PGC‐1α plays a central role in hepatic gluconeogenesis. We previously reported that alternative splicing of the PGC‐1α gene produces an additional transcript encoding the truncated protein NT‐PGC‐1α. NT‐PGC‐1α is co‐expressed with PGC‐1α and highly induced by fasting in the liver. NT‐PGC‐1α regulates tissue‐specific metabolism, but its role in the liver has not been investigated. Thus, the objective of this study was to determine the role of hepatic NT‐PGC‐1α in the regulation of gluconeogenesis. Adenovirus‐mediated expression of NT‐PGC‐1α in primary hepatocytes strongly stimulated the expression of key gluconeogenic enzyme genes (PEPCK and G6Pase), leading to increased glucose production. To further understand NT‐PGC‐1α function in hepatic gluconeogenesis in vivo, we took advantage of a previously reported FL‐PGC‐1α−/− mouse line that lacks full‐length PGC‐1α (FL‐PGC‐1α) but retains a slightly shorter and functionally equivalent form of NT‐PGC‐1α (NT‐PGC‐1α254). In FL‐PGC‐1α−/− mice, NT‐PGC‐1α254 was induced by fasting in the liver and recruited to the promoters of PEPCK and G6Pase genes. The enrichment of NT‐PGC‐1α254 at the promoters was closely associated with fasting‐induced increase in PEPCK and G6Pase gene expression and efficient production of glucose from pyruvate during a pyruvate tolerance test in FL‐PGC‐1α−/− mice. Moreover, FL‐PGC‐1α−/− primary hepatocytes showed a significant increase in gluconeogenic gene expression and glucose production after treatment with dexamethasone and forskolin, suggesting that NT‐PGC‐1α254 is sufficient to stimulate the gluconeogenic program in the absence of FL‐PGC‐1α. Collectively, our findings highlight the role of hepatic NT‐PGC‐1α in stimulating gluconeogenic gene expression and glucose production.The transcriptional coactivator  Blood glucose levels are maintained by a tight regulation of glucose uptake by peripheral tissues and glucose production in the liver. During prolonged fasting, liver induces the gluconeogenic pathway that synthesizes glucose from non‐carbohydrate precursors such as pyruvate, lactate, glycerol and alanine. This metabolic process is primarily controlled by key gluconeogenic enzymes such as PEPCK and G6Pase, the activities of which are regulated at the transcriptional level through hormonal and nutrient signals , which cooperatively promote the full activation of PEPCK and G6Pase gene expression   Fig. . NT‐PGC‐α−/− mice deficient in full‐length PGC‐1α (FL‐PGC‐1α) have been described previously . FL‐PGC‐1g for 5 min at 4°C, and then resuspended in DMEM/F‐12 medium supplemented with 25 mmol/L glucose, 10% FBS, 15 mmol/L HEPES, 100 nmol/L dexamethasone, and penicillin/streptomycin. Hepatocytes were plated on collagen‐coated 12‐well plates and allowed to attach for 1 h at 37°C in a humidified incubator. Cells were washed once with DMEM medium containing 5 mmol/L glucose and incubated in the same medium supplemented with 10% FBS, 100 nmol/L dexamethasone, 1 nmol/L insulin, and penicillin/streptomycin for 4 h. The medium was then replaced with serum‐free DMEM medium supplemented with 5 mmol/L glucose, 10 nmol/L dexamethasone, 1 nmol/L insulin, and penicillin/streptomycin. After an overnight incubation, cells were cultured in the same medium containing 10% FBS. When mimicking a fasted condition, cells were treated with or without 1 μmol/L dexamethasone and 10 μmol/L forskolin for 2 h in serum‐free DMEM medium supplemented with 5 mmol/L glucose and penicillin/streptomycin.Primary mouse hepatocytes were isolated as described previously with slight modifications . Recombination of AdEasy‐GFP, AdEasy‐NT‐PGC‐1α‐HA, or AdEasy‐PGC‐1α‐HA with adenoviral gene carrier vector was performed by transformation into BJ5183‐AD‐1 competent cells . Recombinant adenovirus was produced by transfecting recombinant adenoviral plasmids into HEK293 cells  and normalized to the total protein content determined from the whole cell extracts.α‐HA and Flag‐HNF4α were co‐transfected into HEK293 cells using Fugene 6 . Immunoprecipitation assays were carried out as described previously by Chang et al.  and normalized using Renilla luciferase activity. Data represent mean ± SEM of at least three independent experiments.HEK293 cells were transiently transfected with pHNF4‐tk‐luc  at different time points .Total RNA from livers and hepatocytes was extracted and reverse‐transcribed as described previously . Protein concentration was determined using Bio‐Rad DC protein assay reagents according to the manufacturer's instructions.Whole cell extracts were prepared from tissues and hepatocytes by homogenization in lysis buffer   . PEPCK and G6Pase were upregulated in ob/ob mice concomitantly with elevated expression of PGC‐1α and NT‐PGC‐1α  necessary for binding to nuclear receptors such as HNF4α and GR but not the C‐terminal domain required for binding to FOXO1   or control adenovirus expressing GFP (Ad‐GFP). HNF4α is abundantly expressed in HepG2 cells, but its transcriptional activity is very low due to low expression of the PGC‐1α gene  that targets the 31 bp insert arising from alternative splicing of the PGC‐1α gene . Commercial algorithms also predicted that this sequence is not effectively targeted to NT‐PGC‐1α mRNA.To assess whether NT‐PGC‐1α activity by Akt‐ and Cdc2‐like kinase 2 (CLK2)‐mediated phosphorylation    assay using a PGC‐1α antibody. The PGC‐1α antibody has been confirmed for its specificity to immunoprecipitate FL‐PGC‐1α and NT‐PGC‐1α (WT mice) and NT‐PGC‐1α254 (FL‐PGC‐1α−/− mice)  in the G6Pase promoter (data not shown). This might be due to weak or temporal formation of FL‐PGC‐1α‐ and/or NT‐PGC‐1α‐associated transcriptional complex on this region.Elevated expression of NT‐PGC‐1les Fig. , neitherα−/− mice exhibited normal fasting blood glucose levels after a 24 h‐fast  can co‐activate PPARα .PGC‐1ice Fig. A. A 24 hice Fig. A. The inice Fig. B. In conice Fig. C. In agrα plays a key role in fasting‐induced gluconeogenesis by linking hormonal and nutrient signals to the transcription program in the liver. We previously reported that fasting‐inducible NT‐PGC‐1α as well as full‐length PGC‐1α (FL‐PGC‐1α) are produced from the canonical promoter of PGC‐1α gene in the liver but no additional transcripts from the distal alternative promoter of PGC‐1α gene . Our results clearly demonstrate that NT‐PGC‐1α is sufficient to activate the gluconeogenic program in primary hepatocytes. We think that NT‐PGC‐1α activates the promoters of PEPCK and G6Pase genes through interaction with HNF4α and GR, but not FOXO1. Indeed, our ChIP analyses demonstrated that hepatic NT‐PGC‐1α254 is enriched at the PEPCK and G6Pase promoters containing binding sites for HNF4α and GR in FL‐PGC‐1α−/− mice. The in vitro studies further confirmed that NT‐PGC‐1α interacts with HNF4α and increases HNF4α‐mediated transcription of a reporter gene. HNF4α plays a crucial role in the regulation of PEPCK and G6Pase genes  exhibited normal expression of C/EBPβ, PEPCK and G6Pase in the fed and fasted states (Figs. α254 was recruited to the PEPCK and G6Pase promoters with concomitant increase in fasting‐induced PEPCK and G6Pase gene expression, indicating that NT‐PGC‐1α254 is sufficient to stimulate gluconeogenic gene expression in the absence of FL‐PGC‐1α. Moreover, loss of FL‐PGC‐1α function might be compensated by elevated levels of NT‐PGC‐1α254 protein in FL‐PGC‐1α−/− mice. In agreement with normal expression of PEPCK and G6Pase genes, FL‐PGC‐1α−/− mice had normal fasting blood glucose levels and efficiently produced glucose from pyruvate during a pyruvate tolerance test. Previously, Burgess et al. reported conflicting results regarding the fasting response in FL‐PGC‐1α−/− mice. Six‐week‐old FL‐PGC‐1α−/− mice exhibited reduced hepatic glucose production in response to fasting (Burgess et al. α−/− mice may not generate sufficient energy to support gluconeogenesis since rapid growth and maturation are also occurring in this period. It has been shown that FL‐PGC‐1α−/− mice at 4–5 weeks of age exhibit temporary cold intolerance, whereas adult FL‐PGC‐1α−/− mice are able to defend against cold (Leone et al. α−/− mice after a 24 h fast and no alteration in hepatic gene expression profiles of fatty acid oxidation, mitochondrial oxidative phosphorylation, and lipogenic genes.Adenovirus‐mediated PGC‐1α isoforms in the liver? Although this study shows that NT‐PGC‐1α is recruited to the same target genes regulated by FL‐PGC‐1α, it does not address the relative contributions of NT‐PGC‐1α and FL‐PGC‐1α to target gene expression. We speculate that the timing of their action may be different during the course of fasting because protein stabilities of FL‐PGC‐1α and NT‐PGC‐1α are differentially regulated (Zhang et al. α and FL‐PGC‐1α may regulate a subset of isoform‐specific target genes in response to fasting. Genome‐wide occupancy analysis will be needed to elucidate whether there is differential recruitment to genomic sites between NT‐PGC‐1α and FL‐PGC‐1α. Our study also revealed that NT‐PGC‐1α and FL‐PGC‐1α activities are differentially regulated by insulin. Given that NT‐PGC‐1α gene expression is elevated in diabetic livers in ob/ob mice and that NT‐PGC‐1α‐mediated gluconeogenic gene expression is not suppressed by insulin, it is possible that NT‐PGC‐1α overexpression is implicated in the pathogenesis of hyperglycemia. However, in normal physiological conditions, insulin would indirectly repress NT‐PGC‐1α activity by inhibiting glucagon‐induced expression of PGC‐1α gene.What are the physiological reasons for co‐expression of two different PGC‐1α is a functional transcriptional co‐activator promoting gluconeogenic gene expression. Here, we propose that gluconeogenesis driven by NT‐PGC‐1α, along with PGC‐1α, contributes to the elevated blood glucose level in response to fasting.Collectively, this study demonstrates, for the first time, that hepatic NT‐PGC‐1None declared.


