed after IGF-1 treatment (100 ng/ml for 2 h). Immunoprecipitates were blotted with antibody against human PDK-1 or HA (Ndr2). F, HCT-116 cells transfected with expression vector containing HA-NDR2 were exposed or not exposed to 100 ng/ml IGF-1 for 2 h followed by pretreatment with (ϩ) or without (Ϫ) 20 M LY294002 or 20 M U0126, and immunoprecipitates with anti-HA antibody were blotted with antibody against human PDK-1 or HA (Ndr2).

NDR2, an Upstream Kinase of ARK5
PDK-1 and NDR2 during IGF-1 signaling, wild-type, T442A mutant, and T442D mutant NDR2 were overexpressed in cells. As shown in Fig.  3D, wild-type but not T442D-mutated NDR2 showed an interaction with PDK-1 and PDM site phosphorylation. Interestingly any interaction between T442D mutant and PDK-1 was not observed; however, T442A-mutated NDR2 exhibited an interaction but not PDM site phosphorylation. AKT1 is also one PDK-1 substrate, and it is well known that the activation is caused by PDK-1 through binding via the PDM sequence of AKT and phosphorylation (29 -31). Thus, it seems to be that phosphorylation is promoted after interaction. Therefore, we suggest that PDK binds with NDR2 and phosphorylates the threonine residue within the PDM sequence during IGF-1 signaling.
In this study, we showed that the IGF-1-induced phosphorylation of ARK5, especially threonine 211, was suppressed by the siRNA for NDR2 and that NDR2 was also phosphorylated during IGF-1 signaling. This strongly suggests that NDR2 phosphorylates threonine 211 of ARK5 upon stimulation by IGF-1. A pull-down assay using HA-tagged NDR2 was performed to examine the interaction between NDR2 and PDK-1 during IGF-1 signaling. As shown in Fig. 3E, PDK-1 was detected in anti-HA antibody immunoprecipitates from wild-type HA-NDR2transfected HCT-116 cells but not from HA-NDR2/⌬PDM-transfected HCT-116 cells, and the interaction was IGF-1-dependent. The IGF-1induced interaction between NDR2 and PDK-1 was found to be phosphatidylinositol 3-kinase-dependent and mitogen-activated protein kinase-independent because LY294002, but not U0126, suppressed it (Fig. 3F). These results suggest that PDK-1 plays an essential role in NDR2 activation through binding to its docking motif on NDR2 during IGF-1 signaling. We have demonstrated previously that IGF-1 stimulates tumor invasion through ARK5 activation, and the role of PDK-1/ NDR2/ARK5 activation in tumor invasion was examined. As shown in Fig. 3G, HCT-116 and DLD-1 cells, in which ARK5 expression is weak, exhibited increased invasion activity when ARK5 or NDR2 was introduced, and IGF-1 accelerated the invasion activity. In addition, the invasion activity was suppressed by introduction of DN-ARK5 or NDR2/ ⌬PDM, and the acceleration of activity by IGF-1 in ARK5-overexpressing cells was suppressed by NDR2/⌬PDM (Fig. 3G). When SW480 cells, in which ARK5 mRNA is strongly expressed, were used, NDR2/⌬PDM had a similar inhibitory effect (Fig. 3H). These results suggest that NDR2 regulates the ARK5-mediated tumor invasion activity during IGF-1 signaling, and we identified NDR2 as a novel upstream kinase of ARK5 during IGF-1 signaling.
The regulation of NDR kinases has been well investigated, and the phosphorylation of threonine residues in the C-terminal region by unidentified upstream kinase(s) is well known (28,(32)(33)(34). Although we raised the possibility that PDK-1 activates NDR2 in cancer cells during IGF-1 signaling in this study, PDK-1 is not involved in the phosphorylation of the C-terminal threonine in NDR1 (35). The amino acid sequence of NDR2 shows high similarity to that of NDR1, but the subcellular localization is quite different (NDR1 is found in nuclei; NDR2 is found in cytoplasm (33)), suggesting the presence of different mechanisms activating NDR kinases. In addition, recent progressive investigations revealed MOB protein kinases (33) and STE20-like kinase family MST3 (34) as candidates of the upstream kinase(s), but these kinases in cancer cells seem to act as tumor suppressors (36). ARK5, which is the substrate candidate of NDR2 in cancer cells during IGF-1 signaling, is a tumor progression-associated factor (2), and the role of NDR kinases in non-tumor cells, such as neuronal cells, has been reported recently (37). Although the NDR kinases may be regulated by STE20-like kinases and MOB kinases in non-tumor cells, we suggest that the regulation of NDR kinases, especially NDR2, is initiated by PDK-1 as one of the signal transduction pathways, such as tumor progression signaling stimulated with IGF-1.
