s2fieldsofstudy: Biology Chemistry

text:
Anticolitic Effect of the Rhizome Mixture of Anemarrhena asphodeloides and Coptidis chinensis (AC-mix) in Mice

During a screening program to search the anticolitic herbal medicines, 80% ethanol extract of the rhizome of Anemarrhena asphodeloides (AA) was found to potently inhibit the expression of proinflammatory cytokines TNF-α and IL-1β, as well as the activation of NF-κB in LPS-stimulated colonic macrophages, followed by that of the rhizome of C. chinensis (CC). AA also potently inhibited TNBS-induced colitic markers, shortening of the colon and increase of macroscopic score, myeloperoxidase activity, TNF-α, IL-1β, and IL-6, in mice. The synergistic effect of CC against the anticolitic effect of AA was investigated. CC synergistically inhibited the anticolitic effect of AA. AC-mix (AA+CC, 1:1) potently inhibited them. AC-mix also inhibited the activation of NF-κB, as well as the expression of TNF-α, IL-1β, IL-6, iNOS and COX-2. The effects of AC-mix against oxazolone-induced colitis were investigated in mice. AC-mix also potently inhibited oxazolone-induced inflammatory markers, colon shortening, macroscopic score, myeloperoxidase activity, NF-κB activation and proinflammatory cytokines. Overall, the anti-colitic effect of AC-mix was superior to that of mesalazine. Based on these findings, AC-mix may improve colitis by inhibiting NF-κB activation.

INTRODUCTION
Infl ammatory bowel disease (IBS) refers to two chronic relapsing diseases, ulcerative colitis and Crohn's disease, that cause infl ammation of the intestines (Shanahan, 2002;Binder, 2004). Their pathogenic mechanism is assumed to be an immune response against gut constituents, such as intestinal bacterial toxins, exposed in intestinal environment (Rafi i et al., 1999;Atreya et al., 2000). Among these toxins, bacterial endotoxins, such as lipopolysaccharide (LPS) and fl agellin, that can cause colonic infl ammation stimulate the mucosal immune system (Radema et al., 1991;Rafi i et al., 1999). This induces infl ammatory mediators such as pro-infl ammatory cytokines, activating the infl ammatory reaction via distinct signaling pathways through Toll-like receptors (TLRs) and/ or cytokine receptors (Jung et al., 1995;Cario and Podolsky, 2000). Therefore, to cure infl ammatory diseases, natural products regulating infl ammatory mediators have been attempted (Paradkar et al., 2004;. The rhizome of Anemarrhena asphodeloides (family Lili-aceae) is an antipyretic, antiphlogistic, sedative, and diuretic agent in traditional Chinese medicine. Its main constituents are saponins. Of these saponins, timosaponins improve learning and memory in rats (Hu et al., 2005;Li et al., 2007) and inhibit platelet aggregation factor-induced platelet aggregation (Dong and Han, 1991). Xanthones, such as mangiferin, inhibit lung infl ammation and pruritus in mice Rivera et al., 2011), inhibited LPS-induced iNOS and COX-2 expression in RAW264.7 cells (Shin et al., 2008) and protect scopolamine-induced brain impairment in mice . However, the anti-colitic effect of AA remains to be thoroughly studied. The rhizome of Coptidis japonica (family Ranunculaceae), whose main constituent is an isoquinolone alkaloid berberine, has been used to treat patients who have gastroenteritis and diarrhea in tradition Chinese medicine (Bae et al., 1998;Zhou and Mineshita, 2000). Although its closely related plants are C. chinensis, C. teeta, and C. sinensis, their constituents are different to that of C. japonica. Nevertheless, they contain berberine. Berberine was proved to have biological activities

Isolation and culture of intestinal macrophages
For the isolation of macrophages in intestine, the Payer's patches were isolated from the mouse intestines and were digested in DMEM containing 1 mg/ml dispase, 0.25 mg/ml collagenase A, and 25 U/ml DNAse (Roche Diagnostic, Indianapolis, IN, USA) at 37 o C for 20 min with shaking and passed through a 70 μm cell strainer (BD Biosciences, San Diego, CA, USA). The cells were harvested by centrifugation and washed with PBS containing 4% FBS. The cells were resuspended in cold IMag buffer (0.05% bovine serum albumin and 2 mM EDTA in PBS) and viable cells were counted with trypan blue. The colonic macrophages were isolated from colonic cells by using a biotin-labeled anti-mouse F4/80 antibody and streptoviridin magnetic beads (Invitrogen).
To examine the anti-infl ammatory effect of test agents, intestinal macrophages were incubated in the absence or presence of test agents with 50 ng/ml LPS. The cytoxicities of these agents in the cell viability were measured using the crystal violet method.

Preparation of TNBS-induced colitic mice
TNBS-induced colitis was induced by the administration of 2.5% (w/v) TNBS solution (100 μl, in 50% ethanol) into the colon of lightly anesthetized mice via a thin round-tip needle equipped with a 1-ml syringe . The normal group was treated with vehicle alone. The needle was inserted so that the tip was 3.5-4 cm proximal to the anal verge. To distribute the agent within the entire colon and cecum, mice were held in a vertical position for 30 s after the injection. Using this procedure, >95% of the mice retained the TNBS enema. If the mice quickly excreted the TNBS-ethanol solution, it was excluded from the remainder of the study. Test agents (dissolved in 2% tween 80 solution, 10 or 20 mg/kg) or vehicle alone was orally administered once a day for 3 days after TNBS treatment. The mice were sacrifi ced the next day after the fi nal administration of test agents. The colon was quickly removed, opened longitudinally, and gently cleared of stool by PBS. Macroscopic assessment of the disease grade was scored according to a previously reported scoring system (0, no ulceration and no infl ammation; 1, ulceration without hyperemia; 2, no ulceration and local hyperemia; 3, ulceration and infl ammation at one site only; 4, two or more sites of ulceration and infl ammation; 5, ulceration extending more than 2 cm), and the colon tissue was then used for ELISA and immunoblotting .

Preparation of oxazolone-induced colitis in mice
Oxazolone-induced colitis was studied in mice as previously described (Wirtz et al., 2007). To presensitize the mice, a 1.5 cm×1.5 cm fi eld was shaved on the back between the shoulders, and 200 μl of a 3% (w/v) solution of oxazolone in acetone/olive oil (4:1) was applied. Eight days after presensitization, mice were rechallenged intrarectally under ethyl ether general anesthesia with 100 μl of 1% oxazolone in 50% ethanol to induce colitis or 50% ethanol alone (vehicle control). Test agents (AA, 10 and 20 mg/kg; AC, 10 and 20 mg/ kg; mesalazine, 10 mg/kg dissolved in 2% tween 80) were orally administered once a day for 15 days after oxazolone administration. The mice were sacrifi ced on next day after the fi nal administration of test agents. The colon was quickly removed, opened longitudinally, and gently cleared of stool by PBS. Macroscopic assessment of the disease grade was http://dx.doi.org/10.4062/biomolther.2013.048 scored according to a previously reported scoring system and the colon tissue was then used for ELISA and immunoblotting .

Histological exam
The colons were fi xed in 10%-buffered formalin solution, embedded in paraffi n using standard methods, cut into 8-μm sections, stained with hematoxylin-eosin, and then assessed under light microscopy .

Colon tissue preparation
The colon tissues were excised, perfused with ice-cold perfusion solution (0.15 M KCl, 2 mM EDTA, pH 7.4), and homogenized in 50 mM Tris-HCl buffer (pH 7.4) . The homogenates were centrifuged at 10,000×g at 4 o C for 30 min and the supernatants used for further studies.

Assay of myeloperoxidase activity
An aliquot (50 μl) of the colon supernatant was added to a reaction mixture of 1.6 mM tetramethyl benzidine and 0.1 mM H 2 O 2 and incubated at 37 o C; the absorbance was obtained at 650 nm over time . Myeloperoxidase activity was defi ned as the quantity of enzyme degrading 1 μmol/ml of peroxide at 37 o C and expressed in unit/mg protein. The protein content was assayed by the method of Bradford (1976).

www.biomolther.org
For the immunoblot analyses of p-IRAK1, p-IKKβ, p-p65 and β-actin, the colon tissue homogenates or the colleted cells were resuspended in 1 ml of RIPA lysis buffer containing 1% protease inhibitor cocktail and 1% phosphatase inhibitor cocktail . After centrifugation, the supernatant was used for the immunoblot assay. The proteins from collected cells were subjected to electrophoresis on 8-10% sodium dodecyl sulfate-polyacrylamide gel and then transferred to nitrocellulose membrane. Levels of p-IRAK1, p-IKKβ, p-p65 and β-actin were assayed as previously described. Immunodetection was performed using an enhanced chemiluminescence detection kit.

Statistical analysis
All data are expressed as the mean ± standard deviation (S.D.), with statistical signifi cance analyzed using one-way ANOVA followed by a Student-Newman-Keuls test.