The values are calculated by employing the triple ζ basis sets of the Slater type at the DFT level. 1J are calculated modeled by MeSeSeMe (1a), which shows the typical torsional angular dependence on ϕ(CMeSeSeCMe). The dependence explains well the observed 1Jobsd  of small values (≤ 64 Hz) for RSeSeR′ (1) (simple derivatives of 1a) and large values (330–380 Hz) observed for 4-substituted naphto-1,2-diselenoles (2) which correspond to symperiplanar diselenides. 1J  becomes larger as the electron density on Se increases. The paramagnetic spin-orbit terms contribute predominantly. The contributions are evaluated separately from each MO (ψi) and each ψi → ψa transition, where ψi and ψa are occupied and unoccupied MO's, respectively. The separate evaluation enables us to recognize and visualize the origin and the mechanism of the couplings.Nuclear couplings for the Se-Se bonds,  J) provide highly important information around coupled nuclei, containing strongly bonded and weakly interacting states, since the values depend on the electron distribution between the nuclei -1,2-diselenole : Y = H (a)  contribute to 1JTL ? 1JPSO  and 1JSD+FC  are plotted versus 1JTL , although not shown. The correlations are given in  and 1JSD+FC  contribute 65% and 35% to 1JTL , respectively, irrespective of the ϕ(CSeSeC) values:How do given in  and 3),,1JPSO 1J show the torsional angular dependence? What orbitals and transitions contribute to the dependence? 1JPSO  is analyzed next.Why does 1JPSO  is discussed by analyzing the contributions separately from each ψi and each ψi → ψa transition.ϕ dependence of 1JPSO  contributed from ψ1–ψ43, ψ1–ψ38, ψ39–ψ43, ψ39, ψ40, ψ41, ψ42, and ψ43. The contribution from ψ39–ψ43 to 1JPSO  is large, whereas that from ψ1–ψ38 is small, although not shown. The plot of the contributions from ψ39–ψ43 (y) versus those from ψ1–ψ43 (x) provides an excellent correlation (y = 0.976x + 37.3 : r2 = 0.9999).ψ39, ψ40, ψ41, ψ42, and ψ43 and ψ39–ψ41, ψ42–ψ43, and ψ39–ψ43. Contributions from ψ42 and ψ43 exchange with each other at ϕ ≈ 90°. Those of ψ39 and ψ40 do at ϕ ≈ 135°  are very large at 0° and 180°  will be clarified by analyzing the contributions from ψ42 and ψ43 at 0° and 180°. The mechanism would be complex at 90°, since the small magnitude is the results of the total contributions from ψ39–ψ43.Magnitudes of the contributions from and 180° , althougψ42 → ψ44 and ψ43 → ψ44 transitions at both ϕ = 0° and 180° which are shown in ψ42(HOMO-1), ψ43(HOMO), and ψ44(LUMO) are π(Se–Se), π*(Se–Se), and σ*(Se–Se), respectively, at ϕ = 0° and 180°. ψ42(HOMO-1) is essentially the same as ψ43(HOMO) at ϕ = 90°. ψ42 and ψ43 at ϕ = 90° are also drawn in ψ42 and ψ43 interconvert with each other. Contrary to the case of ϕ ≈ 0 and 180°, all of ψ39–ψ43 contribute to 1JPSO  at ϕ ≈ 90°. Contributions from the ψ42 → ψ44 and ψ43 → ψ44 transitions to 1JPSO  at 90° are almost cancelled by those from the ψ39 → ψ44, ψ40 → ψ44, and ψ41 → ψ44 transitions. In addition, both 1JSD  and 1JFC  substantially contribute at ϕ ≈ 90°. Consequently, it is difficult to specify a few orbitals, together with the transitions, which control 1J at ϕ ≈ 90°. The character of ψ44 [LUMO: σ*(Se–Se)] does not change so much depending on ϕ. Therefore, the behavior of ψ39–ψ43 must be mainly responsible for the ϕ dependence in 1J  and helps us to understand the mechanism, especially at ϕ = 0° and 180°.1JPSO , next extension is to clarify 1J on the basis of the MO theory.After elucidation of the mechanism for 1JTL  values, together with JPSO, 1JSD , 1JFC , and 1JSD+FC . Qn(Se))  into ψ67 [HOMO: π*(Se–Se)] and ψ66 [HOMO-1: π(Se–Se)] at the singlet state. The MO presentation in ψ42 → ψ44 and ψ43 → ψ44 transitions in 1JPSO  at ϕ = 0° in ψ67 (2a) and ψ66 (2a) contain the π(Nap) character. Large 1JPSO  and small 1Jobsd  are well understood by the ϕ dependence in the calculated 1J values.J) provide highly important information around coupled nuclei, containing strongly bonded and weakly interacting states. The 1J values are analyzed as the first step to investigate the nature of the bonded and nonbonded interactions between the Se atoms through nJ. QC calculations are necessary for the analysis and the interpretation of the J values with physical meanings. Calculated nJTL are composed of the contributions from nJDSO, nJPSO, nJSD, and nJFC. The decomposition helps us to consider the mechanisms of the spin-spin couplings, which are closely related to the electronic structures of compounds. Main contributions are evaluated separately from each ψi and each ψi → ψa transition, where ψi and ψa are occupied and unoccupied MO's, respectively.Nuclear spin-spin coupling constants  is calculated modeled by MeSeSeMe (1a), which shows the typical torsional angular dependence of ϕ(CMeSeSeCMe). The dependence explains well 1Jobsd  of small values for RSeSeR′ (1) and large values for 4-Y-1,8-Se2C10H5 (2) which correspond to symperiplanar diselenides. 1JTL  are confirmed to be controlled by Qn(Se). 1JTL  are demonstrated to be smaller when Qn(Se) becomes larger, experimentally and theoretically. The PSO terms contribute predominantly to 1J. The contributions are analyzed separately from each ψi and each ψi → ψa transition. The MO description of each transition enables us to recognize and visualize clearly the origin and the mechanisms of the indirect nuclear spin-spin couplings. Important properties of molecules, such as electronic structures, will be clarified by elucidating the mechanisms of the spin-spin couplings on the basis of the MO theory.