NDR2 Interacts with S100B in a PLC-␥-dependent Manner during IGF-1 Signaling-As described above, a BLAST search analysis revealed NDR2 as a novel ARK5 kinase, and the biochemical properties of NDR2, especially the phosphorylation sites required for activation, have been well characterized. The threonine residue on the N-terminal side of mouse NDR2 is phosphorylated during interaction with S100B in the presence of calcium ion, and an unknown upstream kinase phosphorylates the threonine residue on the C-terminal side (32). After phosphorylation of both threonine residues, the serine residue on the catalytic domain is autophosphorylated, and activated mouse NDR2 transduces signaling to the next factor (32). A comparison of the amino acid sequence of human and mouse NDR2 revealed that all phosphorylation sites and surrounding sequences are well conserved (data not shown). Next we investigated whether an interaction between S100B and the threonine residue on the N-terminal side of human NDR2 in the presence of calcium ion is triggered by IGF-1 the same as in mouse homologue. Because the sequence around threonine 75 of human NDR2 was highly homologous with the S100B binding site of mouse NDR2, we constructed alanine-(T75A/HA-NDR2) or aspartic acid (T75D/HA-NDR2)-substituted mutants in this study. As shown in Fig.  4A, wild-type and mutated NDR2 were expressed in HCT-116 cells, and S100B was detected in immunoprecipitates with anti-HA antibody from cells transfected with wild-type vector and exposed to IGF-1. The same results were obtained in cells transfected with T75D/HA-NDR2 but not with T75A/HA-NDR2 (Fig. 4A). Because T75D/HA-NDR2 interacted with S100B even in the absence of IGF-1 stimulation and no interaction between T75A/HA-NDR2 and S100B was observed (Fig. 4A), we concluded that S100B interacts with NDR2 through threonine 75, the same as in mouse NDR2, and that phosphorylation of the threonine residue is required for their interaction. The effects of BAPTA (chelator of intracellular calcium ion), U-73122 (inhibitor of PLC-␥), bisindolylmaleimide-1 (inhibitor of protein kinase C), genistein (inhibitor of non-receptor tyrosine kinase), and LY294002 (inhibitor of phosphatidylinositol 3-kinase) were examined in relation to the mechanism of the interaction between S100B and NDR2 during IGF-1 signaling. As shown in Fig. 4B, the interaction between S100B and NDR2 induced by IGF-1 was sup- . PLC-␥-dependent interaction between NDR2 and S100B during IGF-1 signaling. A, HCT-116 cells transfected with expression vector containing wild-type (WT), T75A (TA), or T75D (TD) HA-NDR2 were exposed for 2 h to medium containing (ϩ) or not containing (Ϫ) 100 ng/ml IGF-1. Cell extracts (CE) or immunoprecipitates with anti-HA antibody (␣HA-IP) were collected and blotted with antibody against S100B (␣S100B) or HA (␣HA). B, HCT-116 cells transfected with expression vector containing wild-type HA-NDR2 were exposed for 2 h to medium containing (ϩ) or not containing (Ϫ) 100 ng/ml IGF-1 followed by pretreatment with 0.1% Me 2 SO (DMSO), 50 M BAPTA, 20 M U-73122, 20 M bisindolylmaleimide-1 (BIM-1), 50 M genistein, or 20 M LY294002. Cell extracts or immunoprecipitates with anti-HA antibody were collected and blotted with antibody against S100B or HA. MAY 19, 2006 • VOLUME 281 • NUMBER 20 pressed by BAPTA and U-73122 but not by the others. These findings indicate that an increase in free intracellular calcium ion concentration is required for binding of S100B to NDR2. PLC-␥ has been reported to mobilize calcium ions upon stimulation of growth factor receptors, such as epidermal growth factor receptor, platelet-dependent growth factor receptor, and IGF receptor, by ligand ligation (38 -40). Thus, our findings suggest that the calcium ion required for the interaction between NDR2 and S100B is supplied by the PLC-␥-mediated pathway as a result of IGF receptor activation.