RESULTS
During a screening program to search the anticolitic herbal medicines, 80% EtOH extract of AA was found to potently inhibit the expression of proinfl ammatory cytokines TNF-α and IL-1β in LPS-stimulated intestinal macrophages, followed by CC (Fig. 1). AA also inhibited the phosphorylation, of IRAK1,  IKKβ and IκBα and the activation of NF-κB.
The ability of AA to inhibit TNBS-induced colitis was tested in mice (Fig. 2). Intrarectal injection of TNBS signifi cantly caused colitis, manifested by shortening of the colon and increase of myeloperoxidase activity. AA also potently inhibited TNBS-induced shortening of colons and increase of myeloperoxidase activity. AA also inhibited TNBS-induced NF-κB activation, as well as expression of proinfl ammatory cytokines, TNF-α, IL-1β, and IL-6 (Fig. 3).
Next, we tested the synergistic effect of CC in the anticolitic effect of AA in mice (Fig. 4). Intrarectal injection of TNBS potently caused severe infl ammation, manifested by colon shortening and myeloperoxidase increasing. Treatment with AA or CC in TNBS-induced colitic mice signifi cantly inhibited the shortening of colons and the increase of myeloperoxidase activity. Treatment with AC-mix prepared by the addition of CC in AA inhibited the shortening of colons and the increase of myeloperoxidase activity more potently than those of AA or CC.
Next, we tested the anticolitic effect of AC in mice (Fig. 5). Intrarectal injection of TNBS caused severe infl ammation, manifested by shortened, thickened, and erythematous colons. TNBS also increased myeloperoxidase activity. Treatment with the combined mixture AC-mix in TNBS-induced colitic mice inhibited the shortening of colons and the increase of myeloperoxidase activity. AC-mix also inhibited NF-κB activation (Fig. 6). AC-mix at a dose of 20 mg/kg inhibited TNBSinduced TNF-α by 74.1%, IL-1β by 81.6%, and IL-6 expression by 82.8%. AC-mix also inhibited iNOS and COX-2 expression. However, it reversed IL-10 to 71.9% of the normal control group. The anti-colitic effect of AC-mix at a dose of 20 mg/kg was more potent than at a dose of 10 mg/kg. However, in our preliminary study treated with 10 or 50 mg/kg mesalazine to TNBS-induced colitic mice, we could not fi nd the signifi cant differences of anti-colitic effects, such as colon shortening and macroscopic score, between them (data not shown). Thus, the anti-colitic effect of AC-mix was superior to that of mesalazine.
Next, we investigated the effect of AC-mix against oxazolone-induced colitis in mice (Fig. 7). Treatment with oxazolone alone caused severe colonic infl ammation, including the  www.biomolther.org shortening of colon, loosing of bodyweight, and an increase of macroscopic score, myeloperoxidase activity, proinfl ammatory cytokine expression, and NF-κB activation. AC-mix potently ameliorated oxazolone-induced colon shortening and bodyweight loosing, as well as increased myeloperoxidase activity, proinfl ammatory cytokine expression and NF-κB activation. AC-mix also inhibited iNOS and COX-2 expression (Fig. 8). AC-mix at a dose of 20 mg/kg inhibited TNBS-induced TNF-α by 54.6%, IL-1β by 67.3%, and IL-6 expression by 80.0%. However, AC-mix reversed IL-10 to 89.7% of the normal control group. AC-mix also inhibited TNBS-induced IRAK1 and IKKβ phosphorylation, as well as NF-κB activation. Overall, the anti-colitic effect of AC-mix was superior to that of mesalazine.
In the present study, AA potently ameliorated TNBS-induced colitis in mice. Furthermore, AA inhibited infl ammation in LPSstimulated colonic macrophages by regulating the phosphorylation of IRAK1 and the activation of NF-κB. AC-mix, which was prepared by the addition of CC in AA, ameliorated TNBSor oxazolone-induced colitis via inhibiting the activation of NF-κB in the colons of mice. Thus, the anticolitic effect of AA was synergistically increased by the addition of CC. Oeverall, the anticolitic effect of AC-mix was superior to that of mesalazine, which is used commercially for colitis.
Based on these fi ndings, AC-mix may potently ameliorate colitis by synergistically inhibiting NF-κB signaling pathway.


extfieldsofstudy: Mathematics Medicine

text:
Effects of Isometric Scaling on Vertical Jumping Performance

Jump height, defined as vertical displacement in the airborne phase, depends on vertical takeoff velocity. For centuries, researchers have speculated on how jump height is affected by body size and many have adhered to what has come to be known as Borelli’s law, which states that jump height does not depend on body size per se. The underlying assumption is that the amount of work produced per kg body mass during the push-off is independent of size. However, if a big body is isometrically downscaled to a small body, the latter requires higher joint angular velocities to achieve a given takeoff velocity and work production will be more impaired by the force-velocity relationship of muscle. In the present study, the effects of pure isometric scaling on vertical jumping performance were investigated using a biologically realistic model of the human musculoskeletal system. The input of the model, muscle stimulation over time, was optimized using jump height as criterion. It was found that when the human model was miniaturized to the size of a mouse lemur, with a mass of about one-thousandth that of a human, jump height dropped from 40 cm to only 6 cm, mainly because of the force-velocity relationship. In reality, mouse lemurs achieve jump heights of about 33 cm. By implication, the unfavourable effects of the small body size of mouse lemurs on jumping performance must be counteracted by favourable effects of morphological and physiological adaptations. The same holds true for other small jumping animals. The simulations for the first time expose and explain the sheer magnitude of the isolated effects of isometric downscaling on jumping performance, to be counteracted by morphological and physiological adaptations.

Introduction
Jumping is important for survival of many animals because it helps them to catch preys or escape from predators [1]. Jump height (h), defined as vertical displacement of the centre of mass (CM) in the airborne phase, has been found to vary substantially among differently sized primate species. For example, h is about 0.33 m in a 100 g grey mouse lemur (Microcebus murinus) [2], up to about 2 m in a 300 g bushbaby (Galago senegalensis) [3], up to 0.7 m in a 34 kg bonobo (Pan paniscus) [4] and typically about 0.4 m in a 75 kg human [5]. From the perspective of functional morphology, it is interesting to compare jumping performance among species. Galago senegalensis seems to be hors catégorie, because it outperforms other mammals in both absolute and relative terms, but how does Microcebus murinus perform compared to humans? For centuries, the consensus in the literature has been that comparisons should be made in terms of absolute jump height, so Microcebus is not a good jumper compared to humans. However, in relation to body size Microcebus does a much more impressive job than humans. Is absolute jump height a fair measure to compare jumping performance of differently sized primate species?
In the literature, various propositions can be found on how body size affects jump height. The first proposition is that body size does not affect jump height at all. Perhaps most current scientists will not adhere to this proposition, but it has played a dominant role in history and hence is a good starting point here. The proposition has come to be known as Borelli's law, because Borelli, in his book De Motu Animalium published in 1680 [6], was the first to suggest that takeoff speed should be the same regardless of animal size [7]. A few years later, in 1687, Newton formulated the laws of classical mechanics in Philosophiae Naturalis Principia Mathematica [8], and many authors have used these laws to reason why isometrically scaled animals should have the same jump height (e.g., [9][10][11], for specific formulations see [7]). The reasoning is as follows. To jump to a given h, an animal must achieve a certain vertical takeoff velocity of CM (v to ), which corresponds to kinetic energy (E kin,to ) equal to 1 2 m 2 to , where m is body mass. Neglecting air resistance, E kin,to is transformed during the airborne phase into potential energy m, where g is the acceleration due to gravity, so: For the remainder of this paper, it is helpful to write this as: whereÊ E kin,to is E kin,to expressed per kg body mass. If we assume that there is no difference among animals in the amount of work produced per kg of muscle, and no difference in the amount of muscle mass relative to body mass, then each animal should produce the same amount of work per kg body mass (Ŵ W ), leading to the sameÊ E kin,to and hence to the same h (equation 2). According to this proposition, it is fair to compare jumping performance of differently sized primate species in terms of absolute jump height. Recently, Scholz et al. [7] showed that if differently sized animals produced the sameŴ W , small animals should actually be expected to jump higher than big animals. During the push-off, CM gains potential energy equal to m Á Á Á, where s is the vertical displacement of CM from initial height to takeoff height. If big animal B is isometrically downscaled to small animal S by a factor L, s will scale by L too. Hence, if animal S and animal B produce the sameŴ W , animal S will need a smaller fraction ofŴ W to raise CM to the takeoff height, has a greater fraction ofŴ W available for E E kin,to , and hence achieves greater h (equation 2). Accordingly, under the assumption that all animals produce the sameŴ W , comparing jumping performance in terms of h is unfair to big animals. This proposal will be referred to as the revised version of Borelli's law. Note that the definition of jump height is crucial here. If jump height were defined as the vertical displacement of CM relative to the lowest height of CM during the jump, which is proportional to the total change in effective energy per kg body mass (Ê E eff , the sum of potential energy and kinetic energy due to the vertical velocity of CM), then Borelli's law would still hold. In that case, however, simply standing up from a crouched position would qualify as a jump, which seems unacceptable.
Although the argumentation presented above is mechanically straightforward, there is reason to question that small animals should be able to jump higher than big animals. Motion of CM is the result of rotations of body segments. If the motion pattern were invariant (IMP), i.e. if differently sized animals produced the same segment rotational kinematics over time, v to would simply be proportional to L, and h to L 2 (equation 1). It is known that when small animals jump, they produce higher accelerations than large animals [12], and they could in principle achieve higher v to L and h L 2 than large animals. However, these higher accelerations in themselves require explanation, and they may not be sufficient to cause small animals to jump higher than big animals in absolute terms.
The revised version of Borelli's law holds under the assumption that all animals produce the sameŴ W during the push-off. Bennet-Clark [12] reasoned that under a different assumption, namely that all animals produce the same peak power per kg muscle mass, animal S will achieve smaller h than animal B. Bennet-Clark's argument was as follows. Producing the sameŴ W during the pushoff requires animal S to produce a higher peak power per kg body mass during the jump than animal B. After all, animal S has shorter limbs and smaller s for acceleration of CM (a) than animal B. Assuming that a is constant during the push-off, v to~a , where T is push-off duration, and s~a 2 2 , from which it that can be derived that a~v 2 to 2s . If a is constant, peak power occurs at takeoff and equals P peak~m : v to~m 3 to 2s (note that P peak as defined by Bennet-Clark is actually the peak rate of change of kinetic energy due to vertical velocity of CM). Combining this with equation 1 yields h~1 2g whereP P peak is P peak per kg body mass. According to Bennet-Clark [12] it is reasonable to assume that P peak is proportional to muscle mass, which in isometric scaling is proportional to m. Since s is proportional to L, h is proportional to L 2=3 under this assumption. Bennet-Clark's reasoning is another way of saying that ifP P peak is the limiting factor,Ê E kin,to is proportional to L 2=3 , so that animal S should be expected to jump less high than animal B. Therefore, a comparison in terms of h is unfair to animal S. Comparing jumping performance in terms of h may also be unfair to small animals for reasons related to muscle physiology, and this is the topic of the present paper. Firstly, to achieve a given v to , animal S needs higher segment angular velocities than animal B. After all, the velocity of CM is determined by the angular velocities of body segments, and if the segments are shorter the angular velocities need to be higher for the same absolute velocity of CM. This will require animal S to traverse the range of joint motion at higher angular velocities and, because muscle moment arms and muscle fibre lengths scale by L, contractile elements (CE) will shorten at higher relative velocities (ṽ v CE , i.e. CE velocity expressed in optimum CE-lengths per second). At higherṽ v CE , CE will produce less relative force (F F CE , i.e. CE force as fraction of maximum isometric force) and lessŴ W because muscle force drops monotonically with shortening velocity according to the forcevelocity relationship [13]. Secondly, it takes time to develop active state. Active state, which has been defined as the amount of Ca ++ bound to troponin [14], affects the number of cross-bridges attached and henceF F CE . If animal S traverses the range of motion at higher angular velocities and hence in less time than animal B, a relatively larger part of the range of CE-shortening will be travelled at submaximal active state and submaximalF F CE in animal S, and this will also detract fromŴ W produced during the push-off [15,16].
The propositions on how body size affects jump height presented above are all based on simplifying assumptions, for example thatŴ W over the push-off is independent of size [10], or that P peak during the jump is proportional to body mass and hence independent of size [12]. Moreover, although it will be clear from the reasoning presented above that both the force-velocity relationship and the rise time of active state present a disadvantage for small animals, the magnitude of the effects is difficult to predict. It would be helpful, therefore, to study the effects of isometric scaling using a biologically realistic musculoskeletal model. Alexander [17] studied jumps of humans, bushbabies and locusts with a realistic musculoskeletal model that included series elastic structures and muscle forces depending on length and velocity. However, he made three separate models, each with speciesspecific morphological and physiological parameters, and from his simulation results it is impossible to tease apart the possible effects of pure isometric scaling from the effects of inter-species differences in morphology and physiology.
The purpose of the present study was to quantify the effects of pure isometric scaling onŴ W , h and other mechanical and physiological variables relevant for jumping, using a biologically realistic model of the human musculoskeletal system.