The second of two targets (T2) embedded in a rapid serial visual presentation					(RSVSVP) is often missed even though the first (T1) is correctly reported					. The rate of correct T2 identification is quite high,					however, when T2 comes immediately after T1 (lag-1 sparing). This study					investigated whether and how non-target items induce lag-1 sparing. One T1 and					two T2s comprising letters were inserted in distractors comprising symbols in					each of two synchronised RSVSVPs. A digit (dummy) was presented with T1 in					another stream. Lag-1 sparing occurred even at the location where the dummy was					present (Experiment 1). This distractor-induced sparing effect was also obtained					even when a Japanese katakana character (Experiment 2) was used as the dummy.					The sparing effect was, however, severely weakened when symbols (Experiment 3)					and Hebrew letters (Experiment 4) served as the dummy. Our findings suggest a					tentative hypothesis that attentional set for item nameability is					meta-categorically created and adopted to the dummy only when the dummy is					nameable. That is, only an item that matches the attentional set can be				processed further. In the TLC model, it is assumed that the observers initially				adopt the attentional set for a target category in an endogenous manner. The				attentional set requires periodic maintenance signals from the central executive in				the higher brain regions . That is, one stream had T1 and T2, and the				other had the dummy T1 and T2. In this situation, the dummy T1 category should be				configured as one of the distractor categories. Moreover, previous studies suggested				that lag-1 sparing did not occur when T1 and T2 locations were different 						with a resolution of 1024 × 768 pixels and a vertical refresh rate						of 75 Hz. A viewing distance of 60 cm was maintained with a head-and-chin						rest. A PC/AT-compatible computer controlled the presentation of stimuli and						collection of data. Stimuli and experiments were programmed in Delphi 6						(Borland Software Corporation). In every trial, from a set of letters of the						English alphabet excluding “I”,						“O”, “Q”, and						“Z”, three uppercase letters, all different, were						randomly chosen as targets. Ten keyboard symbols served as distractors						. The dummy T1 was						an Arabic digit. Each item subtended a visual angle of around 1° in						height. The luminance of these items was 2.5 cd/m2 against a background with						a luminance of 98.5 cd/m2. The stimulus display comprised a fixation cross						at the centre of the screen and two synchronised RSVP streams to the left						and right of the fixation cross. T1 was one of the three targets that						appeared in one of the two streams, and the T2s of the remaining two targets						were simultaneously presented in both streams. The dummy T1 was presented						simultaneously with T1 but in another stream. In a trial in which the dummy						T1 was absent, a distractor item  was inserted instead. The						dummy presence/absence was equally probable. The centre-to-centre distance						between the two streams subtended a visual angle of 3.4°.The observers were individually tested in a dark room. F = 14.3, MSE = 226.8,						p < .005, and Lag, F = 16.3,						MSE = 97.0, p < .0001. It also					revealed significant interactions between Dummy and Lag, F = 2.8, MSE = 49.91, p = .04, between T2					location and Lag, F = 7.5, MSE = 68.77,						p = .0002, and among the three factors,					F = 2.7, MSE = 83.18,						p = .04. The main effect of Dummy, however, was not					significant, F = 0.6, p = .46 Moreover,					the interaction between Dummy and T2 location was not significant,						F = 0.6, p = .48 The tests of the					simple effects, based on the significant interaction among the three factors,					revealed significant simple-simple main effects of Lag in the present-consistent					condition, F = 8.3, p < .0001,					present-inconsistent condition, F = 7.2,						p < .0001, absent-consistent condition,						F = 8.8, p < .0001, and					absent-inconsistent condition, F = 8.7,						p < .0001. Moreover, a simple-simple main effect of					Dummy was found in the inconsistent condition at lag 1, F = 10.0, p = .002.A three-way analysis of variance (ANOVA) on T2 performance with three					within-subject factors showed significant main effects of T2 location,						t(144) = 4.52, p < .0001;						t(144) = 4.23, p < .0001;						t(144) = 5.05, p < .0001,					respectively. The identification rate of T2 at lag 1, however, was not					significantly different from that at lag 2 in the absent-inconsistent condition,						t(144) = 0.72, p = .47.Multiple comparisons using Ryan’s method ,2 based t-test					revealed that the correct rate for T1 identification was significantly lower					when T2 appeared at lag 1 in the present condition than when it appeared in the					absent condition, t(9) = 3.04, p = .01.Additionally, the correct rate for T1 identification was analysed to confirm					competition between T1 and the dummy item. A two-tailed In Experiment 1, we found that lag-1 sparing was observed for both T2s					concurrently. Specifically, lag-1 sparing occurred at a location different from					the T1 location only when the dummy T1 was presented. On the other hand, at the					T1 location, robust lag-1 sparing occurred regardless of the presence/absence of					the dummy item. At the location different from the T1 location, lag-1 sparing					did not occur when the dummy T1 was not presented, consistent with previous					studies showing that lag-1 sparing occurred only when a common location was					shared by T1 and T2 . The comAdditionally, performance at a location different from the T1 location was					severely impaired at lag 1 when the dummy item was absent, even though					performance at the T1 location was quite high. These results support the notion					that the T2 item in each stream was processed as a part of a discrete					attentional episode established at each stimulus location. That is, each stream					of RSVP seems to be filtered by an attentional set that can be split into					multiple locations and works independently .Not all the results of this experiment, however, can be explained by					multidimensional attentional setting  based onOne might argue that the results of Experiment 1 reflect the adoption of an					attentional set configured for an alphanumeric category, which is a					meta-category of letters and digits. That is, it was possible that the observers					in Experiment 1 adopted the attentional set that corresponds to a category					including both letters and digits all together, namely, alpha-numerals. Thus, an					alphanumeric attentional set might be applied to both the dummy and T2,					resulting in conventional lag-1 sparing. The next experiment examined this					possibility by introducing a new category, which is not included in the					alphanumeric category, as a dummy category.This experiment aimed at testing whether the adoption of an alphanumeric attentional				setting produced lag-1 sparing as in the first experiment. In Experiment 2, a new				category, Japanese katakana, was used as the category of the dummy T1. This category				was quite familiar to the Japanese observers employed in this experiment and was not				included in the alphanumeric category. If the results of Experiment 1 were a product				of alphanumeric attentional setting, lag-1 sparing should not occur even when the				dummy T1 of Japanese katakana was presented.Eleven Japanese students from Kyushu University, including one of the authors						(Y.Y.), participated in this experiment. Except for Y.Y., they were unaware						of the purpose of the experiment. All of them reported normal or						corrected-to-normal eyesight.The apparatus, stimuli, and procedure were identical to those in Experiment 1						except that, instead of digits, 10 Japanese katakana characters,						“ア” (a),						“イ” (i),						“ウ” (u),						“エ” (e),						“オ” (o),						“カ” (ka),						“キ” (ki),						“ク” (ku),						“ケ” (ke), and						“コ” (ko), were introduced as dummies. The						observers were asked to ignore Japanese katakana.F = 11.5, MSE =					143.03, p = .007, T2 location, F =					10.0, MSE = 276.52, p = .01, and Lag,						F = 11.3, MSE = 327.02,						p < .0001. Significant interactions between Dummy					and Lag, F = 4.4, MSE = 161.61,						p = .005, between T2 location and Lag,					F = 6.2, MSE = 205.49,						p = .0006, and among the three factors,					F = 2.9, MSE = 114.42, p = .03, were					obtained. The interaction between Dummy and T2 location, F = 0.1, p = .80, was not significant. Tests of the simple					effects, based on significant interactions among the three factors, revealed					significant simple-simple main effects of Lag in the present-consistent					condition, F = 12.7, p < .0001,					present-inconsistent condition, F = 6.4,						p = .0001, and absent-consistent condition,						F = 10.2, p < .0001, but					not in the absent-inconsistent condition, F = .40,						p = .81. Moreover, simple-simple main effects of Dummy were					found in the inconsistent condition at lag 1, F = 6.6,						p = .01, and at lag 3, F = 10.9,						p = .001.t(160) = 5.40, p					< .0001, present-inconsistent condition, t(160) = 3.12,						p = .002, and absent-consistent condition,						t(160) = 3.64, p = .0004. A post hoc						t-test did not reveal a significant difference between the					performances at lag 1 and lag 2 in the absent-inconsistent condition,						t(10) = 0.44, p = .67.Multiple comparison tests using Ryan’s method, based on the					simple-simple main effect of Lag, indicated that the correct identification rate					of T2 at lag 1 was significantly higher than that at lag 2 in the					present-consistent condition, t-test did not reveal any difference between the					correct identification rates of T1 when T2 appeared at lag 1 in the present					condition and when T2 appeared in the absent condition, t(10) =					0.13, p = .90.A two-tailed In this experiment, as well as in Experiment 1, lag-1 sparing with Japanese					katakana as the dummy T1 was clearly observed. The involvement of an					alphanumeric attentional setting for both letters and digits can still explain					lag-1 sparing observed in Experiment 1, but cannot explain the results of					Experiment 2.Why did lag-1 sparing occur even though no attentional set was configured for the					dummy T1? As a straightforward interpretation suggests, it is likely that the					dummy T1 erroneously served as the actual T1, leading to the lag-1 sparing of					the trailing T2. In this interpretation, an attentional set for targets or an					attentional set for distractors, related to active selection or active					rejection, would be involved in this erroneous selection. The cognitive system					seemed mistakenly to select the dummy T1 because the dummy category (digits or					Japanese katakana) was similar to the target category (letters) or because the					dummy category was different from the distractor category (symbols) that made up					the majority of RSVP streams and consequently was not rejected.This experiment examined whether a dummy item belonging to a category which was				simply different from a distractor category led to lag-1 sparing. In Experiment 3,				the categories of dummy items and distractors used in Experiment 1 were reversed				. Despite				the categorical reversal, the dummy and target categories were still clearly				separated although the difference between the dummy and distractor categories				remained unchanged from that in Experiment 1. Lag-1 sparing would occur when a dummy				symbol item was presented if mere categorical difference between the dummy and				distractor was the decisive factor.Fourteen students from Kyushu University participated, and none of the						students were aware of the purpose of the experiment. All reported normal or						corrected-to-normal eyesight.The fundamental aspects of the apparatus, stimuli, and procedure were						identical to those in Experiment 1, with the following exceptions: The dummy						T1 category was changed to symbols, and the category of the distractors was						changed to digits. The observers were asked to ignore the symbols.F = 16.9,						MSE = 405.87, p = .001, and Lag,						F = 7.2, MSE = 226.64,						p = .0001. It also revealed significant interactions					between T2 location and Lag, F = 28.0,						MSE = 137.69, p < .0001, and among					the three factors, F = 3.9, MSE = 87.7,						p = .007. The main effect of Dummy was not significant,						F = 0.4, p = .53. Moreover, the interactions between					Dummy and T2 location, F = 0.02, p =					.90, and between Dummy and Lag, F = 1.2,						p = .33, were not significant. Tests of the simple effects,					based on the significant interaction among the three factors, revealed					significant simple-simple main effects of Lag in the present-consistent					condition, F = 10.9, p < .0001,					present-inconsistent condition, F = 4.5,						p = .002, absent-consistent condition,					F = 13.3, p < .0001, and					absent-inconsistent condition, F = 15.8,						p < .0001. Moreover, simple-simple main effects of					Dummy were found in the inconsistent condition at lag 1, F = 4.2, p = .04, and lag 5, F =					4.5, p = .04.t(208) = 4.15, p < .0001,					and the absent-consistent condition, t(208) = 5.75,						p < .0001. The difference between the correct					identification rate of T2 at lag 1 and at lag 2, however, did not reach					significance in the present-inconsistent condition, t(208) =					0.70, p = .49, or absent-inconsistent condition,						t(208) = 0.26, p = .79. That is, lag-1					sparing was not observed in these inconsistent conditions.Multiple comparisons using Ryan’s method, based on the simple-simple					main effect of Lag, indicated that the correct identification rate of T2 at lag					1 was significantly higher than that at lag 2 in the present-consistent					condition, t-test did not reveal any difference between the					correct identification rates of T1 when T2 appeared at lag 1 in the present					condition and when T2 appeared in the absent condition, t(13) =					0.14, p = .89.A two-tailed In this experiment, lag-1 sparing was attenuated even when the dummy T1 was					present. Specifically, although T2 performance at lag 1 was not significantly					higher than that at lag 2 in the inconsistent condition, T2 performance at lag 1					in the Dummy-present condition was higher than in the Dummy-absent condition.					