NDR2, an Upstream Kinase of ARK5
ARK5-mediated Tumor Cell Survival and Invasion Is Regulated by NDR2 through Three Point Phosphorylations-We have reported that SW480 cells express ARK5, FAS ligand, and FAS and that the suppression of ARK5 caused by introduction of antisense RNA, siRNA, or dominant-negative mutants leads to cell death through ligation of FAS ligand and FAS (6). In the current study, we further investigated the involvement of NDR2 in suppression of cell death through activation of ARK5 and its mechanism by constructing T75D, T75A, S282D, S282A, T442D, and T442A mutants of NDR2. As shown in Fig. 5A, SW480 cells died after introduction of two ARK5 mutants (ARK5/T211A and ARK5/S600A) that act as dominant negatives or the siRNA for ARK5, consistent with our previous report (6). Because the introduction of DN-AKT1 or siRNA for NDR2 into cells induced cell death (Fig. 5A), AKT and NDR2 act in the upstream portion of ARK5 for cell survival in SW480 cells. Interestingly three alanine-converted mutants, T75A/HA-NDR2, S282A/HA-NDR2, and T442A/HA-NDR2, also induced cell death (Fig. 5A), suggesting that phosphorylation of three amino acids (threonine 75, serine 282, and threonine 442) is required for NDR2 activation to promote ARK5-mediated cell survival in SW480 cells. In addition, IGF-1 accelerated tumor invasion activity of SW480 cells, and the acceleration was mediated by ARK5 because DN-ARK5 suppressed it (Fig. 5B). The invasion activity of SW480 cells was also suppressed by transfection of three alanine-converted mutants, and the same was true for acceleration of invasion activity by IGF-1 (Fig. 5B). It should be noted that aspartic acid-converted mutant of serine 282 (S282D/HA-NDR2), but not the other two aspartic acid-converted mutants (T75D/HA-NDR2 and T442D/HA-NDR2), significantly accelerated invasion activity even in the absence of IGF-1 (Fig. 5B). The same results were obtained when ARK5 was expressed in HCT-116 and DLD-1 cells (Fig.  5C). In the case of mouse NDR2, serine 282 has been reported as the most important amino acid residue for NDR2 activation (32). It was reported that autophosphorylation of serine 282 is initiated followed by threonine 75 and threonine 442 phosphorylation, and even aspartic acid conversion of serine 282 led to enough activity (32). Therefore, our current results confirm the previous study and suggest that phosphorylation of serine 282 is the most important step for NDR2 activation during IGF-1 signaling.
NDR2: New Function as the Upstream Kinase of ARK5 and Its Activation Mechanism during IGF-1 Signaling-ARK5 is the tumor invasion-associated factor that transduces IGF-1 signaling in tumor cells, and we identified NDR2 as a novel upstream kinase of ARK5 in the present study. Based on the results of this study, we concluded that the mechanism of ARK5 activation through NDR2 during IGF-1 signaling is as follows (Fig. 6). At least two intracellular signals are initiated to activate ARK5 upon stimulation by IGF-1. One signal activates PDK-1 through phosphatidylinositol 3-kinase activation. Activated PDK-1 transduces the signal to AKT, which, in turn, phosphorylates serine 600 on the regulatory domain of ARK5; activated PDK-1 also causes phosphorylation of threonine 442 of NDR2. The other is a calcium ion regulation signal through PLC-␥. Interaction between S100B and NDR2 through threonine 75 is promoted by mobilization of calcium ion as a result of PLC-␥ activation. After phosphorylation of these two threonine residues, serine 282 is autophosphorylated, and activated NDR2 phosphorylates threonine 211 in the catalytic domain of ARK5. Thus, ARK5 acts as the tumor invasion-associated factor through NDR2 during IGF-1 signaling.