Musculoskeletal Model
Vertical squat jumps were simulated using a musculoskeletal model capable of successfully reproducing human vertical jumps [18,19] (Fig. 1). It comprised four body segments, actuated by six major muscle tendon complexes (MTCs) of the human lower extremity. Each MTC was represented by a Hill type unit, comprising contractile element CE, series elastic element SEE and parallel elastic element PEE. Forces of SEE and PEE quadratically increased with SEE elongation only, while force of CE (F CE ) depended on length of CE (l CE ), velocity of CE (v CE ) and active state [20]. Active state, in turn, dynamically depended on muscle stimulation over time (STIM(t)).

Simulation of Jumps
Squat jumps were simulated from the initial posture shown in Fig. 2, which is considerably crouched for humans [19]. Initial STIM levels were set such that the model was in equilibrium. During the jump, STIM of each MTC was only allowed to increase linearly towards its maximum at a reference rate of 5/s [19], and this increase started at a STIM onset time. The combination of STIM onset times that maximised the height achieved by CM was found using a genetic algorithm [21] for each value of L. In order to quantify the effect of active state dynamics, we also optimized STIM onset times for a model in which STIM increased instantaneously to its maximum at STIM-onset time and the rate constant of excitation dynamics had been boosted by a factor of 100.
It might occur to the reader that humans and most animals tend to make countermovement jumps and not squat jumps. Making a countermovement has the advantage that active state and force can be built up during the downward motion of CM rather than during the push-off [15]. However, this advantage becomes negligible when active state increases rapidly [15], and it can safely be said that the outcome of the present study would have been the same if countermovement jumps had been simulated.

Scaling of the Model
The author was interested in animals ranging in size from humans to Microcebus, with mass being used as the variable for scaling. The human musculoskeletal model, with a mass of 82 kg, served as reference model (L = 1). Microcebus has a mass of only 90-100 g [2], which is about one-thousandth of the reference mass. Therefore, L was chosen to run from 1 to 0.1 (i.e. 0.001 1/3 ), in 30 steps. All body segment lengths, distances of segmental mass centres to segment ends, and muscle moment arms, were scaled by L, all masses by L 3 , and all moments of inertia for rotation about the segmental mass centre, with kg : m 2 as unit, by L 5 . Lengths of CE, SEE and PEE were scaled by L and their forces, which depend on physiological cross-sectional areas, by L 2 . Note that for all scales, the maximum shortening velocity was 12.7 optimum CE lengths per second. Because muscle fibre length scaled by L, the maximum shortening velocity of muscle fibres in absolute terms, i.e. in m/s, also scaled by L. The specific tension of the muscles in the model was taken to be 0.25 MPa and the theoretical maximal power output 367 W per kg of muscle tissue [22], which gave the model a theoretical maximal CE power output of 60 W per kg of body mass independent of scale. Figure 2 presents for L = 1 and L = 0.l models stick diagrams including ground reaction force vectors, and values for v to and h, and Fig. 3 shows time histories of relevant variables for L = 1 and L = 0.1. The first observation is that the duration of the push-off in the L = 0.l model is only about 25% of that in the L = 1 model. The second observation is that the acceleration of CM increases with miniaturization, but that takeoff velocity and hence jump height nevertheless decrease. The third observation is that, although the theoretical maximum power output of the muscles per kg body mass was independent of scale, the mean and peak values ofP P eff (the rate of change ofÊ E eff ) drop at small values of L. Fig. 4 (A-C) shows how kinematic variables changed over the investigated range of L. Dash-dotted lines in Fig. 4A-C represent outcomes as they would be if the Motion Pattern were Invariant (IMP), i.e. if a model with L,1 had the same segment angles, angular velocities and angular accelerations over time as the L = 1 model. Under IMP, a and v to would be proportional to L, and h to L 2 (equation 1). In isometrically downscaled models, however, peak a, v to and h exceeded the values corresponding to IMP. Thus, in relation to body size, i.e. in terms of v to L and h L 2 , isometrically downscaled models performed better than the reference model. However, with downscaling the duration of the push-off phase became less, and in absolute terms lower v to and h were reached. The model jumped 40 cm when human-sized, only 10 cm when miniaturized to the size of a 300 g bushbaby, and only 6 cm when miniaturized to the size of Microcebus. Thus, with isometric scaling, small animals jump less high than big animals, in contrast to Borelli's law and its revised version.

Results
Scaling h by L 2=3 as proposed by Bennet-Clark ( [12], dashed curve in Fig. 4C) overestimated h of the L = 0.1 model by only 2.7 cm, but overall the relationship between h and L could not be fitted well with L 2=3 , nor with any other power of L. Bennet-Clark's estimation builds on a constant peak power per kg body mass (equation 3), but the actual peakP P eff reached during the jump decreased from 49 W/kg at L = 1 to about 25 W/kg at L = 0.1, even though the theoretical maximum power output of the muscles per kg body mass was kept constant across scales. The reader might point out that P eff is not equal to the power output of contractile elements (P CE ) summed over all MTCs. On the one Effects of Isometric Scaling on Vertical Jumping PLOS ONE | www.plosone.org hand, P CE of an MTC may differ from the power output of the MTC as a whole (P MTC ) because of the presence of series elastic elements; for example, when muscle force drops during the final part of the push-off these elements recoil, causing MTC shortening velocities to be higher than muscle fibre shortening velocities, and hence causing P MTC to be higher than P CE [23,24]; this is known as 'catapult action'. On the other hand the total P MTC of the model will differ from P eff because of power flow to non-effective terms, such as segment rotational power [25]. Despite these caveats, however, the peak of P CE summed over all MTCs, expressed per kg body mass, showed the same behaviour as peak P P eff , dropping from 48 W/kg at L = 1 to 24 W/kg at L = 0.1 (results not shown). As mentioned above, the 'catapult action' causes peak P MTC to be higher than peak P CE [23,24]. This action is very important for performance in jumping [25], but it disqualifies peak power output as measure for the performance of muscle tissue. In the literature, comparisons among different animals are therefore also made in terms of mean power output over the push-off phase. During the push-off phase, the model produced a meanP P eff of 19 W/kg when human-sized, 13 W/kg when miniaturized to the size of a 300 g bushbaby, and 9 W/kg when miniaturized to the size of Microcebus. For mean P CE per kg body mass, summed over all MTCs, these values were 21, 14 and 10 W/kg, respectively.
According to equation 2 the drop in h with reduction of L (Fig. 4C) corresponds to a drop inÊ E kin,to . Figure 4F shows that the latter was due to a drop in work per kg body mass of muscletendon complexes (Ŵ W MTC ). In the simulated squat jumps,Ŵ W MTC produced during the push-off phase depended for more than 99% on contractile element work per kg body mass (Ŵ W CE ), whereŴ W CE of a given MTC is proportional to the integral ofF F CE to normalised CE-length (l l CE ).F F CE at givenl l CE depends onṽ v CE and active state. To analyze differences inŴ W CE of a given MTC it is therefore helpful to plotF F CE ,ṽ v CE and active state as function of l l CE [25]. This has been done in Fig. 5 for glutei and vasti, which at L = 1 contributed 35% and 30% to totalŴ W MTC , respectively. At L = 0.1,Ŵ W CE of glutei was reduced compared to L = 1 for two main reasons, as can be explained with the help of the left panels of Fig. 5A-C (note thatŴ W CE is proportional to the surface under the curve in Fig. 5C). First, in L = 0.1,F F CE was reduced because of the force-velocity relationship:ṽ v CE was higher at eachl l CE (Fig. 5B). Second, in L = 0.1,F F CE was reduced because active state was lower at eachl l CE (Fig. 5A); in the model the increase in active state was fixed over time, but the range ofl l CE was traversed in less time. Note that the lower active state was also part of the reason why power output of CE reached a smaller peak value at L = 0.1 (Fig. 5D). It can also be seen in Fig. 5 that at L = 0.1, CE shortened over a smaller range, because at takeoff joints were less extended than in the reference model (Fig. 2). However, takeoff occurs because the muscle forces become insufficient [26], so the reduced range of motion in L = 0.1 is a consequence of the higherṽ v CE and lower active state in L = 0.1. The explanation for the reduced W W MTC of vasti at L = 0.1 is essentially the same as that for the reducedŴ W MTC of glutei, but the effect of the force-velocity   . Kinematic, energetic and work variables as function of isometric scale factor L. a peak is peak vertical acceleration of centre of mass (CM), v to vertical velocity of CM at takeoff, and h (jump height) is vertical displacement of centre of mass (CM) in the airborne phase.Ê E kin,to is kinetic energy due to v to , DÊ E pot,to increase in potential energy during push-off, DÊ E eff increase in effective energy during pushoff (sum of DÊ E pot,to andÊ E kin,to ),P P eff ,peak peak rate of change of DÊ E eff during the push-off, andŴ W MTC work of muscle-tendon complexes, all expressed per kg body mass as indicated by caret over variables. IMP: relationship was even more devastating (Fig. 5, right panels);ṽ v CE increased almost immediately after the start of shortening to values at which only smallF F CE was produced.
What is the relative importance of these two complications of isometric scaling? When muscle active state was allowed to increase almost instantaneously to its maximum and STIM(t) was re-optimised, h increased by only 0.2 cm at L = 1 and by only 2.5 cm at L = 0.1, suggesting that muscle dynamics constitute a much bigger complication than activation dynamics.