Moreover, the analysis of T1 performance suggests that the dummy T1 symbol did					not impair the performance for the actual T1. These results suggest that lag-1					sparing largely depends on potential common properties between the dummy and					target categories rather than on the categorical difference between the dummy					and distractor categories.What then was the common property of the dummy and target categories producing					lag-1 sparing in this study? A tentative answer to this question is the					nameability of items. Names of items were different among the categories used in					the previous experiments. For example, an item belonging to letter, digit, and					Japanese katakana categories is easy to name. Such easy-to-name dummy items					might have produced lag-1 sparing in Experiments 1 and 2. Symbols such as					“$”, “#”, and					“!”, however, are difficult to name. The difficult-to-name					dummy items might not have produced lag-1 sparing in Experiment 3. If					easy-to-name items were preferentially treated by the cognitive system, the					attentional set would be erroneously adopted for such items, resulting in lag-1					sparing.Experiment 4 was performed to determine whether lag-1 sparing with the dummy items				depended on item nameability. We used Hebrew alphabet letters as dummy categories,				Roman alphabet letters and symbols as target categories, and digits as the				distractor category. Data were collected from Japanese students who knew the shape				of Hebrew alphabet letters, but could not name an individual letter. If item				nameability underlies dummy-driven lag-1 sparing, no lag-1 sparing with the dummy				item from Hebrew letters would be observed because Japanese participants could not				name them.Twelve Japanese adults participated in this experiment. All of them were						unaware of the purpose of the experiment and reported normal or						corrected-to-normal eyesight.This experiment was similar to Experiment 1 except for the following. First,						categories of targets, dummies, and distractors were changed. Ten Roman						alphabet letters (“A” to “K”						excluding “I”) or 10 symbols used in the previous						experiments were employed as the targets. Digits served as the distractors.						Ten Hebrew alphabet letters, “א” ,						“ב” (bet),						“ג” (gimel),						“ד” ,						“ה” (he),						“ז” (zayin),						“ח” (chet),						“ט” (tet),						“ל” (lamed), and						“ש” (shin), were introduced as dummies. A						pre-experiment questionnaire revealed that none of the observers knew Hebrew						at all, and the observers were asked to ignore the Hebrew letters within the						RSVP streams. Second, only lags 1, 2, and 5 were used. Thus, each observer						performed 120 trials with two experimental blocks including two						target-category conditions . Each block contained						2 dummy conditions (present or absent) × 2 T1 location conditions						(right or left) × 3 lag conditions  × 5						replications. In each block, the trial order was randomised. The order of						the blocks was counterbalanced across observers.F = 4.3,							MSE = 383.17, p = .03. It also						revealed significant interactions between Dummy and T2 location,							F = 14.3, MSE = = 402.29,							p = .003, between T2 location and Lag,							F = 9.3, MSE = 382.93,							p = .001, and among the three factors,							F = 4.1, MSE = 227.80,							p = .03. The main effects of Dummy,						F = 4.7, p = .05, and T2 location,							F = 3.5, p = .09, were						marginally significant. An interaction between Dummy and Lag,							F = 0.2, p = .79, was not						significant. Tests of simple effects based on the interaction between Dummy						and T2 location revealed a significant simple main effect of Dummy in the						consistent condition, F = 18.8, p =						.0003. Tests of the simple effects based on the significant interaction						among the three factors revealed significant simple-simple main effects of						Lag in the present-consistent condition, F = 3.2,							p = .05, absent-consistent condition,							F = 8.3, p = .0005, and						absent-inconsistent condition, F = 4.6,							p = .01, but not in the present-inconsistent condition,							F = 1.4, p = .26. Multiple						comparisons using Ryan’s method, based on the simple-simple main						effect of Lag, indicated that the correct identification rate of T2 at lag 1						was no different from that at lag 2 in the absent-inconsistent condition,							t(88) = 1.00, p = .32. A post hoc							t-test did not reveal a significant difference in the						performance at lag 1 and lag 2 in the present-inconsistent condition,							t(11) = 1.20, p = .25. Moreover, a						significant simple-simple main effect of Dummy was acknowledged in the						inconsistent condition at lag 1, F = 7.8,							p = .007. Additionally, a t-test						revealed that T1 performance was significantly lower when T2 appeared in the						present-consistent condition than when it appeared in the absent-consistent						condition, t(11) = 2.32, p = .04.A three-way ANOVA on T2 performance with three within-subject factors 						showed a significant main effect of Lag, F = 0.02, p = .98, separate						one-way ANOVAs on T2 performance with Lag as a factor were performed. As a						result, significant main effects in the present-consistent condition,							F = 3.7, p = .04, and						absent-consistent condition, F = 4.4,							p = .02, were found. The main effects, however, in the						absent-inconsistent condition, F = 0.1,							p = .93, and present-inconsistent condition,							F = 1.0, p = .38, were not						significant. Post hoc t-tests did not reveal a significant						difference in the performance at lag 1 and lag 2 in the present-inconsistent						condition, t(11) = 0.33, p = .75, and						absent-inconsistent condition, t(11) = 0.73,							p = .48. Moreover, the difference in the performance at						lag 1 between the present and absent conditions was not significant,							t(11) = 1.17, p = .27. Furthermore, T2						performance averaged across lags in the absent-consistent condition was						marginally significantly higher than that in the present-consistent						condition, t(11) = 2.16, p = .05.						Additionally, a t-test revealed that T1 performance was						significantly lower when T2 appeared in the present-consistent condition						than when it appeared in the absent-consistent condition,						t(11) = 2.66, p = .02.Because a three-way ANOVA on T2 performance with three within-subject factors						did not show a significant interaction among the three factors,							The results showed that lag-1 sparing with the dummy item was weakened in the					Roman alphabet condition and disappeared in the symbol condition when a Hebrew					alphabet letter, which was not nameable by the Japanese observers who					participated in this experiment, was employed as the dummy item. The results are					consistent with the prediction that item nameability strongly influences					dummy-driven lag-1 sparing. This idea is compatible with the present results in					that weak or no sparing effect was found in this experiment because Hebrew and					symbols were not nameable. Moreover, the results in the symbol condition suggest					that a mere categorical difference between the dummy and distractor categories					does not determine lag-1 sparing.An unexpected finding in this experiment was that T2 performance in the stream					consistent with the actual T1 dropped when the dummy item was presented. This					was not a general tendency in the previous experiments in this study. Hence,					this finding seems to be a stimulus-specific one. In Experiment 4, Hebrew					alphabet characters were employed as dummy items, and Japanese observers did not					know these characters. We surmise that quite unfamiliar items like Hebrew					characters increased overall processing cost, affecting processing of the actual					T1 and trailing T2. This is beyond the scope of the present study, but we may					examine this issue in future research.The present study found that a non-target item in neither a target nor distractor				category can elicit lag-1 sparing. Experiment 1 showed that a dummy T1 (digits) not				belonging to a target category  produced lag-1 sparing. Moreover, in				Experiment 2, it was demonstrated that a dummy item from Japanese katakana caused				lag-1 sparing for the following T2 of Roman alphabet letters, suggesting that				dummy-based lag-1 sparing occurs beyond an alphanumeric attentional setting.				Additionally, Experiment 3 showed that a dummy item from symbols did not cause				robust lag-1 sparing, suggesting that the mere presence of the dummy item at the				temporal location of T1 does not explain dummy-based lag-1 sparing. Finally, the				results of Experiment 4 suggest that nameability of the dummy item was related to				dummy-driven lag-1 sparing. Categories such as the Roman alphabet, Japanese katakana				letters, and digits were nameable whereas symbols and the Hebrew alphabet were not				nameable by observers in the present experiments. Our findings suggest that				attentional set for item nameability is meta-categorically created and adopted to				the dummy T1 only when the dummy T1 is nameable. The idea of a meta-categorical				attentional set for nameability is consistent with almost all the results in this				study.How can the cognitive system differentiate target from distractors if the				meta-categorical attentional set is actually adopted? Simple assumptions about the				meta-categorical attentional setting for nameability cannot explain why in				Experiment 4 the observers could differentiate letter targets from the digit				distractors. Here we assume two-stage filtering, as illustrated in The results of a previous study  might alSD = 115.8), 421.7 (SD = 100.5), 545.9					(SD = 105.0), 711.5 (SD = 98.8), and 479.9					(SD = 388.2), respectively. The results of statistical					comparisonsOne might argue that the present results stemmed from an artefact involving a				low-level visual feature of the stimuli used in the experiments. Maki, Bussard,				Lopez, and Digby  showed t				SD = 88.2, resAn account based on feature dissimilarity between the dummy and distractors may,				however, explain the present results that cannot be explained by item nameability.				These results were the higher T2 performance at lag 1 in the dummy-present condition				compared with that in the dummy-absent condition in Experiment 3 and the similar				difference in the Roman alphabet condition of Experiment 4. Maki et al.  showed tOther than the filtering dependent on attentional set, an attentional mechanism may				explain the lag-1 sparing with the dummy item. In a previous study, Potter et al.					 suggesteA meta-categorical setting is not an irrational idea. Previous studies have suggested				that the character style or the type style is also the subject of an attentional				set. For example, reported findings show that an attentional set was adopted for				uppercase words inserted in the RSVP of lowercase words . Additio


Throughout the captions of Figure S2 and Figure S3 all instances of “sigma_syn/sigma_ant” should read “N_ant_double/N_syn_double.”