extfieldsofstudy: Biology Medicine

text:
Viruses' Life History: Towards a Mechanistic Basis of a Trade-Off between Survival and Reproduction among Phages

Life history theory accounts for variations in many traits involved in the reproduction and survival of living organisms, by determining the constraints leading to trade-offs among these different traits. The main life history traits of phages—viruses that infect bacteria—are the multiplication rate in the host, the survivorship of virions in the external environment, and their mode of transmission. By comparing life history traits of 16 phages infecting the bacteria Escherichia coli, we show that their mortality rate is constant with time and negatively correlated to their multiplication rate in the bacterial host. Even though these viruses do not age, this result is in line with the trade-off between survival and reproduction previously observed in numerous aging organisms. Furthermore, a multiple regression shows that the combined effects of two physical parameters, namely, the capsid thickness and the density of the packaged genome, account for 82% of the variation in the mortality rate. The correlations between life history traits and physical characteristics of virions may provide a mechanistic explanation of this trade-off. The fact that this trade-off is present in this very simple biological situation suggests that it might be a fundamental property of evolving entities produced under constraints. Moreover, such a positive correlation between mortality and multiplication reveals an underexplored trade-off in host–parasite interactions.

Introduction
A fundamental assumption underlying many theories on the evolution of life history traits is the existence of constraints, leading to trade-offs, between various aspects of the life cycle [1,2]. For higher organisms, such as mammals, fishes, and birds, a major trade-off has been observed between fertility and longevity [3]. The mechanisms that are most commonly proposed to explain such a relation are the existence of metabolic constraints and the allocation of finite resources either to reproduction or to survival [4,5]. Based on this assumption, as an individual's fitness improves with an increase in its reproductive capacity, but is diminished by mortality; highest fitness is thus achieved as a compromise between these two traits. Theoretically, this should be true for any evolving entity that maximizes its fitness under constraints of limiting resources. These resources may be those produced by the organism itself, but they could also be someone else's resources as in the case of parasites that exploit their hosts.
To our knowledge, the existence of a trade-off between parasites' multiplication rate and survival has not been properly investigated. In the case of parasites, other relationships between traits are well known, such as the one between rapid exploitation of the host and the duration of the infection [6], or the impact on their virulence of the survival of parasites between hosts [7]. Over an important period of their life cycles, parasites transmitted via the environment have to survive outside their host before meeting a new susceptible host. Parasite survival in the environment is thus of great importance for an effective transmission. This is true of model organisms such as coliphages, which are phages that infect the bacteria Escherichia coli. Such phages have to survive between the bacteria they have just left and the one they will infect next, and there may be considerable variations in the period of time between these two events. Indeed, after reproduction on susceptible and accessible E. coli cells in an animal gut [8,9], phage particles are excreted in the external environment. In rare cases only, they can be transmitted to a new gut where they may meet new host bacteria. Therefore, phages might have then to survive between hosts for an extended period of time. Hence, if we consider that viruses are evolving entities, one can test the validity of the predictions of life history theory by examining whether the trade-off between survival and reproduction also applies to viruses, even though they do not have a metabolism of their own.
Little information is available on the determinants of the stability of phage particles. However, as some E. coli phages are model organisms for molecular and structural biology, complete structural information is available and allows investigation of the physical forces involved in the inactiva-tion of viral particles or virions. Virions consist of an RNA or DNA genome packaged into a proteic capsid, and some virions also include lipids or a proteic tail. A strong pressure is exerted on the inside of the capsid by strong repulsions between the highly charged neighboring nucleic acid segments, as well as by forces caused by the bending of the DNA [10,11]. Experimental [12] as well as theoretical [10] studies have established that the forces inside the capsid reach values on the order of 50 piconewtons, which corresponds to 50 times standard atmospheric pressure.
Several studies have proposed that the major lethal event in harsh conditions, i.e., under heat or osmotic shock, is the release of the nucleic acid from phage particles [13][14][15]. Indeed, deletion mutants proved to be more resistant, particularly to heat shock [13,14,16]. Conversely, virions of phages with genomes larger than the wild type are highly unstable [17]. It was concluded from these observations that genomic deletions decreased the internal force exerted by the packaged genome on the capsid, and therefore enhanced virion stability. These results were extended by studies showing that heat-resistant mutants are also more resistant to osmotic shock [14], extreme pH levels, high salt concentrations, and chemical treatments such as chlorination [18]. Direct observations of phage particles by electron microscopy have shown that the proportion of damaged phage particles increases proportionally with the loss of infectivity of phage populations [15], supporting the hypothesis that virion inactivation in stressful conditions is due to the rupture of the capsid.
The multiplication rate in the bacterial host, which is another essential aspect of the life cycle of these parasites, has been measured for almost every newly isolated virus, using classical techniques [19]. After infection of a host in an appropriate physiological state, new phage particles are synthesized, leading, in general, to the rapid lysis of the bacterial cell and the concomitant release of the newly formed virions. The multiplication rate of phages can be determined by two parameters: the burst size (the number of particles released in one cycle of infection) and the latency period (the time between infection and lysis of the host). Even among phages infecting E. coli cells in similar growth conditions, large differences can be measured in burst sizes and latency periods [20].
Here, we determined the life history traits of coliphages by measuring the decay rate and the multiplication rate of 16 well-characterized phages of E. coli-k, M13, MS2, Mu, P1, P2, P4, u80, uX174, PRD1, R17, T2, T4, T3, T5, and T7-under identical laboratory conditions. These phages were selected as being the most well-known representatives of each important family of phages [21], or the less well-studied close relatives of these phages: T2 (relative of T4), T3 (relative of T7), R17 (relative of MS2), and u80 (relative of k). With the exception of these pairs of related phages, the other studied phages cannot be placed within a phylogenetic relationship [21], ensuring that the effects reported cannot be due to phylogenetic constraints. Decay rates proved to be constant with time for all phages, indicating an absence of aging, but are very variable between phages. To understand this variability, we made an analysis of covariance between the decay rate and other traits of phages, such as structural properties of the virions and life cycle characteristics. This analysis revealed that the decay rate is positively correlated with the multiplication rate in the host, suggesting that phages have evolved a continuum of different reproductive strategies constrained by a trade-off. Moreover, two physical characteristics of virions, the density of the packaged genome and the thickness of the capsid, are correlated with mortality.

Characteristics of Phage Decay
The kinetics of mortality of a population not only gives information about how fast death occurs, but by describing the distribution of the age at death throughout the population, it gives information about the processes that lead to death. We determined the precise kinetics of inactivation of the 16 phages previously listed, from a population of approximately 10 8 viable phages down to 10 2 , by measuring the number of phage particles that are able to complete an infectious cycle when plated on the appropriate host, at different time points and at different incubating temperatures. Phage stability is very dependent on salt concentration and osmotic pressure [19]; so we used the standard high-salt LB broth instead of any defined medium that could favor one phage over another. For every phage, and at each temperature, the number of viable particles decreases exponentially with time ( Figure 1A). This indicates that these phages do not age; i.e., their probability of dying does not increase with time, which is probably linked to their absence of metabolism, repair, and redundancy. Indeed, the exponential decay of these phages is representative of a single-step process [22]. If two or more successive events were necessary to cause inactivation, we would observe an increase in the probability of dying since the number of viable particles that can be affected by the lethal event would increase. Although some changes in capsid characteristics may occur over a period of time prior to inactivation, these changes do not affect the ability of the phages to infect and multiply under the conditions we used.
To account for the possibility of phage interactions with free host cell receptors in the incubation media, we have shown with very different phages, T4, k, and uX174, that most of the inactivation is independent of the initial concentration of phage in the incubation medium (unpublished data). Moreover, we observed populations of phages T4, T3, and k by electron microscopy after incubation at 37 8C. We show that for the three phages, the proportion of broken virions increases after 8 d in the same proportion as the loss of ability to infect a susceptible host ( Figure 2). This result is in line with those published in similar conditions for phages k and u80 [15].