Discussion
The purpose of the present study was to quantify the effects of pure isometric scaling onŴ W , h and other mechanical and physiological variables relevant for jumping, using a biologically realistic model of the human musculoskeletal system. In the simulated jumps presented in this paper, angular and linear accelerations increased when a human model was isometrically downscaled. The increased angular accelerations led to higher angular velocities and, in relation to body size, downscaled models performed better than the reference model. However, in absolute terms v to and hence h dropped becauseŴ W became less, rather than remaining constant as had been assumed by proponents of Borelli's law (e.g., [9][10][11]). Bennet-Clark [12] had already predicted that h would decrease with size under the assumption that P peak was proportional to body mass. However, the relationship between h and body size borne out by the present simulations was different from the one that Bennett-Clark had Invariant Motion Pattern, i.e. values as they would be if segment angles over time were the same as in reference model (L = 1). BC: dependence of h on L predicted by Bennet-Clark [12] (equation 3). doi:10.1371/journal.pone.0071209.g004 Figure 5. Explanation for reduced work output per kg of glutei (left panels) and vasti (right panels) with isometric downscaling. Force, velocity and active state of contractile elements (CE) of glutei and vasti have been plotted as function of normalised CE-length (l l CE ) for models with isometric scale factor L equal to 1 (reference model) and 0.1.ṽ v CE is CE shortening velocity expressed in optimum CE-lengths (l CE,opt ) per second, F F CE is CE force as fraction of maximum isometric force, andP P CE is CE power output as fraction of its maximum according to the force-velocity relationship. Arrows indicate the direction of time. When L = 0.1, CE work per kg (proportional to surface under curves in A and D) is less becauseṽ v CE is higher and active state is lower than when L = 1. doi:10.1371/journal.pone.0071209.g005 proposed [12]. Although in the model the theoretical maximum power output of the muscles per kg body mass was kept constant across scales, the mean and peak values ofP P eff (the rate of change ofÊ E eff ) dropped substantially at small values of L (Fig. 3, Fig. 4D). Below, I will first explain these findings and then elaborate on their relevance for the study of functional morphology and evolution of jumping animals.
We have seen that with reduction of L, segment angular accelerations and linear accelerations increased (Figs. 3-4). The increased angular accelerations led to higher angular velocities and, in relation to body size, i.e. in terms of v to L and h L 2 , the downscaled model performed better than the reference model; in other words, it performed better than under IMP, (Fig. 4A-C, dash-dotted lines). Why did segment angular accelerations increase with miniaturization? Translation of CM occurs because muscle moments rotate body segments against gravitational moments. Maximal muscle moments at the joints are proportional to L 3 because they are the product of muscle force proportional to L 2 and moment arms proportional to L. They act against moments due to gravity proportional to L 4 (the product of mass proportional to L 3 and moment arms relative to joints proportional to L) and cause angular accelerations of segments with moments of inertia proportional to L 5 . This explains why angular accelerations, and the ensuing angular velocities, increase as size decreases. Clearly, as was already pointed out elsewhere in general terms [27,28], an isometrically downscaled animal is relatively strong and moves relatively fast. However, as revealed by the simulations in this study, these positive effects are counteracted by negative effects: compared to the muscles of the reference model, the muscles of an isometrically downscaled model have less time to build up active state and, more importantly, are hampered more in their force production by the force-velocity relationship, leading to a decrease inŴ W MTC , inŴ W , inÊ E kin,to and hence in h in absolute terms. In conclusion, a small animal that is an isometrically downscaled version of a big animal achieves lower h, and this is largely due to muscle dynamics. The simulations reveal the sheer magnitude of the effect of isometric scaling on h: the model jumped 40 cm when human-sized, and only 6 cm when miniaturized to the size of Microcebus. This puts the 33 cm jump height of Microcebus [2] in a different perspective: Microcebus is not performing poorly compared to humans, as Borelli and his followers would have concluded, but instead jumps to more than five times the height expected on the basis of isometrically downscaling a human body. The same is true for other small mammals such as rats, which also seem to be able to achieve jump heights of 50 cm or more [29].
In a general sense, the results of the present study merely reiterate what had already been claimed by Bennet-Clark ( [12]). However, Bennet-Clark's predictions (equation 3, Fig. 4C) were purely based on the argumentation that a smaller animal has a smaller distance over which to accelerate CM and hence a smaller push-off time; they took into account neither the positive effect of isometric downscaling on relative strength explained above, nor the negative effects of the force-velocity relationship on actual peak power output (Fig. 4D, Fig. 5D) and work (Fig. 4E) during the jump. The present study quantified the effects of pure isometric scaling on jumping performance using forward dynamic simulations with a realistic musculoskeletal model. Here, it was not necessary to adopt Bennet-Clark's assumption that the shapes of time-histories of force, velocity and hence power are consistent across scales, which they are not (Fig. 3). Even for current scientists who did not adhere to Borelli's law in the first place and considered its role in the present paper as that of a straw man, the sheer magnitude of the effect of pure isometric scaling on h and other relevant variables, as borne out by the simulations (Fig. 4), may still come as a surprise.
The simulation model used in this study is realistic in that it takes into account the fundamental properties of the components of the musculoskeletal system and in terms of parameter values represents a human musculoskeletal system. However, after miniaturization it obviously does not represent the musculoskeletal system of small primates. There are many morphological and physiological differences that may help small primates to jump higher than a downscaled human model. Let us address a few of these differences and their functional implications, armed with the insights gained from the simulations. First of all, small jumping primates may have relatively muscular legs; for example, the muscle mass contained in both legs together is about 25% of total body mass in Galago senegalensis [3] and only about 17% in humans [30]. It will be obvious that this benefitsŴ W ,Ê E kin,to and hence h. Second, small primates have relatively long leg segments, including an elongated metatarsal segment ( [17]), which benefits the transfer from joint angular velocities to vertical velocity of CM. Third, small primates have relatively short muscle moment arms [28], which benefits the transfer fromṽ v CE to angular velocities. Having relatively long muscle fibres would tend to reduceṽ v CE itself, but the author has not come across any comparisons of relative muscle fibre length among differently sized primates in the literature. Fourth, an important role has been claimed for compliant structures in series with muscle fibres in the vasti of Galago senegalensis [3]. It is possible that the 'catapult action' of these structures contributes more to jumping performance in small jumping primates than in humans. However, this action depends on precisely how moment arms vary with joint angles [31], and therefore its quantification requires simulations with detailed species-specific musculoskeletal models that, unfortunately, are currently not available. Fifth, small animals have equally long myosin filaments as large animals (1.60 mm, [32]) but shorter actin-filaments (e.g., 1.04 mm in rats, 1.16 mm in Rhesus monkeys, 1.27 mm in humans, [32]). Thus, in a small animal, a unit of muscle fibre length will have more sarcomeres in series and, at a given rate of sliding of actin relative to myosin, higher velocity and power output than in humans, all else remaining equal. Sixth, small jumping animals tend to have higher percentages of fast twitch fibres in important leg extensors such as vastus lateralis (more than 95% in Microcebus, [33], and 100% in bushbabies, [34]) than humans (less than 60%, [35]), which obviously benefits the power output per kg muscle tissue. Seventh, the maximal shortening velocity of muscle fibres and the rate of force development are higher in small animals than in large animals (e.g., [36][37][38][39]) because of differences in intrinsic contractile properties and myofibrillar protein composition [36,39]. The latter variations are referred to in the literature as 'effects of scaling' (e.g., [36,37]), but this is confusing because isometric scaling does not affect these variables directly. Rather, isometric scaling directly affects potential performance, as clearly demonstrated in the current study, and morphological and physiological adaptations may occur that partly or completely counteract the variations caused by isometric scaling, thereby determining actual performance. For example, small animals may jump high despite the performance-limiting effects of being small revealed in this study, by virtue of adaptations causing their muscles to be very fast, and/or large animals may jump high by virtue of the performance-enhancing effects of being large revealed in this study, and therefore can afford adaptations causing their muscles to be slower and metabolically cheaper. We are still a long way from understanding the effects of morphological and physiological differences among different animals on locomotor performance, but the results of the current study indicate that they compensate for major effects of isometric scaling (Fig. 5A). Clearly, the effects of isometric scaling on jumping performance, as revealed here by simulations with a model that includes key aspects of muscle dynamics, should be taken into account in the study of functional morphology and evolution of jumping animals.


extfieldsofstudy:
Biology
Medicine
Computer Science

text:
MODexplorer: an integrated tool for exploring protein sequence, structure and function relationships

Summary: MODexplorer is an integrated tool aimed at exploring the sequence, structural and functional diversity in protein families useful in homology modeling and in analyzing protein families in general. It takes as input either the sequence or the structure of a protein and provides alignments with its homologs along with a variety of structural and functional annotations through an interactive interface. The annotations include sequence conservation, similarity scores, ligand-, DNA- and RNA-binding sites, secondary structure, disorder, crystallographic structure resolution and quality scores of models implied by the alignments to the homologs of known structure. MODexplorer can be used to analyze sequence and structural conservation among the structures of similar proteins, to find structures of homologs solved in different conformational state or with different ligands and to transfer functional annotations. Furthermore, if the structure of the query is not known, MODexplorer can be used to select the modeling templates taking all this information into account and to build a comparative model. Availability and implementation: Freely available on the web at http://modorama.biocomputing.it/modexplorer. Website implemented in HTML and JavaScript with all major browsers supported. Contact: anna.tramontano@uniroma1.it Supplementary information: Supplementary data are available at Bioinformatics online

INTRODUCTION
Exploring the sequence, structure and function relationships between a protein and its homologs is a powerful strategy to transfer functional annotations such as, for example, ligand-binding sites and/or to detect the existence of alternative conformational states. Likewise, in homology modeling, a thorough survey of all homologs with known structure often can lead to building much better models than obtained by automatically selecting the closest homolog as a template.
Current tools either provide only a general overview of sequence-structure-function relationships [e.g. GeneSilico Metaserver (Kurowski and Bujnicki, 2003) or MESSA (Cong and Grishin, 2012)] or focus on selected aspects, such as ligand-binding sites [e.g. firestar (Lopez et al., 2011)]. A platform for the in-depth integrated analysis of the sequence, structure and function relationships is still missing. Consequently, the information contained in remote relationships may be missed or exploited only through tedious procedures.
Here we describe MODexplorer, a web server that integrates sequence analysis and structure comparison with functional annotations. It provides a more complete and detailed view of the sequence, structural and functional diversity within and between protein families than existing tools. If the structure of the query protein is not known, it permits to select a template for homology modeling and to automatically build the model.