Recently, in the compressed sensing framework we found that a two-dimensional interior region-of-interest (ROI) can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant . Here we present a general theorem charactering a minimization property for a piecewise constant function defined on a domain in any dimension. Our major mathematical tool to prove this result is functional analysis without involving the Dirac delta function, which was heuristically used by Yu and Wang (2009). While in general an interior region-of-interest (ROI) cannot be uniquely reconstructed from projection data only associated with lines through the ROI , 2, in td > 0, let Ω ⊂ ℝd be a d-dimensional open bounded set and denote its boundary by ∂Ω. Then the space of functions of bounded variation isυ||L1(Ω) is the integral of |υ(x)| over Ω, andυ ∈ BV(Ω). Here C01(Ω) is the space of continuously differentiable functions that vanish on ∂Ω, and for ϕ = T ∈ C01(Ω)d, div ϕ = ∑i = 1d∂ϕi/∂xi. The Sobolev spaceFor piecewise constant or piecewise smooth functions, it is natural to use the space of functions of bounded variation  to captuC1 boundary, and it is decomposed into a union of a finite number of subsets with disjoint interiorsm has a piecewise C1 boundary. The unit outward normal vector on ∂Ωm is denoted by υm. Denote i and j between 1 and M. We write meas(Γi j) for the (d − 1)-dimensional measure of Γi j; it is the area of Γi j for d = 3, and the length of Γi j for d = 2. The symbol ∑i<j will refer to a summation for those i and j with a nonempty Γi j in the range 1 ≤ i < j ≤ M. The main result of this note is the following. We that assume Ω has a piecewise f be a piecewise constant function corresponding to the decomposition (Let position :f(x) = cm ∈ ℝ for x ∈ Ωm, 1 ≤ m ≤ M. Then we haveConsequently, we have the minimization propertyϕ ∈ C01(Ω)d,g(x) = 0 in ∫Ω|∫Ω|ϕ ∈ C) = 0 in  follows ε > 0, define two open subsetsx, D) = min {|x − y| : y ∈ D} is the distance between x and a closed set D. Obviously, for some constant c > 0,Bδ is the ball of radius δ centered at the origin, ηδ(x) = η(x/δ)/δd, andϕε,δ(x)| ≤1 for x ∈ Ω, and for δ sufficiently small, ϕε,δ(x) ∈ C0∞(Ω)d. Moreover, as δ → 0, ϕε,δ(x) converges uniformly to ψε(x) for x ∈ Ω\(Ωε/2∂ ⋃ Ωε/2Γ). Thus from ∫Ω|ombining  and 21)g∈C1(Ω̅).d, and1η(x)={c0eg ∈ W1,1(Ω), we use the density ofW1,1(Ω) [n → ∞ in . For W1,1(Ω)  and choo∞.Since  defines n → ∞ in  for gn⊂Ce obtain  for g ∈ Br0 ⊂ ℝ2 be a disk of radius r0 centered at the origin. Consider a piecewise constant, radial function f(r) defined on Br0 such that it has a jump jm ∈ ℝ at rm, 1 ≤ m ≤ M, where 0 < r1 < ⋯ < rM < r0. Then by  to achieve specific properties for satisfactory recovery of an underlying signal, rather than imposed by a specific detector arrangement as in limited data tomography. However, in a broad sense, compressed sensing can be interpreted as achieving better reconstruction from less data relative to the common practice. Hence, while the current name is not far off, an alternative phrase for our approach can be “total variation minimization-based interior tomography.” In conclusion, we have extended the total variation minimization property of a piecewise function from two-dimensions to any dimensionality in the Sobolev space, which can be used for exact reconstruction of any piecewise function on an ROI by minimizing its total variation under the constraint of the truncated projection data through the ROI. Previously, we implemented an alternating iterative reconstruction algorithm to minimize the total variation, which is time-consuming and needs improvement. Under the guidance of the theoretical finding presented here, we are working to develop a multidimensional ROI reconstruction algorithm for better performance. Clearly, major efforts are still needed in this direction.