Relation of Mortality Rates with Temperature
In order to investigate the variation of the mortality rate with temperature, survival curves were determined at five different temperatures ranging from 30 8C to 55 8C. Between 30 8C and 45 8C, the mortality rate K follows the Arrhenius equation: where K is the mortality rate, T is the temperature, E a is the energy of activation, k the Boltzman constant, and A is a constant specific to each reaction ( Figure 1B). For each phage, this equation gives the energy of activation of the reaction causing inactivation, i.e., the energy the system has to overcome so that the reaction occurs (Table 1). Depending on the phage, the activation energy varies between 90 and 150 kJ/mol, which correspond to the values of reactions that result in the denaturation of globular proteins. In the case of T3, the mortality rates were determined at 55 8C, 60 8C, 62 8C, and 65 8C and proved to be much higher than expected, indicating that a different reaction is the predominant cause of inactivation. This result is in line with previously published data [19].

Analysis of Covariance between Parameters Characterizing Phages
Even if the mortality of phages follows the same law for all phages, there is a great variation in the decay rate from one phage to another. To unravel what can account for this variation, for each phage, we compiled a number of structural parameters from the literature. We also calculated two associated variables that are potentially related to the stability of the capsid. The first is the density of the packaged genome, calculated only for double-stranded DNA phages because their nucleic acid shares the same chemical and physical properties. The second is the surfacic mass of the capsid, which is representative of the thickness of the proteic shell of the phage head (Table 1). Moreover, we measured burst size, latency period, and adsorption rate for all phages, in conditions as similar as possible.
A statistical analysis of covariance revealed the existence of two groups of parameters, which can be visualized in a threedimensional representation along axes constructed by principal components analysis, or PCA ( Figure 3). In PCA, axes are linear combinations of parameters, thus parameters that appear collinear on the figure are correlated. We can see that the size, mass, and geometry of capsids are highly correlated with genome size. This reflects the very good linear relationship between the internal volume of the capsid and the genome size over a 300-fold range (linear regression on 16 phages, R 2 ¼ 0.95, p , 10 À4 ). This relation reveals important constraints about genome packaging, as the size of the capsid is determined almost entirely by genome length. Such a relation is not observed among other viruses [23] and thus must be caused by the specificities of phage life cycles, such as the ejection of the genome into the bacterial host at the time of infection. Indeed, phages do not enter the host cell upon infection, but instead inject only their genome into the cytoplasm.
As described below, the multivariate analysis revealed that the decay rate correlates with the multiplication rate of virions, but also with the density of the packaged DNA and the surfacic mass of the capsid; the latter correlates also with the multiplication rate. Other parameters are not repre-

Relations between Physical Constraints and Life History Traits
For phages that have a double-stranded DNA genome, the mortality rate increases significantly with the density of the packaged DNA (q pack ) ( Figure 4A; R 2 ¼ 0.67 and p ¼ 0.001 for a linear regression on 12 phages). q pack estimates the confinement of the genome in the capsid, by calculating the fraction of the internal volume of the capsid occupied by the genome [23]: where N bp is the genome size in base pair and Volume capsid is the internal volume of the capsid in nm 3 . Due to the strong repulsion between charged DNA strands and the curvature of the nucleic acid chain, this confinement generates a high internal pressure. Internal pressure depends also on the presence of ions, the curvature stress of the DNA, interactions between DNA and the internal face of the capsid, the conformation state of the nucleic acid, and the spatial organization of the genome [23]. However, we can assume that for double-stranded DNA phages in similar environments, these parameters are roughly similar, and thus q pack , which varies from 0.4 to 0.7 for studied phages, should be a  good approximation of the internal forces. Indeed, published DNA densities proved to be very similar for phages T7, T4, k, P2, and P4, but smaller for PRD1 [24]. These measures correspond qualitatively to the ranking of values of q pack and confirm that this parameter is a good approximation of the genome density in the capsid. The positive correlation between the mortality rate and q pack suggests that a higher internal pressure is associated with a higher mortality.
Other physical parameters are involved in the stability of phage particles and, in particular, the mechanical strength of the capsid. To estimate this strength; the method used so far, and for only one phage, is the calculation of energy of the liaisons between capsomers derived from the analysis of the molecular structure [25]. As molecular structures are determined for only four of the phages used in this study, we approximated capsid strength by calculating the surfacic mass of the capsid shell, i.e., the molecular weight of the capsid shell divided by the surface of the capsid. In the cases in which these data were missing for a given phage, but a close relative displayed identical morphology by electron microscopy, we used the data of the related phage. Under the assumption that the density of liaisons between capsomers is somewhat constant between phages, this parameter should be related to capsid strength. Indeed, the mortality rate decreases with an increase in this parameter ( Figure 4B; R 2 ¼ 0.35 and p ¼ 0.031 for a linear regression on 13 phages).
Moreover, the surfacic mass of the capsid correlates negatively with the multiplication rate ( Figure 4C; R 2 ¼ 0.46 and p ¼ 0.011 for an exponential regression on 13 phages). A possible explanation is that more complex capsids may necessitate more time and resources for assembly than simpler ones. The density of the packaged DNA, however, does not correlate with the multiplication rate. This might be due to the variability in molecular processes of genome packaging between phages.