TOOL DESCRIPTION
A snapshot of the user interface of MODexplorer is shown in Figure 1. A detailed description of integrated software and databases, along with the parameters and references, is included in Supplementary Data and available on the server. MODexplorer accepts as input a protein sequence, PDB code or PDB file.

MODexplorer then
Creates the multiple sequence alignment (MSA) of the query protein family using HHblits (Remmert et al., 2012).
Generates alignments to proteins of known structure using HHSearch (So¨ding, 2005) and the PDB database filtered at 70% sequence identity.
For every HHSearch hit, retrieves and aligns related PDB chains, i.e. chains with a sequence similarity 470%. Such 'redundant' chains can be useful. For example, they might have been solved in complex with different ligands, represent alternative conformational states or have a better structural quality.
Displays alignments to the HHSearch hits and their related PDB chains both schematically with a BLAST-like bar diagram and as multiple sequence alignments including query and hit sequences, and their homologs.
Graphically shows annotations on the alignments. The annotations include ligand-and DNA-/RNA-binding sites, secondary structure (predicted for the query and calculated for chains of known structures), disorder (predicted for the sequence query and estimated by B-factor/missing residue annotations for the structure query and known hit structures), HHSearch similarity scores and QMEAN (Benkert et al., 2011) scores of models built based on the alignments. *To whom correspondence should be addressed. y The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Allows filtering of the hits based on the presence of nucleic acids and other ligands in the structures, on the HHSearch scores, the experimental technique used to solve the structures and the crystallographic resolution.
Allows modeling of the query based on any selected alignment. Models are built using Modeller (Sali and Blundell, 1993) and evaluated using QMEAN.
Provides visualization of structural superpositions of PDB chains and model structures in Jmol (http://www.jmol.org/). Enables superposition of PDB chains based on pairwise alignment inferred from the alignments to the query. This is useful, for example, to verify if a low scoring hit has a similar structure in the aligned region as a higher scoring one.
Allows the assessment and modification of the alignments via our interactive alignment editor MODalign (Barbato et al., 2012).
Provides management utilities such as eliminating the hits and creating a 'favorites' list of relevant hits.

CONCLUSIONS
MODexplorer can be used both to explore the protein sequence, structural and functional diversity and to help in template selection in homology modeling. Thanks to tight integration of sequence, structure and function information, MODexplorer provides a comprehensive overview of the features of the target protein and of its homologs, helps in detecting remote homologs and facilitates the selection of templates for modeling.


extfieldsofstudy: Biology Medicine

text:
Comparative Genomic Analysis of Brucella melitensis Vaccine Strain M5 Provides Insights into Virulence Attenuation

The Brucella melitensis vaccine strain M5 is widely used to prevent and control brucellosis in animals. In this study, we determined the whole-genome sequence of M5, and conducted a comprehensive comparative analysis against the whole-genome sequence of the virulent strain 16 M and other reference strains. This analysis revealed 11 regions of deletion (RDs) and 2 regions of insertion (RIs) within the M5 genome. Among these regions, the sequences encompassed in 5 RDs and 1 RI showed consistent variation, with a large deletion between the M5 and the 16 M genomes. RD4 and RD5 showed the large diversity among all Brucella genomes, both in RD length and RD copy number. Thus, RD4 and RD5 are potential sites for typing different Brucella strains. Other RD and RI regions exhibited multiple single nucleotide polymorphisms (SNPs). In addition, a genome fragment with a 56 kb rearrangement was determined to be consistent with previous studies. Comparative genomic analysis indicated that genomic island inversion in Brucella was widely present. With the genetic pattern common among all strains analyzed, these 2 RDs, 1 RI, and one inversion region are potential sites for detection of genomic differences. Several SNPs of important virulence-related genes (motB, dhbC, sfuB, dsbAB, aidA, aroC, and lysR) were also detected, and may be used to determine the mechanism of virulence attenuation. Collectively, this study reveals that comparative analysis between wild-type and vaccine strains can provide resources for the study of virulence and microevolution of Brucella.

Introduction
Brucellosis is one of the most common bacterial zoonoses endemic in many countries, particularly those with developing livestock industries [1]. Among these, China is a large stock-raising country with approximately 300 million sheep [2]. Along with the increased number of animals, the incidence of human brucellosis has also increased rapidly over the last 10 years, which has negatively affected the public health and the livestock industry [3]. B. melitensis was the predominant strain associated with human brucellosis in China from 1950 to 2010 [2][3][4]. As a result, Brucella vaccination has become one of the most important measures for preventing and controlling brucellosis in animals, but different vaccine strains are used for different animals. For example, B. abortus B19 and B. abortus RB51 are used to vaccinate cattle [5][6][7] and B. melitensis Rev. 1 to vaccinate sheep and goats [8,9].
In addition to these strains, the B. melitensis vaccine strain M5 was developed in 1962 by the Harbin Institute of Veterinary Medicine at the Academy of Agricultural Sciences. This vaccine was derived from the virulent strain of B. melitensis M28 and used mainly to vaccinate both pregnant and non-pregnant sheep and goats [2]. B. melitensis vaccine strain M5 exhibited superior protection against brucellosis in sheep and goats as demonstrated by the first large-scale use in 1970 [2]. However, the molecular and physiological mechanism causing the loss of virulence of this strain is not well understood. Species identification and subtyping of Brucella isolates are very important for epidemiologic surveillance, especially for differential diagnosis between infected and vaccinated animals. To address this problem, a series of methods have been reported. For example, Cloeckaert et al. discovered a unique mutation in the rpsL gene of Rev. 1 that eliminates an NciI restriction site; thus, this mutation can be detected by polymerase chain reaction-restriction fragment length polymorphism [10]. In addition, B. abortus strain-specific PCR assays were conducted to identify and distinguish specific B. abortus field strains from the vaccine strains S19 and RB51 [11][12][13][14]. Several other B. melitensis attenuated strains have also been sequenced and the data are available [15][16][17]. However, none of these studies addressed factors affecting virulence in the M5 vaccine strain. Thus, the primary aim of this study was to identify candidate ORFs associated with virulence in 16 M or lack of virulence in M5. Using bioinformatics, this study is the first to perform whole genome analysis directed toward identifying the sequence differences between the B. melitensis vaccine strain M5 and the virulent strain 16 M and reveals a mechanism of attenuation genes.

Results and Discussion
General features of the B. melitensis vaccine strain M5 genome High-throughput bioinformatics revealed 26 genes present in the M5 genome. These genes were compared to those of the 16 M stain, and all were found to be homologues of the 16 M genome. These data suggest that the genomes of M5 and 16 M have a very similar genomic structure.
Genome assembly and annotation. The draft-quality genome of the vaccine strain M5 was sequenced and assembled into 95 contigs with N50 = 775,745 bp (Table S1). Scaffold 1, the largest scaffold, comprised most of chromosome II. Further analysis by PCR sequencing showed that this scaffold contained a complete 16S rRNA gene which was different from the copy in chromosome I. However, we did not continue to close the whole gap caused by duplication of the 16S-23S-5S rRNA operon. Compared to the sequenced genome of B. melitensis 16 M, the size, number of open reading frames (ORFs), guanine-cytosine (GC) content, gene length, and gene density were largely conserved in the genome of M5 (Table 1).
In the M5 genome, we identified 3305 ORFs that were larger than 300 bp (100aa), covering 86.5% of the genome, of which 80.9% matched the NCBI COG database with an e-value less than 1e-5 (Table S2). Many genes of these ORFs were located in common pathogenic pathways, such as virB and bvrS/bvrR. In addition, the previously reported genomic island containing a type IV secretion system [14] was also identified in the vaccine strain M5. Moreover, two additional GC islands with unknown functions were identified, and appeared to be possible transfer elements by a horizontal transfer with extremely low GC content. We also detected all 3 rRNA operons in the genome, which was consistent with the B. melitensis 16 M. However, 2 SNPs in the rRNA sequences within chromosome II of M5 were identified when compared to those of chromosome I.