Transcriptional and postranslational regulation of the cell cycle has been widely studied. However, there is scarce knowledge concerning translational control of this process. Several mammalian eukaryotic initiation factors (eIFs) seem to be implicated in controlling cell proliferation. In this work, we investigated if the human eIF3f expression and function is cell cycle related.The human eIF3f expression has been found to be upregulated in growth-stimulated A549 cells and downregulated in G0. Western blot analysis and eIF3f promotor-luciferase fusions revealed that eIF3f expression peaks twice in the cell cycle: in the S and the M phases. Deregulation of eIF3f expression negatively affects cell viability and induces apoptosis.The expression pattern of human eIF3f during the cell cycle confirms that this gene is cell division related. The fact that eIF3f expression peaks in two cell cycle phases raises the possibility that this gene may exert a differential function in the S and M phases. Our results strongly suggest that eIF3f is essential for cell proliferation. Saccharomyces cerevisiae, and the highest in humans (thirteen subunits from eIF3a to eIF3m) , mapped on chromosome 11p15.4, we used the 11:7947867-11:7948866 fragment to generate a plasmid, called pF11P1K, that encompasses 1 kb of genomic DNA corresponding to the promoter . For the intronless copy [NCBI removed record XM_290345], mapped on chromosome 2p16.1, we used the 2:58331072-2:58332078 fragment to generate a plasmid, called pF02P1K, that also encompasses 1 kb of genomic DNA corresponding to the promoter region of the eIF3f intronless copy . The pMSG plasmid with the leaky inducible (dexamethasone) MMTV LTR promoter was used to generate eIF3f gene expression vector, called pFH11S, and eIF3f antisense vector, called pFH11A. Total RNA was extracted from A549 cells with TRIzol, according to the manufacturer's instructions. eIF3f gene sequence [NCBI Entrez Gene 86652: Locus NP_003745] was amplified by RT-PCR using forward primer TTCTCGACAAGATGGCCACACCGGCG and reverse primer TCACAGGTTTACAAGTTTTTCATTGAG. Amplification product was subcloned in the pBluescript SK(-) vector (plasmid construct pSK11F) and orientated into the pMSG plasmid to generate the sense and antisense constructs. All constructs were verified by sequencing . The verified antisense targeting sequence of eIF3f is 5'---ttctcgacaagATGgccacaccggcggtaccagtaagtgctcctccggccacgccaaccccagtcccggcggcggccccagcctcagttccagcgccaacgccagcaccggctgcggctccggttcccgctgcggctccagcctcatcctcagaccctgcggcagcagcggctgcaactgcggctcctggccagaccccggcctcagcgcaagctccagcgcagaccccagcgcccgctctgcctggtcctgctcttccagggcccttccccggcggccgcgtggtcaggctgcacccagtcattttggcctccattgtggacagctacgagagacgcaacgagggtgctgcccgagttatcgggaccctgttgggaactgtcgacaaacactcagtggaggtcaccaattgcttttcagtgccgcacaatgagtcagaagatgaagtggctgttgacatggaatttgctaagaatatgtatgaactgcataaaaaagtttctccaaatgagctcatcctgggctggtacgctacgggccatgacatcacagagcactctgtgctgatccacgagtactacagccgagaggcccccaaccccatccacctcactgtggacacaagtctccagaacggccgcatgagcatcaaagcctacgtcagcactttaatgggagtccctgggaggaccatgggagtgatgttcacgcctctgacagtgaaatacgcgtactacgacactgaacgcatcggagttgacctgatcatgaagacctgctttagccccaacagagtgattggactctcaagtgacttgcagcaagtaggaggggcatcagctcgcatccaggatgccctgagtacagtgttgcaatatgcagaggatgtactgtctggaaaggtgtcagctgacaatactgtgggccgcttcctgatgagcctggttaaccaagtaccgaaaatagttcccgatgactttgagaccatgctcaacagcaacatcaatgaccttttgatggtgacctacctggccaacctcacacagtcacagattgcactcaatgaaaaacttgtaaacctgTGA---3'.et al. [Design and production of siRNAs against eIF3f was carried out according to Kittler et al. . Brieflyet al.  to selecctccagaacggccgcatgagcatcaaagcctacgtcagcactttaatgggagtccctgggaggaccatgggagtgatgttcacgcctctgacagtgaaatacgcgtactacgacactgaacgcatcggagttgacctgatcatgaagacctgctttagccccaacagagtgattggactctcaagtgacttgcagcaagtaggaggggcatcagctcgcatccaggatgccctgagtacagtgttgcaatatgcagaggatgtactgtctggaaaggtgtcagctgacaatactgtgggccgcttcctgatgagcctggttaaccaagtaccgaaaatagttcccgatgactttgagaccatgctcaacagcaacatcaatgaccttttgatggtgacctacctg---3'.in vitro transcription of the eIF3f fragment sequence with T7 and T3 RNA polymerases. siRNAs against eIF3f were obtained by digestion of dsRNA with RNase III, generating a mixture of siRNAs from 25 to 30 pb. Scrambled siRNAs were obtained by in vitro transcription of the MCS of vector pSL301 with SP6 and T7 RNA polymerases and further digestion with RNase III. siRNAs were cleaned with Q-sepharose columns.The amplified sequence was cloned into the pGEM plasmid to generate the pF11SIR vector. dsRNA were obtained by Cells were accumulated in G0 by serum deprivation (0.1% FBS) for 48 hours . SynchroTotal RNA was extracted from A549 cells with TRIzol, according to the manufacturer's instructions. The resulting RNA pellet was air-dried, resuspended in nuclease-free water, and treated with amplification-grade DNase I. RNA was quantified using a spectrophotometer ; A260/280 readings between 1.8 and 2.0 were used to ensure purity. Total RNA was also quantified with a VersaFluor fluorometer  using Quant-iT™ RiboGreen RNA Assay Kit, according to the manufacturer's instructions. Agarose/formaldehyde denaturing gel electrophoresis, as described by Sambrook and Russel , was per4, 4 mM EGTA, 1% Triton X-100 and 1 mM DTT) containing a protease inhibitor mix. 15 μg of protein were separated per lane by 10% SDS-PAGE gel electrophoresis. After electrophoretic transfer of proteins to a polyvinylidene difluoride (PVDF) membrane, eIF3f, Cyclin B1 or caspase-9 specific bands were detected by reaction with a rabbit antibody against eIF3f, a mouse antibody against caspase-9, followed by horseradish peroxidase-conjugated anti-rabbit or anti-mouse secondary antibody, respectively. Bands were visualized by enhanced chemiluminescence (ECL). Immunoblots were stripped using mild antibody stripping solution and re-probed with a mouse anti-tubulin antibody. For quantification purposes, densitometric measurements were performed using the Kodak Digital Science Electrophoresis Documentation and Analysis System 100 for Windows . All eIF3f values were normalized to tubulin levels.For Western blot analysis, cells were scraped from dishes and cellular protein extracts were prepared by homogenization in ice cold lysis buffer  was 2 μg, and 3 μg for the siRNAs. Transfection times varied according to each experiment. Cells were harvested 8, 24 and 48 hours after transient transfection with siRNAs, eIF3f sense and antisense constructs, and control plasmids. For transfections with pMSG and derived constructs, 0.1 mM dexamethasone was added to the culture medium. For luciferase activity assays with cells in G1, cell cultures were transiently transfected with the promoter plasmid constructs for 20 hours, then arrested in M phase with nocodazole for 24 hours, and finally released from nocodazole to harvest cells 4 hours later (G1 cells). For luciferase activity assays with cells in M or S, first cell cultures were transiently transfected with the promoter plasmid constructs for 24 hours, then these cultures were arrested with nocodazole or hydroxyurea for 24 hours, to finally harvest transfected cells in M or S phase, respectively. To determine luciferase activity in G0, cell cultures were transiently transfected with the promoter plasmid constructs for 6 hours, and then cultures were accumulated in G0 by serum deprivation for 48 hours after which transfected cells were harvested for luciferase activity assays.4, 4 mM EGTA, 1% Triton X-100 and 1 mM DTT). Each clarified cell lysate was first assayed for total protein, using the Bio-Rad Protein Assay, according to the manufacture's instructions . After adjusting samples for equivalent protein concentration in lysis buffer, samples were assayed for luciferase activity as described by Sambrook and Russel [Promoter activity was determined by measuring the luciferase activities of cell lysates 48 hours after transfection with empty pGL3, pF11P1K or pF02P1K plasmids. Briefly, cells were lysed in ice cold lysis buffer  and collected. Floating cells were also recovered and included with the formerly adherent cells for cell viability with the trypan blue exclusion method. Percent of cell viability was determined microscopically  by counting triplicate samples of 200 to 400 cells in a hemotocytometer chamber.Adherent cells were washed twice with PBS and stained with a mixture of 4 μg/ml acridine orange and 4 μg/ml ethidium bromide in PBS  for 5 miAll experiments were repeated at least three times. Results of multiple experiments are expressed as mean ± standard error (SE). Analysis of Student's t test was used to assess the differences between means. A P < 0.05, with respect to control experiments, was accepted as statistically significant.The authors declare that they have no competing interests.AEHM participated in the design of the study, carried out the molecular genetic and cellular studies, performed the statistical analysis and drafted the manuscript. MAPG conceived the study and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.We would like to thank Y.E. Guzmán-Infante, A. Mendoza-Pineda, D.A. Bolaños-Cornejo and A. Aguilera-Méndez for technical assistance. We would also like to thank S. Zárate for critically reading the manuscript. A.E. Higareda-Mendoza was a recipient of a CONACyT Doctoral Scholarship. This research was supported by CIC Research Programs 2.15 and 16.3 from the Universidad Michoacana de San Nicolás de Hidalgo, and the Mixed Fund CONACyT-Goverment of the State of Michoacan MICH-2009-C05-116168.Original Western blots for the data used in Figure Click here for fileOriginal Western blots for the data used in Figure Click here for fileOriginal Western blots for the eIF3f and tubulin data used in Figure Click here for fileOriginal Western blots for the Cyclin B1 data used in Figure Click here for fileOriginal Northern blots for the data used in Figure Click here for fileOriginal Northern blots for the data used in Figure Click here for fileOriginal Western blots for the data used in Figure Click here for fileAmplified micrographs for the data used in Figure Click here for fileOriginal Western blots for the data used in Figure 6Aand Click here for fileAmplified micrographs for the data used in Figure Click here for file