Negative Correlation between Survival and Multiplication Rate of Phages
Despite the above-described correlations, the parameter showing the highest correlation with the mortality rate is not a structural characteristic of the capsid, but rather a major parameter of the life cycle, specifically the multiplication rate in the bacterial host ( Figure 4D; R 2 ¼ 0.73 and p , 0.001 for a linear regression on 16 phages). The multiplication rate was defined as the number of phage particles produced per unit of time in an infected host, calculated by dividing the burst size by the latency period. We have also tried a definition of productivity that takes into account the exponential character of phage multiplication on hosts, and leads to the same conclusions (unpublished data). An advantage of the above definition is that it is not dependent on the density of host cells. Moreover, it better reflects the rate at which cellular resources are used during virion morphogenesis.
We estimated the consequence of this positive correlation between the mortality and multiplication rates on the success of transmission of phages by calculating the persistence time s, i.e., the number of days during which at least one phage particle is infectious, following one cycle of multiplication: where U is the number of viable phage as a function of time, K is the decay rate, and B the burst size. Because of the negative correlation between the decay rate K and the multiplication rate, and despite an inverse relation expected from the role of the burst size B in the equation above, phages with the largest burst sizes are, however, the least persistent (p ¼ 0.022, Spearman Rho test). The optimal compromise between survival and multiplication might thus depend on the probability of finding a new susceptible host.

Explanatory Power of the Correlations
The independence of the parameters that correlate with decay rate was estimated by a stepwise regression on the ten double-stranded phages for which complete structural data were available. All three parameters that have been shown to correlate individually with the decay rate-the multiplication rate, the density of the packaged DNA, and the surfacic mass of the capsid-are selected by this method and are the same in both directions of the stepwise procedure. The order of entrance of parameters is a decreasing function of their explanatory power. On these ten phages, the parameter that correlates the best with the mortality rate is the density of the packaged DNA. A model combining the three parameters accounts for 91% of the variability in the mortality rates ( Figure 5). In this multiple regression, the density of the genome and the multiplication rate contribute significantly to the coefficient of correlation, indicating that they have independent effects on inactivation. On the contrary, the remaining effect on the model of the surfacic mass is low, because this parameter correlates with the multiplication rate.
Moreover, the part of the positive association between the decay and the multiplication rates that is explained by the physical properties of the virions has been estimated by looking at the correlation between the residuals of the regression between physical parameters and decay rate, and the residuals of the regression between physical parameters and multiplication rate. There is still a positive correlation between residuals (p ¼ 0.013, linear regression on ten phages), which indicates that the relation between decay and multiplication is not entirely explained by the virion physical properties that have been used in this analysis.

Discussion
This study reveals a negative correlation between mortality and multiplication rates among phages infecting the bacteria E.
coli. This relation parallels the trade-off between survival and reproduction revealed by life history studies of numerous species. Interestingly, this relation is observed among organisms that have diverged so much that they cannot be phylogenetically related, indicating convergent evolution. Moreover, the fact that this trade-off is present in this very simple biological situation suggests that it might be a fundamental property of evolving entities produced under constraints.
In order to maximize the data available on phages, we have selected the most well-known, representative phages of each major family of coliphages. A potential drawback is that these phages were isolated decades ago and were long maintained in various laboratories. Over this period, they probably were under selection for specific properties that might have been different for each phage. To limit this potential evolution since they were isolated, we obtained temperate phages from lysogenic strains. For lytic phages, we verified that the burst sizes and latency periods measured were almost the same as first reported. This was the case except for MS2 (400 instead of 12,000) [26] and PRD1 (50 instead of a few hundred) [27]. However, these changes did not modify the conclusions concerning the relationship between mortality and multiplication rates (R 2 ¼ 0.56 and p ¼ 0.001 with published data and R 2 ¼ 0.73 and p , 0.001 with measured values).
Moreover, we have measured the mortality rate of nonpurified phage particles in very specific conditions: in the classical LB high-salt broth, under limited aeration and light. We can thus question whether phage life history traits in their natural environment would show the same correlation. One argument to think so is that our study give the same rank ordering among phages as found in studies of phage survival in the environment, throughout various inactivation processes [28][29][30].
The positive correlation between mortality and multiplication rates of phages reveals an underexplored trade-off in hostparasite interactions. In most epidemiological models, transmission and virulence are assumed to be positively coupled [31]. Here we propose that this assumption is not necessarily valid, because transmission depends not only on the multiplication rate of the parasite, but also on survival, which is negatively correlated to the multiplication rate for coliphages.
The variation in fundamental life history traits suggests that coliphages have evolved different reproductive strategies, which would be expected to be related to their respective ecological niches. Unfortunately, if E. coli and its phages are the most carefully investigated genetic systems, only a few reports [32,33] have yet investigated their ecology. We thus hope that this work will encourage further experiments with animal hosts of E. coli to investigate the effect of this negative correlation between multiplication and survival in vitro on the transmission of phages in more natural conditions. In particular, an interesting complement to this work would be to measure the capability of multiplication and the survival of these phages in the gut of model animals, as well as their capability of transmission between different animals.