Genome region differences
Genomic arrangement. Although there was homology between the genes of M5 and 16 M, the genome layout of M5 suggests a ,56 kb inversion or rearrangement in the head of scaffold 1, which mapped to chromosome II of 16 M. This region is flanked by two duplicate sequences of the insertion sequence (IS) IS1953. Two genes located upstream (BMEII0183/BMII0184) and downstream (BMEII0227/BMEII0228) of these inversion sequences were associated with transposes. In addition, inactivated derivatives were found in the IS elements. These findings suggest that the inversion was due to the duplication of insertion elements.
Aligning this 56 kb inversion region with other Brucella strains revealed that genomic inversions vary among available genomes including; B. microti CCM 4915, B. canis ATCC 23365, B. suis ATCC 23445, and B. suis 1330; similar to B. ovis ATCC 25840 and B. melitensis ATCC 23457; and missing in B. abortus bv.1 str.9-941, B. abortus S19, and B. Abortus 2308 (Table 2). Functional analysis of this 56 kb inversion indicated that the region contains a large number of peptide ABC transporter genes (19 of 42 genes) such as spermidine-putrescine (pot), oligopeptide (opp), and dipeptide (dpp) systems. These ABC transporter genes play an important role the virulence of Brucella stains, and can influence trafficking of Brucella to compartments associated with endoplasmic reticulum where peptides and amino acids can be found in large numbers. However, absence of these transporters can be negative effects. For example, loss of the opp system in B. ovis may deregulate peptide uptake resulting in increased peptide levels and reduced intracellular survival [18].
Genome deletions. This inversion region revealed a potential marker for distinguishing infected animals from animals vaccinated with M5. To explore this possibility, we designed primers and verified the results by testing various strains. Comparative genomic analysis identified 11 low read coverage regions and 2 high coverage regions of more than 300 bp each in the 16 M genome. Except for deletion, the other possible reason previous SOLEXA reads matching failed to identify these regions is that they were side effects of genomic rearrangement, where reads only match references with half-lengths. However, these characteristics may not produce a gap over 300 bp (100aa, 4 folds of reads length). Among these regions, we hypothesized that the low coverage regions in the 16 M genome comprised the majority of the genomic differences observed in the M5 genome. To test this, we examined with the assembled genome of M5 to verify whether these regions were missing. However, the large regions were not missing in the M5 draft genome, and only 11 large RDs were found: RD1 to RD11, 5 in chromosome I, and 6 in chromosome II ( Table 3). The differences between the whole genomes of M5, 16 M, and other Brucella strains, are presented in Table 3 and Figure 1. Sixteen genes of 16 M genome were located in the RDs. The accurate genomic positions of these RDs were determined using the assembled M5 genome data. After the assembled M5 genome was compared with the 16 M genome, 5 of 11 RDs (RD1, RD2, RD4, RD5, and RD7) were confirmed to vary in the 16 M genome. The variation of these 5 RDs are described as follows: RD1 included two ORFs, BMEI0194 and BMEI0195. BMEI0194 encodes a hypothetical cytosolic protein, whereas BMEI0195 encodes the ATP-dependent chaperone ClpB, which is essential for heat-shock response and belongs to the Class 1 family of Clp/Hsp100 AAA+ ATPases. It is involved in the recovery of the cell from heat-induced damage in cooperation with DnaK, DnaJ and GrpE.
RD2 contained 1 ORF, BMEI0934, encoding the ATPdependent RNA helicase, which catalyzes the unwinding of double-stranded nucleic acids. Helicases are tightly integrated (or coupled) components of various macromolecular complexes involved in processes such as DNA replication, recombination, and nucleotide excision repair, as well as RNA transcription and splicing.
RD4 contained 1 ORF, BMEI0407, which encodes a hypothetical protein. A 59 bp repeat region was found in the flank of this region in the 16 M genome, and each of the two flanking repeats was duplicated on each side ( Fig S1). This region is a candidate for multi-locus variable-number tandem-repeat analysis detection.
RD5 contained 2 ORFs, BMEI1661 and BMEI1662. BMEI1661 is part of an operon, whereas BMEI1661 belongs to the resolvase family ( Fig S1). Notably, BMEI0902 in chromosome I of 16 M also belongs to the resolvase family. Located in a region with high read coverage, this gene is considered a duplicate in the M5 genome. Of the whole 231-bp sequence of BMEI0902, 180 bp was identical to BMEI1661. This observation suggests that the two genes are possible homologs, although BMEI1661 contained other sequences more than 500 bp that were not present in any other species. This kind of use bias in different site-specific recombinases may lead to various acquisition abilities among different DNA fragments.
RD7 contained 1 ORF, BMEII0259, which encodes the Asp-tRNAAsn/Glu-tRNAGln aminotransferase A subunit and related amidases. In this gene, a 21 bp fragment in M5 replaced a 6 bp fragment in 16 M. This mutation may minimally affect the protein function because a 36 nucleotide insertion in a gene does not cause a downstream frame shift. Thus, we considered this region a normal mutation causing the low read coverage.
The other 6 RDs may be caused by large numbers or continual SNPs in the core regions ( Figure S1). These regions were compared to all available Brucella genomes. In addition to RD4 and RD5, all other RDs were specific in the vaccine strain M5 compared with the virulent strain 16 M. RD4 and RD5 showed large divergence among Brucella, suggesting that they are active mobile elements and can be targets for typing and detection of Brucella. RD4 is a tandem repeat with different lengths in each genome ranging from 58 bp to 342 bp. A similar method was investigated in all Brucella genomes. Figure 1 shows all the RDs compared with the M5 genome. Unique genome regions. Chromosome I of M5 contained two regions with high read coverage, suggesting duplication. These regions were identified as RI1 and RI2. RI1 contained the ORF BMEI0200, which encodes a transposase insertion sequence in the transposon Tn4003. This transposon was also found in 16 M, where another copy was also found. RI2 contained two ORFs, BMEI0901 and BMEI0902. BMEI0901 encodes a resolvase similar to phage Mu DNA invertase Pin homologs, whereas BMEI0902 encodes a site-specific recombinase similar to an antirepressor (ant) gene. This gene encodes a phage-related DNA binding protein and is homologous to BMEI1661 in RD5. Comparative genome analysis revealed that most available Brucella genomes contained only one copy of BMEI0901/BMEI0902. However, B. canis ATCC 23365, B. suis 1330, B. melitensis ATCC 23457, B. abortus bv.1 str.9-941, B.abortus S19, and B. Abortus 2308 as well as the M5 strain, have two copies of these ORFs in each genome. Unlike other genomes, we did not find BMEI0901/ BMEI0902 homologues in the genome of B. ovis ATCC 25840. Thus, this site-specific recombinase widely exists in almost every Brucella genome, suggesting that it originates from a Brucella ancestor. Interestingly, the recombinase appears to the same family to RD5, suggesting that it complements the absent genes in that region. Similar to RI2, RD5 exhibits different patterns of these genes in different genomes (Table 4).
Genome SNPs. Beyond the 11 RDs, M5 and 16 M exhibit considerable general genetic homology. Pairwise analysis demonstrated that there were 2819 SNPs between the genomes of 16 M and M5. Among these SNPs, 585 were in the intergenic regions and 2234 were in the genetic regions. Although the genome divergence time can be calculated by non-synonymous intra-species mutations, the splice time of these two genomes is difficult to estimate because the genome is under manual selection pressure. The 2234 SNPs in the genetic regions included 798 synonymous SNPs and 1436 non-synonymous SNPs (Table S3). The non-synonymous SNPs only resulted in amino acid changes, with no frame shift or nonsense mutations.
Delrue et al. presented a list of 196 unique virulence-related genes divided into 12 functional classes [19]. Our comparative analysis of these two genomes suggests that SNPs widely exist in these genes. A total of 183 SNPs were present in 197 genes, among which 101 SNPs were non-synonymous mutations. Compared with SNPs occurring at other sites of the genome, these genes presented higher positive selection pressure, with Ka/Ks of 1.23. Detailed function analysis of these virulence-related genes revealed that most functional categories were involved in fast evolution ( Figure 2 and Table 5). Within all known functional groups, both total SNP rates and non-synonymous SNP rates were higher in the genes of ''metal acquisition'' and ''nitrogen metabolism,'' and both Ka/Ks values were greater than 1. The Ka/Ks values were also large in classical virulence factors, including genes of envelope molecules and secretion or transport system. These data suggest that mutations in these genes can play important roles in virulence attenuation of the vaccine strain. However, the SNP rates and Ka/ Ks were also high in the groups of unknown function and other genes, suggesting the need for further investigation into the functions of these genes.