Even though the commonality of the observed genomic rates remains unexplained, it implies that mutation rates in unstressed microbes reach values that can be finely tuned by evolution. An insight originating in the 1920s and maturing in the 1960s proposed that the genomic mutation rate would reflect a balance between the deleterious effect of the average mutation and the cost of further reducing the mutation rate. If this view is correct, then increasing the deleterious impact of the average mutation should be countered by reducing the genomic mutation rate. It is a common observation that many neutral or nearly neutral mutations become strongly deleterious at higher temperatures, in which case they are called temperature-sensitive mutations. Recently, the kinds and rates of spontaneous mutations were described for two microbial thermophiles, a bacterium and an archaeon. Using an updated method to extrapolate from mutation-reporter genes to whole genomes reveals that the rate of base substitutions is substantially lower in these two thermophiles than in mesophiles. This result provides the first experimental support for the concept of an evolved balance between the total genomic impact of mutations and the cost of further reducing the basal mutation rate.Rates of spontaneous mutation have been estimated under optimal growth conditions for a variety of DNA-based microbes, including viruses, bacteria, and eukaryotes. When expressed as genomic mutation rates, most of the values were in the vicinity of 0.003–0.004 with a range of less than two-fold. Because the genome sizes varied by roughly 10 Spontaneous mutations are key drivers of evolution and disease. In microbes, most mutations are deleterious, some are neutral (without significant impact), and a few are advantageous. Because deleterious mutations reduce fitness, there should be constant selection for antimutator mutations that reduce rates of spontaneous mutation. However, such reductions are necessarily achieved at some cost. Therefore, a mutation rate should converge evolutionarily on a value that reflects this trade-off. For DNA microbes, the observed genomic mutation rate is remarkably (and mysteriously) invariant, in the neighborhood of 0.003–0.004, with a range of less than two-fold despite huge variation per average base pair in organisms with a wide diversity of life histories. Would an environmental condition that increased the average deleterious impact of a mutation be balanced by additional investments in antimutator mutations? It is widely observed that many mutations with mild impacts become strongly deleterious at higher temperatures, so mutation rates were measured in two thermophiles, a bacterium and an archaeon. Remarkably, both displayed average mutation rates reduced by about five-fold from the characteristic mesophilic value, most of the decrease reflecting a 10-fold reduction in the rate of base substitutions. It has become increasingly clear that the basal rate of spontaneous mutation per genome per replication is remarkably invariant in DNA microbes: using a classical correction factor for estimating the ratio of all base-pair substitutions (BPSs) to detected base-pair substitutions, genomic mutation rates (mutations per genome per replication) vary by less than twofold while genome sizes vary by ≈6,000-fold . Thus, whypothesis of dangerous missense). This simple prediction was supported by the observation that missense mutations accumulated to a lesser extent (compared to synonymous mutations) in thermophiles than in mesophiles during the course of molecular evolution , implying stronger purifying selection in thermophiles If the Kimura conjecture is correct, then increasing the average deleterious impact of a spontaneous mutation  would lower the rate of mutation, at least on an evolutionary time scale. The concept of an equilibrium basal mutation rate is difficult to test in a laboratory context because any imposed resetting of the equilibrium would probably require numbers of generations large even by microbial standards, and is difficult to test convincingly because only one or a few habitats could be explored. However, it has recently proven possible to test the concept by examining a natural evolutionary experiment, life at high temperatures. Those who gather mutants for fun or profit have often observed that the most common class of mutations is to temperature sensitivity, indicating that many missense mutations are well tolerated at the standard growth temperature but become much more deleterious, often to the point of lethality, at a temperature only 5°C–10°C higher. This widespread anecdotal observation implies that macromolecular stability becomes increasingly dependent on structural integrity as temperatures rise, a reasonable conjecture in keeping with the considerable constraints observed in the proteins of thermophilic microbes e.g., . It is tThe first phase of determining genomic mutation rates involves measuring a mutation frequency, converting the frequency to a rate, and taking precautions to exclude or take into account the impact of perturbations such as differential growth rates of mutants versus wild type and delayed expression of the mutant phenotype. In addition to measuring rates, it is crucial to identify the kinds of mutations that arise in order to exclude biases due to massive mutational hotspots or to bizarre classes of mutations. The typical result is a rate for a mutation-reporter gene, which is then extrapolated to the whole genome provided that the spectrum of mutations is fairly ordinary. However, there is a substantial problem here: while most indels are detected, most BPSs fail to produce a phenotypic change detectable in the laboratory. One must therefore estimate their full frequencies. (An exception is the still rare case that mutation detection is achieved with phenotype-blind genomic DNA sequencing.) Two methods have been applied. Both make the reasonable assumption that almost all indels and chain-termination (CT) BPSs are detected with high efficiency in protein-coding sequences.  The first method was based in part on the average relative frequencies of CT and non-CT BPSs in a handful of spectra and provided a correction factor for base substitutions of 4.726 E. coli lacI gene, where ∼72% of all mutations are indels arising at a stretch of 13 BPSs consisting of 3.25 repeats of a tetramer The other major barrier to accurate extrapolation from a mutation-reporter gene to the whole genome becomes manifest when sequencing reveals a major hotspot. Mutation rates at particular sites vary greatly, but most mutational spectra display a range of site-specific numbers of mutations ranging from 1 to hotspots with from several percent to even a quarter of the whole collection. The impact of a hotspot containing a quarter of all the mutations is modest, but some genes contain single hotspots bearing the large majority of mutations; the classic example is the Sulfolobus acidocaldariusThermus thermophilusS. acidocaldarius, BPSs were a smaller fraction of the spectrum than in mesophiles, and this observation prompted the hypothesis of dangerous missense. Note, however, that if greater fractions of missense mutations are phenotypically detectable in thermophiles than in mesophiles, then the historical method of correcting for undetected BPSs becomes inappropriate when based on mesophiles. It is therefore advisable to resort exclusively to the CT method for estimating total BPS rates, which is the central result for this report.All informative microbial mutation rates obtained before 2000 were for mesophilic species, but rates and spectra are now available for two genes in each of two very different thermophiles, the crenarchaeon lacZα equivalent), sometimes based on the same sources as for p values are 0.018 for both the total mutation rate and its BPS component, and 0.27 for the indel values that include the hotspots.Genomic mutation rates have long been suspected to evolve as a balance between the deleterious impact of the average mutation and the cost of further reducing the mutation rate. A test of this conjecture on the evolutionary scale could consist of estimating mutation rates in organisms whose environment increases the impact of the average mutation. Because many base substitutions do greater harm at higher temperatures, thermophiles were suitable candidate organisms. For both a bacterium and an archaeon, the thermophiles display sharply reduced rates of base pair substitutions compared to the typical mesophile.37–38–41–50–50–51–68–69, providing no hint of a role for this variable, as also noted in the earlier molecular-evolution study The lower mutation rates in thermophiles are likely to reflect their higher optimal growth temperatures. There is no obvious hint of a particular aspect of life history other than temperature that sets the two thermophiles apart from the mesophiles. The %(G+C) values for the ten organisms in p value of 0.27 for these data. One candidate explanation for this difference is that the reduction in BPS rates is achieved by the accumulation of modifiers selected to target BPS mutagenesis but at most incidentally targeting indel mutagenesis. Because single-base additions and deletions tend to be the large majority of indels in mesophiles  and are similarly frequent in thermophiles , these small indels must be the main targets of antimutagenic modifiers acting on indels generally. Both single-base indels and BPSs result from errors of insertion followed by failures of proofreading and DNA mismatch repair in well studied model organisms such as E. coli and S. cerevisiae, but little is known about the sources of spontaneous mutations in S. acidocaldarius and T. thermophilus.The hypothesis of dangerous missense predicts that BPS rates will be reduced in thermophiles but does not speak directly to indel rates. However, indel rates are also reduced, although less strongly than are BPS rates and with a Are there likely to be other outliers with informative deviations from the mutational pattern that is consistently displayed among the mesophilic microbes examined to date with respect to either the mutation rate or the BPS:indel ratio?Mutations to cold sensitivity are rarely reported and are anecdotally described as difficult to discover. If they are indeed rare, perhaps fewer missense mutations produce mutant phenotypes in psychrophiles than in mesophiles. One evolutionary consequence might then be a relaxation to a higher spontaneous rate of BPS mutation, perhaps with little effect on the rate of indel mutation.