A Mechanistic Origin for the Trade-Off
Currently, physiology fails to fully explain the relation between survival and reproduction observed in cellular organisms [34]; this is probably due to the complexity of the phenomena involved. In the case of viruses, insights into the mechanistic understanding of the constraints linking inactivation and multiplication can be proposed.
Firstly, the exponential decay of phage stocks indicates that a unique kinetically dominant event leads to the inactivation of viral particles. This supports the hypothesis that for all phages, the rupture of the capsid shell is the major event leading to inactivation, and not only under stressful conditions (exposure to high temperatures or osmotic shock, for example), but also at temperatures naturally encountered by phages during their life cycles.
Moreover, the density of the packaged DNA(q pack ) and the surfacic mass of the capsid account for 82% of the variation in the decay rate. The multiplication rate correlates with the surfacic mass of the capsid, but a part of the variability is still unaccounted for by the physical parameters considered in this study. This is probably because the multiplication rate depends on the processes involved in phage particles biosynthesis, and these processes are very different between phages [35,36]. In particular, the kinetics of the assembly of the capsid, as well as the time and energy necessary to package DNA in a conformation that minimizes internal pressure, might be involved in the potential relationship between the multiplication rate and the strength of the capsid. However, the fact that the capsid surfacic mass correlates both with the decay and the multiplication rates suggests that capsid characteristics are involved into the mechanistic cause of this survival/reproduction trade-off ( Figure 6). We can hypothesize that the energy and time necessary to produce and assemble the components of phage particles might be the limiting factors in the production of virions. We hope that this report will inspire further studies analyzing the molecular mechanisms and constraints leading to trade-offs between life history traits such as reproduction and survival.

Materials and Methods
Phage and bacterial strains. We obtained the following phage and bacterial strains: phage R17 from H. W. Ackermann  All the above experiments were carried out in LB broth (Miller). Phage growth. Phages were grown on E. coli MG1655 harboring the plasmid F, except for experiments with the following phages: T7 which is inhibited by the plamid F, had been grown on E coli MG1655; uX174 which uses E. coli C LPS for adsorption had been grown on E. coli C WT; PRD1 which uses plasmid of group P, N, or W as receptor was grown on E coli MG1655 RP4; and P4, which is a satellite of P2, was grown on E. coli MG1655 F lysogenic for P2. For the production of phage suspensions, 10 ml of mid-exponential phase E. coli cultures are infected with phage and incubated with shaking at 37 8C. When the lysis is visible, the culture is centrifuged and the supernatant is passed through a 0.22-lm pore filter to eliminate bacterial cells.
Phage inactivation. Inactivation, which is defined as the loss of ability to form a plaque of lysis on a lawn of susceptible hosts, was studied by incubating samples of a phage lysate diluted in LB in 1.5ml tubes at 30 8C, 37 8C, 42 8C, 45 8C, and 55 8C. Over periods of weeks, the number of viable phage was repeatedly assessed by measuring plaque forming units (PFU), each measurement coming from an individual sample. Mortality rate was calculated by fitting an exponential curve to the decay of the phages.
Phage adsorption. The phage adsorption constant was determined by mixing a mid-log phase E. coli (OD 600 between 0.2 and 0.3) susceptible culture and the appropriate number of phage to ensure a multiplicity of infection (m. o. i.) of 10 À4 . The mix tubes were incubated at 37 8C, and then every 2-5 min, a tube was centrifuged for 2 min at maximum bench centrifuge speed. The number of free phage in the supernatant was estimated by plaque assay. The phage adsorption constant was estimated by fitting an exponential curve to the evolution of the free-phage population with time, with the adsorption rate being defined as the value of the exponential fit divided by the number of colony-forming units at the time of mixing, as determined by plaque assay.
Burst size and latency period. Burst size was determined by incubating 10 ml of a mid-exponential phase E. coli culture on ice for 10 min, centrifuging, and then resuspending the bacterial culture in minimal medium (M9) and mixing with phage (multiplicity of infection ¼ 0.01) for 7 min at 37 8C. The mix was then diluted 100fold and 10,000-fold in LB at 37 8C. The number of free phage was evaluated by plaque assay after centrifugation. Samples of the dilutions mix were taken repeatedly throughout time and assayed immediately for plaque-forming units. The burst size is the factor between final phage number and initial phage number, subtracting the number of non-adsorbed phage at time of dilution. Latency period is the time at which 50% of the virions have been released. Figure 6. Potential Relations between Phage Survival, Multiplication Rate, and Capsid Characteristics Evolutionary theory predicts that a high decay rate is associated with an elevated multiplication rate. Forces exerted on the capsid might lead to the rupture of the head of phages, leading to their inactivation. The multiplication rate of phages is possibly associated with properties of phage particles as a consequence of kinetic and energetic considerations involved in the assembly of the capsid and/or the encapsidation of the genome. DOI: 10.1371/journal.pbio.0040193.g006 Electron microscopy. Aliquots (10 ll) of phage suspension were adsorbed onto 400-mesh carbon-coated copper grids, washed on a 10ll drop of phage buffer (pH 7.1), and negatively stained in 1.5% (wt/ vol) uranyl acetate. Samples were observed in a CM120 transmission electron microscope (Philips, Eindhoven, The Netherlands) equipped with a minimum electron dosage system. Low-dose micrographs were recorded on a 1k 3 1k CCD Multiscan Gatan camera (Gatan, Pleasanton, California, United States) at an accelerating electron voltage of 120 kV, and at nominal magnifications from 6,0003 to 17,0003. Average defocus values were À1,200 nm.
Phage parameters and statistical analysis. We made a systematic analysis of correlations between the decay rate and the parameters describing phages characteristics presented in Table 1. For ease in comparing the different phages, we defined the multiplication rate as the burst size divided by the latency period. This measure provides a growth rate that is not scaled to generation time, which varies between phages. Phage capsids are considered spherical for the calculation of the volume, expect for phages T4 and T2, elongated phages, which are considered to be a capped cylinder, and M13, a filamentous virus, which is considered to be an elongated cylinder. Depending on the available data, values of the molecular weight of capsids had been obtained by direct mass measurements of capsids, or by the addition of the molecular weight of every protein constituting the capsid shell.
The statistical analysis was made with JMP IN 4 (SAS Institute, Cary, North Carolina, United States). Regressions are estimated by least squares methods. They were systematically performed on all the phages for which the relevant data were available.