Virulence-associated metabolism and virulence
factors. Comprehensive analysis of the 16 M and M5 genomes revealed variations in genome deletion, insertion, and SNPs. Given these genomic differences, we next sought to identify  Table S4. Envelope molecule. The LPS O-polysaccharide (O-PS) is the first molecule clearly involved in Brucella intracellular entry [20,21]. Genetic studies demonstrated that the O-PS is synthesized by the so-called ABC transporter-dependent pathway, which characterizes homopolymetric polysaccharides. In Brucella, the genes involved in the synthesis of the O-PS and its translocation to the periplasm are clustered in the Wbk region of chromosome I (ORF BMEI1393 to BMEI1427) [22]. In our analysis of M5, no non-synonymous mutations were found in the core genes of this region (wbpZ, wbkA, rfbD, and others); however, several mutations Secretion or transport system. In addition to envelope molecules, the type IV secretion system (T4SS) of Brucella encoded by the VirB operon is a major virulence factor [23]. This operon is composed of 12 ORFs clustered in chromosome II (BMEII0025 to BMEII0036). We identified three ORFs associated with the M5 T4SS that contained single or two sites of non-synonymous mutations. These included: A463C (K155Q) in inner membrane ATPases (virB4, BMEII0028), A622G (T208A) and T1007A (F336Y) in the channel protein (virB6, BMEII0030), and C700G (R234G) in the channel protein (virB9, BMEII0033). The virB mutants cannot reach the replicative niche and reside in a  membrane-bound vacuole [24]. In addition to mutations in the T4SS mutations C613G(P205A) in the ABC transporter (BMEII0923), C188T(A63V) and C659T(T220I) in the flagellar gene BMEII0154, and A427G(I143V) in the flagellar gene BMEII0159 were also identified. Recent studies showed that some proteins involved in the export of flagellar components are similar to the components of the type III secretion machinery, and flagellar apparatus itself can secrete virulence factors [25]. These data suggest that defects in virulence factor secretion may affect the replicative ability of M5.
Regulation. Brucella must survive under varying conditions ranging from the open environment to the intracellular medium. Thus, the bacterium must coordinate an intricate network of factors to generate a suitable adaptive response to various signals. The adaptive response is mediated by two-component regulatory systems (TCSs) and transcriptional regulators (TRs) [26]. To date, BvrS/R is the best characterized Brucella TCS [27]. Moreover, BvrS/R regulates several critical cellular functions, including cell division, chemotaxis, metabolism, and expression of toxins and other proteins important for pathogenesis. TRs important for Brucella survival are the GntR and LysR families. In addition, LysR-type transcriptional regulators (LTTRs) are widely distributed in the diverse genera of prokaryotes [28]. Many virulence factors of Brucella are regulated by LTTRs [29,30]. Although no mutations were observed in the common TCSs or TRs, we did identify a non-synonymous mutation of T176C(L59P) and a 3 bp deletion from 391 to 393 at the M5 locus encoding LTTR (BMEI0116 in 16 M). These data suggest that M5 may have reduced ability to regulate survival in certain environmental conditions.
Metal acquisition. Iron acquisition and transport systems are essential for intracellular survival ability. For instance, Brucella with a mutation in the Fe 3+ uptake gene dhbC, which affects DBHA siderophore synthesize, exhibits a high degree of attenuation [31]. In B. abortus, DHBA is important for bacterial growth on erythritol, explaining its potential role in cattle abortion [32,33]. Detailed comparison between the in vitro and in vivo phenotypes of B. melitensis dhb mutants and strains with defined mutations in the erythritol catabolic genes is necessary to more accurately define the relationship among DHBA production, erythritol catabolism, and virulence in the natural ruminant host. The role of the TonB transport system is well documented in Salmonella entrica [34] and Shigella dysenteriae [35]. The tonB mutants in these bacteria exhibit reduced growth in host cells, and the TonB complex is required for optimal uptake of DHBA siderophore and/or citrate in B. melitensis [36]. However, few studies have evaluated the expression of iron-related genes in Brucella spp. [31,32]. The comparison between the transcript levels of B. melitensis 16 M and B. melitensis M5 during macrophage infection led us to presume that some common and differentially expressed transcripts of iron acquisition genes could be identified. In addition, previous proteomics studies of the vaccine strain M5 showed altered expression of proteins associated with iron utilization (data unpublished). To verify this hypothesis, we next focused on analyzing differences metabolic activity between M5 and 16 M.
Amino acid metabolism. Among the 19 synthesis subclass genes of these two strains, 10 genes of M5 contained point mutations. These data suggest that M5 may have some amino acid metabolism defects; yet failure of M5 to resist a sudden lack of nutrients requires further investigation.
Sugar metabolism. Maltose, ribose, arabinose, galactose, glucose, glycerol, erythriol, and inositol degradation pathways are essential for the intracellular survival of Brucella [37]. For example, mutants of genes encoding sugar transport systems, such as pyruvate carboxylase (BMEI0266), ribokinase (BMEII0089), 6phosphogluconate dehydrogenase (BMEII1124), inosamine methylase (BMEI1301), glycosyl transferase (BMEII1101), ribose transport (BMEII0300), and glucose/galactose transport (BMEII1053) limit carbon or energy supply and affect gene regulation. In addition, the production of cyclic b-1,2 glucan (BMEI1837) is necessary for the intracellular replication of Brucella [38]. Upon examination of these pathways, SNP mutations in the ORF BMEI1837 of M5 caused five residue mutations at the positions 3587 to 3594, 3631 to 3638, and 3774 to 3782, and another non-synonymous mutation at position 4996. These data suggest that M5 may have defects in cyclic b-1,2 production. DNA/RNA. Purine/pyrimidine synthesis, repair, and regulatory genes are essential for the intracellular replication of Brucella. A study by Crawford RM indicated that deletion of the purE gene from B. melitensis dramatically reduced bacterial virulence and suggested that this gene should be further evaluated as a potential vaccine [39]. Other genes under the same classification, such as xseA, purL, purD, mgpS, tldD, and cobB mutants, appear to be targets for attenuation vaccine strains.
Stress proteins/Chaperones. Genes encoding stress proteins have been an obvious choice for directed mutagenesis for virulence studies and vaccine generation. Within the M5 genome, genes encoding chaperones (BMEI2002 and BMEI1513) showed one non-synonymous mutation, namely, A37C(M13L) and G398A(G133D), respectively.
Oxidoreduction and nitrogen metabolism. DsbA has been shown to be involved in toxin production, adhesion, motility, extracellular enzyme production, and type III secretion system [40]. Few studies have investigated the role of DsbB in pathogenicity [41]. A G120A(M40I) mutation was detected in the DsbB (BMEI0384) gene of M5. In addition, published genome analysis indicated that B. melitensis can adapt to either aerobic or anaerobic environments. Among the genes involved in the process, we detected non-synonymous mutations in FdhA, glnA, nifS, and glnD of M5.
Unknown function. Seven ORFs encoding hypothetical protein were slightly different between the attenuated M5 strain and the virulent strain 16 M. Among these ORFs, BMEII0128 was identified as a virulence-associated ORF in another attenuated vaccine strain S19. Mutations of A31C(M11L), C589T(H197S), and A590C(H197S) were also identified in the vaccine strain M5.

Conclusions
The vaccine strain M5 has been widely used in Asia for decades to vaccinate sheep against brucellosis [2]. While extensive studies have been performed on the phenotype and physiology of this strain, the molecular mechanisms responsible for loss of virulence are unclear. In this study, we determined the whole-genome sequence of M5 and conducted a comprehensive comparative analysis against the whole-genome sequence of the virulent strain 16 M. This comparative analysis revealed that the genome sequences were highly conserved with more than 99% identity, which agreed with previous studies. However, we identified 11 RDs and 2 RIs by the reads-to-genome comparisons. The gene and genomic structure analysis of RDs and RIs revealed that only 5 RDs and 1 RI showed a consistent variation with a large deletion in the M5 genome compared to 16 M. The other RDs and RIs displayed multiple SNPs, which caused the low read coverage. In addition, a genome fragment with a 56 kb rearrangement was determined to be consistent with the previous study. Based on these results, we suggest the use of genome sequence differences to create a precise map for effective vaccine detection and diagnosis. RD4 and RD5 indicated a large diversity among all Brucella genomes (Table S5) not only in RD length but in copy number as well. Thus, they are strong candidate sites for typing different Brucella strains.
RD4 is a typical random repeat region consisting of the sequence ''GGGGATCAAGA-GATCGCCCCCGTTTCCGTCGCCGTTCCCGTGCTGCC-GAAGGCACACAAAGAGAAGAAACATGGCGGCGACGG-CAATTGTTGAAAGCGCCATCATTTTTCGTGGGTGAC-GATGAC,'' which repeats 1 to 2 times in each genome and can be considered the candidate VNTR loci for MLVA genotyping. RD5 is 883 bp and overlaps with two resolvase family genes in 16 M. The ORFs BMEI1661 and BMEI0902 belong to this family, which are 231 and 747 bp respectively. The proteins encoded by these ORFs have the same core functions but different protein and structure combinations. Comparative genome analysis in Brucella revealed two typical sequence types present in this region. The first sequence was approximately 139 bp and contained a gene similar to BMEI0902. The second was 883 bp and contained a gene similar to BMEI1661. In our study, RI2 belongs to the first sequence. Compared with the 16 M genome, the M5 strain had an additional RI2 region but the RD5 was missing. Further analysis in all Brucella strains presented different patterns of these two regions in the various genomes. This bias of the selected site-specific recombinase has been shown to be involved in the acquisition of drug resistance genes and alteration of gene expression [42], suggesting that this unique gene affects virulence.
Considering the genetic patterns widely present in all strains, we consider the 2 RDs, 1 RI, and the inversion region as candidates for detection of genome differences. Our analysis was also aimed at identifying 1) the genes or genome fragments that only existed in our genome,2) the mutations specific toward the virulent strain and the attenuated strain, and 3) the molecular mechanisms of M5 strain attenuation . Among the 11RDs identified, RD1, RD2, and RD7 were in M5, whereas they are consistent with other strains in 16 M. Furthermore, RD3, RD6, RD8, RD9, RD10, and RD11 were highly differentiated regions with a large number of mutations. All of these regions are possible candidates for distinguishing M5 from 16 M. Notably, RD9 includes an ORF encoding an ABC-type Fe 3+ transport system (sfuB, BMEII0566).
In Brucella, SfuA binds free Fe 3+ in the periplasm, and SfuB and C transport this cation across the cytoplasmic membrane. Several studies suggested that the ferric dicitrate complex itself represents important substrates for the SfuABC type transporters [32,33]. On the basis of the potential relevance of ferric dicitrate as an iron source for Brucella in vitro, the sfuB mutation seemingly is responsible for the loss of virulence in vaccine strain M5. Surveys of the genome sequences of B. melitensis 16 M and B. suis 1330 suggest that transport systems that allow the Brucella to utilize heme and ferric dicitrate as iron sources are important for sustaining the intracellular lifestyle of the Brucella in host macrophages [43].
The whole-genome sequences analyzed in this study clearly revealed large genome differences between 16 M and M5. Many of these differences were in genes associated with Brucella metabolism and survival, and thus serve as potential detection sites for distinguishing between the virulent 16 M and attenuated M5 strains. In addition, our analysis revealed two novel sites for typing Brucella. Furthermore, 3 RDs and SNPs of important virulence-related genes such as motB, dhbC, sfuB, dsbAB, aidA, aroC, and lysR, were considered to be sites causing virulence loss in the M5 strain. Collectively, this study reveals that comparative analysis between the wild-type and vaccine strains can provide resources for the study of Brucella virulence and Brucella microevolution. Further detailed analysis of the gene function of deletion genes can elucidate their roles in virulence attenuation of B. melitensis.

Materials and Methods
Isolation extraction and identification B. melitensis M5 was obtained from the National Institute for Communicable Disease Control and Prevention Center. This strain was submitted for classical identification procedures as follows: CO 2 requirement, H 2 S production, and inhibition of growth by basic fuchsin and thionin, agglutination with monospecific antisera, and phage typing. Total genomic DNA was extracted using the DNeasy Blood and Tissue Kit (QIAGEN, China Ltd., China). An aliquot of the DNA was subjected for analysis using the Bioanalyzer (Agilent Technologies) and was confirmed for absence of DNA degradation.