extfieldsofstudy:
Computer Science
Medicine

text:
Optimized mixed Markov models for motif identification

Background Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. Results We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. Conclusion Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods.

Supplementary Materials
Calculation of Pr(x|M L k ) and Pr(x|M C k ) In real applications, it is easier to calculate Pr(x|M L k ) or Pr(x|M C k ) in terms of probabilities of oligomers. In the following, we derived generalized formulas to calculate a motif probability given a Markov model. These generalized formulas also give us a clearer view of the dierences in the probabilities of a motif sequence given dierent Markov models.

Estimation of model parameters
For the k th order Markov model, the length of all possible oligomers ranges from 1 to k + 1. We can estimate the probabilities of all these oligomers, as model parameters, by tting the model with training data. Let Y = Y 1 , . . . , Y v be a random vector associated with the oligomers consisting of bases from some certain positions of a motif, then Y follows a multinomial distribution: where N is the total number of motif sequences, v is the number of all possible y i , y i is the number of oligomer i and v i=1 y i = N , and θ i is the probability of oligomer i occurred and v i=1 θ i = 1.
According to the Bayes' theorem, the probability of θ given Y can be expressed as Suppose that the prior distribution of θ is a Dirichlet distribution as given by equation (12), then we can estimate the posterior mean of θ by maximum likelihood (equations 13 and 14).
where α is the parameter of Dirichlet distribution. In the context of Markov motif models, α is equivalent to pseudo counts of oligomers. For all our main results, we set pseudo counts = max(4, 0.01N ), where N is the number of true motif sites in training dataset. The prior was chosen based on a rule of thumb without being optimized for performance.

Detection of protein domains
OMiMa is a versatile motif prediction tool for biological sequences, including DNA, RNA and protein sequences. In this evaluation, we tested OMiMa's capability for motif identication in protein sequences. The functional motifs in protein sequences are also known as function domains that are usually well-conserved across dierent members in the same protein family. The data for this evaluation are the alignment seeds of Pfam protein domains (ftp://ftp.sanger.ac.uk/ pub/databases/Pfam/Pfam-A.seed.gz). We used a domain named A Propetide (accession # PF07966), of which the length is 29 bases. In total, there are 86 true domain sites, and for each true domain site, 50 false domain sites are simulated by randomly shuing the amino acids of the true site. For the 0-1 mixture model, OMiMa optimized position arrangement for the rst order chain is given by 5-3-15-7-10-12-26-9-28-13-1-24-21-16-20-0-2- 14-11-6-25-17-27-8-22-18-23-4-19 Although no position is independent of all others for this domain motif (the length of zero order chain is 0), the best model chosen by OMiMa (AIC criterion) is the zero order Markov model. We compared the following three Markov models: zero order, un-optimized rst order, and DNJ optimized rst order models by 10-fold cross validation. The log-likelihood ratio was used to score a domain site, and the rst order Markov chains were set to linear. Results (not given here) show that both the zero order, and DNJ optimized Markov models have perfect prediction (both Sn and Sp are 1), while the un-optimized rst order Markov model does not.

Computational eciency
OMiMa is an ecient computational tool designed for nding motifs in large genome sequences. Using Reese's dataset of human donor sites, we compared the running times used by OMiMa, PVLMM, and NNSplice. Since PVLMM and OMiMa run on dierent computer operating systems, the test was performed on a SUSE Linux/Window XP dual-boot computer with Intel 3.0 GHZ CPU and 512 M main memory. NNSplice is a web-based program at http://www.fruitfly.org/seq_ tools/splice.html, so we calculated its computational time as the interval from submitting data to returning the result from the NNSplice web-server using Microsoft IE browser on the same dualboot computer. Results (Table 1) showed that OMiMa is much more ecient than either NNSplice or PVLMM.