Genome sequencing, assembly, and annotation
A genome shotgun method was used to collect the genome sequence. We constructed two DNA libraries: 500 bp and 3 kb,5 kb DNA fragments for high-throughput genome sequencing with SOLEXA. Pair-end 75-bp reads were collected. We obtained 518 Mb and 7,000,000 reads covering ,157 folds for the 16 M genome. The draft genome sequence of B. melitensis vaccine strain M5 is available in GenBank under accession number AONT00000000.1.
The SOLEXA data were assembled using the SOAP de-novo software. Genome sequences were analyzed using an automatic genome analysis pipeline. Gene prediction was conducted following the method used in the study by Glimmer [44] with default parameters. Gene annotation was performed with BLAST compared with nt, nr, and SwissProt databases with an e-value of 1e-5. Gene function prediction was conducted using COG and InterproScan. Gene pathways were annotated based on Kyoto Encyclopedia of Genes and Genomes.

Comparative genomic analyses
High-throughput SOLEXA reads mapping to 16 M was used in the whole-genome comparison because that the draft genome had limits in gene prediction and alignment. Genome differences were valued by each site of SOLEXA coverage, which is standardized by calculating the CDM (read coverage of each site/mean coverage of the whole genome). The log2 value of the CDM (Log2CDM) was used to improve the description.
We compared the sequences of two strains by MUMmer [45] and removed unclear fragments by BLAST. The IS elements were identified and compared based on the IS database (http://www-is. biotoul.fr/). Genes lost or gained were selected by PERL scripts, and their functions were determined. A synteny map between the two strains was constructed by the OrthoMCL pipeline [46]. Whole-genome raw SNPs were detected following the method published by Mummer et al. with default parameters. The detailed variations in virulence-associated genes between M5 and 16 M, including SNPs, deletion, and insertion, were further identified by alignment using CLUSTALW and then calculated by PERL scripts. A Ka/Ks calculator was used [47].


text:
New microsatellite markers for Tricyrtis macrantha (Convallariaceae) and cross-amplification in closely related species1

• Premise of the study: Microsatellite markers were characterized in Tricyrtis macrantha (sect. Brachycyrtis, Convallariaceae), a vulnerable species endemic to Japan, to investigate its genetic diversity and population structure. • Methods and Results: Eleven microsatellite markers were developed. The number of alleles per locus ranged from one to seven, and genetic diversity per locus ranged from 0.142 to 0.812. Four markers were successfully amplified in other species of sect. Brachycyrtis (T. ishiiana, T. ishiiana var. surugensis, and T. macranthopsis). • Conclusions: The microsatellite markers can be used to investigate the genetic diversity and population structure of the vulnerable and endangered species of Tricyrtis sect. Brachycyrtis, to aid in the development of conservation strategies for each species.

; Ministry of the Environment of Japan, 2000 ).
Nine microsatellite markers were previously developed for T. ishiiana ( Setoguchi et al., 2011 ); however, only four markers could be amplifi ed in all species of sect. Brachycyrtis . Development of additional microsatellite markers would contribute not only to the elucidation of the genetic structure of T. macrantha populations but will also allow us to compare the genetic diversity among the four taxa of Tricyrtis sect. Brachycyrtis using the same markers. Here, we report the development and characterization of microsatellite markers for T. macrantha and the transferability of T. macrantha markers to the other species of sect. Brachycyrtis .

METHODS AND RESULTS
Genomic DNA was extracted from fresh leaves of one T. macrantha individual from the Kyoto Prefectural Botanical Garden, originally collected from a wild population at Mt. Yokokura (Ochi-cho, Takaoka-gun), in Kochi Prefecture, Japan (33 ° 32 ′ N, 133 ° 12 ′ E). The voucher specimen ( H. Setoguchi JP12005 ) was deposited at the herbarium of Kyoto University (KYO).
Fresh leaf material was frozen in liquid nitrogen and then ground into a fi ne powder. The leaf powder was suspended in 2-[4-(2-hydroxyethyl)-1piperazinyl] ethanesulfonic acid (HEPES) buffer (pH 8.0) and centrifuged (10,000 rpm at 20 ° C for 5 min) to remove polysaccharides ( Setoguchi and Ohba, 1995 ). Total DNA was then isolated from each pellet using cetyltrimethylammonium bromide (CTAB; Doyle and Doyle, 1990 ). The extracted DNA was dissolved in 100 μ L of TE buffer. Microsatellite loci were isolated using an improved technique for isolating codominant compound microsatellite markers ( Lian and Hogetsu, 2002 ;Lian et al., 2006 ). A total volume of 100 μ L containing ~2.5 μ g genomic DNA was digested separately with the blunt-end restriction enzymes Ssp I, Eco RV, and Alu I. The restricted fragments were ligated with a specifi c blunt adapter (consisting of the 48-mer 5 ′ -GTAATACGACTCACTATAGGGCACGCGTGGTCGACGGCCCGGG-C TGGT-3 ′ and an 8-mer with the 3 ′ -end capped with an amino residue: 5 ′ -ACCAGCCC-NH 2 -3 ′ ) using a DNA ligation kit (TaKaRa Biotechnology Co., Ohtsu, Shiga, Japan). Fragments were amplifi ed from Ssp I, Eco RV, and Alu I DNA libraries using one of the compound simple sequence repeat (SSR) primers (AC) 6 (AG) 5 or (TC) 6 (AC) 5 and an adapter primer (5 ′ -CTA-TAGGGCACGCGTGGT-3 ′ ). PCR was performed in a total reaction volume of 50 μ L containing 38.25 μ L sterilized water, 0.2 mM dNTP mixture, 0.125 U AmpliTaq Gold (Applied Biosystems, Foster City, California, USA), 1.5 mM reaction buffer with MgCl 2 (Applied Biosystems), 0.5 μ M of each primer, and 0.5 μ L of template DNA (10 ng/ μ L). The amplifi cation profi les included initial denaturation at 94 ° C for 9 min; followed by 20 cycles of 30 s at 94 ° C, 30 s at 66-60 ° C (reduced 0.3 ° C/cycle), and 1 min at 72 ° C; 20 cycles of 30 s at 94 ° C, 30 s at 60 ° C, and 1 min at 72 ° C; and a fi nal extension at 72 ° C for 6 min. After electrophoresis on a 1.5% agarose gel, the fragments (400-800 bp) were purifi ed using the GENECLEAN II kit (QBioGene, Solon, Ohio, USA) and cloned using the QIAGEN PCR Cloning plus Kit (QIAGEN, Hilden, Germany) in accordance with the manufacturer's instructions. Recombinant clones were identifi ed using blue/white screening on Luria-Bertani agar plates containing ampicillin, X-gal, and isopropyl-β -D -1-thiogalactopyranoside (IPTG ).

CONCLUSIONS
The microsatellite markers presented here will be useful for investigating the genetic diversity and population structure of the species of Tricyrtis sect. Brachycyrtis . The markers that amplifi ed in all species of Tricyrtis sect. Brachycyrtis , developed by Setoguchi et al. (2011) and this study, are now being used to Two hundred and fi fty insert-positive clones were amplifi ed using M13 forward primer (5 ′ -GTAAAACGACGGCCAGT-3 ′ ) and M13 reverse primer (5 ′ -AACAGCTATGACCATG-3 ′ ). The amplifi cation profi les included initial denaturation at 95 ° C for 9 min; followed by 40 cycles of 30 s at 94 ° C, 1.5 min at 54 ° C, and 1 min at 72 ° C; and a fi nal extension at 72 ° C for 4 min. PCR was performed in a total reaction volume of 10 μ L containing 6.75 μ L sterilized water, 0.08 mM dNTP mixture, 0.25 U TaKaRa Ex Taq (TaKaRa Biotechnology Co.), 1.0 mM reaction buffer (TaKaRa Ex Taq ), 0.2 μ M of each primer, and 1.0 μ L of template DNA (10 ng/ μ L). The PCR products in a total reaction volume of 5 μ L, using M13 forward primer, were sequenced using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems). For 48 fragments containing the (AC) 6 (AG) n or (TC) 6 (AC) n compound SSR sequence at one end, primers were designed using Primer3 version 0.4.0 ( Rozen and Skaletsky, 2000 ). Each primer was designed to have <50% of GC content with optimal annealing temperature at 58 ° C, and total size of approximately 20-25 bp. PCR amplifi cations were performed following the standard protocol of the QIAGEN Multiplex PCR Kit (QIAGEN) in a fi nal volume of 6 μ L. Compound SSR primers [(AC) 6 (AG) 5 or (TC) 6 (AC) 5 ] were labeled with the fl uorochromes 6-FAM or HEX (Applied Biosystems). The amplifi cation profi les included initial denaturation at 95 ° C for 15 min; followed by 35 cycles of 30 s at 94 ° C, 1.5 min at 58 ° C, and 1 min at 72 ° C; and a fi nal extension at 60 ° C for 30 min. The size of the PCR products was measured using the ABI PRISM 3100 Genetic Analyzer with GeneScan 350 ROX Size Standard (Applied Biosystems) and GeneMapper analysis software (Applied Biosystems).
Eleven microsatellite loci that showed a clear and strong band on the electrophoretic gel were selected in the preliminary screening ( Table 1 ) . Each reverse primer was designed to contain six repeats of AC followed by fi ve repeats of AG [(AC) 6 (AG) 5 ] or six repeats of TC followed by fi ve repeats of AC [(TC) 6 (AC) 5 ]. For further characterization, 20 individuals of T. macrantha were genotyped using the procedure described above. These plant samples were collected from a population at Mt. Yokokura in Kochi Prefecture, Japan. Three loci were monomorphic and the number of alleles per locus varied from four to seven in polymorphic loci ( Table 2 ) . The observed heterozygosity and genetic diversity per locus ( H S ; Nei, 1987 ) ranged from 0.050 to 0.851 and 0.142 to 0.812, respectively.
Tests for deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium were conducted using FSTAT version 2.9.3.2 ( Goudet, 1995 ). Signifi cant deviations ( P < 0.05) from HWE were detected in all polymorphic loci except TM_11. No signifi cant linkage disequilibrium was found in the pairwise comparisons of the polymorphic loci. Of the 11 microsatellite markers