+ and H+, respectively, compared to other microbes. These ionic environments might be unusually stressful to mutants carrying missense mutations, resulting in adjustments to their mutational patterns in the same direction as seen for thermophiles. Although without significance because of sampling constraints, S. acidocaldarius than to the non-acidophile T. thermophilus. Unfortunately, an attempt to characterize mutation in the halophilic archaeon Haloferax volcanii failed, probably because this mesophile is highly polyploid Because of incomplete buffering against the impacts of their environments, halophiles and acidophiles experience relative high internal concentrations of NaOenococcus oeni, used in wine making to convert malic acid to lactic acid, lacks the usual bacterial DNA mismatch repair (MMR) system and has a high mutation rate as judged by mutations conferring resistance to rifampin and erythromycin, as does Oenococcus kitaharaeThe lactic acid bacterium S. acidocaldarius, lack all known bacterial MMR genes, but S. acidocaldarius, at least, displays an antimutator phenotype compared with mesophiles. How can this be? In Escherichia coli, the mutation rate per average base pair ≈8×10−10 .T = the measured mutation rate at T, corrected where necessary for mutants expressing the characteristic phenotype but revealed by sequencing to lack mutations in the reporter gene, but not corrected for mutants with two or more mutations (which are infrequent and sometimes absent). In many cases, μT = f/ln(μTN) where f = the measured mutation frequency for the given target, N = the final population size, and the median μT over several cultures is used μM = number of sequenced mutants = B+I, where B = number of BPS mutants and I = number of indel mutants, the latter also including complex mutants  regardless of their components.b = [μT corrected upwards by (I+4.726B)/M]/T = (I+4.726B)( μT/MT). The genomic mutation rate μg = Gμb.For the “historical” method, we correct for undetected BPSs by multiplying the number of detected BPSs by 4.726 g(I) is calculated as above ignoring the BPS component, B becomes CTB = number of mutations to a chain-terminating codon , and P = number of possible mutational pathways to a CT mutation within T . Then the BPS genomic mutation rate μg(B) = μT (3CTB/MP)G. The total genomic rate μg = μg(I)+μg(B).For the “CT” method, the indel genomic mutation rate μG = 6407. This system is unique among popular mutation reporters. It consists of an E. coli lacZα transgene embedded in the single-stranded DNA of the M13 genome and carrying both an upstream regulatory region and the beginning of the lacZ gene. Because thousands of mutants have been sequenced, it has become apparent which mutations are detectable when present singly and which are not BT = 245) and for single-base indels (T±1 = 177) are thus well defined, and we further assume that the infrequent larger indels are fully detectable (LT = 239). The measured mutation frequency f was 5.86×10−4M = 117, B = 67, I±1 = 11 and LI = 39. Assuming that virtually all replication occurs by a rolling circle mechanism, the mutation rate is calculated as for RNA viruses, μ = f/2c where c is the number of consecutive cycles of infection 13 pfu) were added to l L of medium containing E. coli cells diluted from an overnight culture to about 107 cells/ml, so that the multiplicity of infection was about 103. The input of infected cells from the plaque was ≤108, so that the input concentration of infected cells  = 108/103/ml = 105/ml, that is, no more than 105/107 = 0.01 of all cells. c≈2.5 in the plaque +1 in the liquid culture  = 3.5. Then μb = (f/2c)Σ(proportion of mutations of type i = iN/117)(1/iT) = (5.86×10−4/7)[(3×67/117)(1/245)+(11/117)(1/177)+(39/117)(1/239)] = 7.48×10−7. μg(B) = (f/2c)(3×proportion of BPSs)(G/BT) = (5.86×10−4/7)(3×67/117)(6407/245) = 0.00376, μg(I+L) = (f/2c)[(proportion of ±1 indels)(1/T±1)+(proportion of larger indels)(1/LT)](G) = (5.86×10−4/7)[(11/117)(1/177)+(39/117)(1/239)](6407) = 0.00103, and μg = μg(B)+μg(I+L) = 0.00479.G = 4.850×104. For the cII gene, T = 294, f = 5.36×10−5, and, for 93 mutants, B = 55 (CTB = 8 and P = 35) and I = 38 (T = f/ln(μT N) = 6.10×10−6. Using the historical method, μb = (6.10×10−6)(38+55×4.726)(1/93)(1/294) = 6.64×10−8 and μg = 0.00304. μg(I) = (6.10×10−6)(38/93)(4.85×104/294) = 0.00041. Using the CT method, μg(B) = (6.10×10−6)(8/93)(3×4.85×104/35) = 0.00218 and μg = μg(I)+μg(B) = 0.00259.G = 1.523×105. For the tk gene, T = 1131, f = 6×10−5, N = (0.3–20)×108 and, for 67 mutants, B = 22 (CTB = 5 and P = 90) and I = 45 T = f/ln(μTN) and μT = f/2c (with c = 2), respectively. The corresponding values are μT = 8.40×10−6 and 10×10−6, giving a mean of 9.20×10−6. Using the historical method, μb = (9.20×10−6)(45+22×4.726)(1/67)(1/1131) = 1.81×10−8 and μg = 0.00275. μg(I) = (9.20×10−6)(45/67)(1.52×105/1131) = 0.00083. Using the CT method, μg(B) = (9.20×10−6)(5/67)(3×1.52×105/90) = 0.00348 and μg = μg(I)+μg(B) = 0.00432.See G = 1.689×105. For the rI gene, T = 294, μT = 2.82×10−6 and, for 66 mutants, B = 34 (CTB = 6 and P = 43) and I = 32 b = (2.82×10−6)(32+34×4.726)(1/66)(1/294) = 2.80×10−8 and μg = 0.00473. μg(I) = (2.82×10−6)(32/66)(1.689×105/294) = 0.00079. Using the CT method, μg(B) = (2.82×10−6)(6/66)(3×1.69×105/43) = 0.00302 and μg = μg(I)+μg(B) = 0.00381.G = 2.127×106. For the pyrEF genes, T = 1326, μT = 3.21×10−7 and, for 73 mutants, B = 19 (CTB = 2 and P = 103) and I = 54 (or 18 without the hotspot) g(I) = (3.21×10−7)(54/73)(2.127×106/1326) = 0.000381. Using the CT method, μg(B) = (3.21×10−7)(2/73)(3×2.127×106/103) = 0.000545. μg = μg(I)+μg(B) = 0.000926 (or 0.000672 without the indel hotspot).G = 2.226×106. For the pyrEF genes, T = 1240, μT = 3.37×10−7 and, for 108 mutants, B = 13 (CTB = 1 and P = 184) and I = 95 (or 46 without the hotspot) g(I) = (3.37×10−7)(95/108)(2.226×106/1240) = 0.000532. Using the CT method, μg(B) = (3.37×10−7)(1/108)(3×2.226×106/184) = 0.000113. μg = μg(I)+μg(B) = 0.000645 (or 0.000371 without the indel hotspot).G = 4.639×106. For the lacI gene, μT = 6.043×10−7 (excluding 10 IS insertions) T = 1083 and, for 721 mutants, B = 80 (CTB = 24 and P = 110) and I = 641 (or 116 without the indel hotspot) b = (6.043×10−7)(641+80×4.726)(1/721)(1/1083) = 7.89×10−10 and μg = 0.00366. μg(I) = (6.043×10−7)(641/721)(4.639×106/1083) = 0.00230. Using the CT method, μg(B) = (6.043×10−7)(24/721)(3×4.639×106/110) = 0.00255 and μg = μg(I)+μg(B) = 0.00485 (or 0.002961 without the indel hotspot).G = 1.246×107 and calculations are as above.URA3 gene, T = 804, P = 123, and four sets of values are available. For the first T = 2.77×10−8 and, for 106 mutants, B = 89 (CTB = 39) and I = 17; for the historical method, μb = 1.42×10−10 and μg = 0.00177; μg(I) = 0.00007; for the CT method, μg(B) = 0.00310; and μg = 0.00317. For the second T = 6.25×10−8 and, for 20 mutants, B = 15 (CTB = 4) and I = 5; for the historical method, μb = 2.95×10−10 and μg = 0.00368; μg(I) = 0.00024; for the CT method, μg(B) = 0.00380; and μg = 0.00404. For the third T = 3.50×10−8 and, for 106 mutants, B = 89 (CTB = 39) and I = 17; for the historical method, μb = 1.56×10−10 and μg = 0.00195; μg(I) = 0.00017; for the CT method, μg(B) = 0.00022; and μg = 0.00038. For the fourth T = 4.75×10−8 and, for 106 mutants, B = 89 (CTB = 39) and I = 17; for the historical method, μb = 2.37×10−10 and μg = 0.00295; μg(I) = 0.00014; for the CT method, μg(B) = 0.00446; and μg = 0.00460. The respective averages are, for the historical method, μb = 2.08×10−10 and μg = 0.00259; μg(I) = 0.00015; and, for the CT method, μg(B) = 0.00289 and μg = 0.00305.For the CAN1 gene, T = 1773, P = 226, and three sets of values are available. For the first T = 2.77×10−7 and, for 20 mutants, B = 11 (CTB = 1) and I = 9; for the historical method, μb = 5.18×10−10 and μg = 0.00645; μg(I) = 0.00095; for the CT method, μg(B) = 0.00249; and μg = 0.00344. For the second T = 3.01×10−7 and, for 23 mutants, B = 17 (CTB = 5) and I = 6; for the historical method, μb = 4.13×10−10 and μg = 0.00514; μg(I) = 0.00036; for the CT method, μg(B) = 0.00701; and μg = 0.00737. For the third T = 1.52×10−7 and, for 227 mutants, B = 150 (CTB = 70) and I = 77 (including 13 complex mutations); for the historical method, μb = 2.97×10−10 and μg = 0.00370; μg(I) = 0.00036; for the CT method, μg(B) = 0.00775; and μg = 0.00811. The respective averages are, for the historical method, μb = 4.09×10−10 and μg = 0.00510; μg(I) = 0.00056; and, for the CT method, μg(B) = 0.00575 and μg = 0.00631.For the URA3 plus three CAN1 values (sum÷7) are: for the historical method, μb = 2.94×10−10 and μg = 0.00366; μg(I) = 0.00033; and, for the CT method, μg(B) = 0.00412 and μg = 0.00444.The averages of the four G = 1.252×107, values are from ura4 gene, T = 795, μT = 4.56×10−8 and, for 39 mutants, B = 22  and I = 17; for the historical method, μb = 1.78×10−10 and μg = 0.00223; μg(I) = 0.00031; for the CT method, μg(B) = 0.00189; and μg = 0.00221. For the ura5 gene, T = 648, μT = 8.44×10−8 and, for 49 mutants, B = 34  and I = 15; for the historical method, μb = 4.67×10−10 and μg = 0.00585; μg(I) = 0.00050; for the CT method, μg(B) = 0.00337; and μg = 0.00387. The average values for the two genes are: for the historical method, μb = 3.23×10−10 and μg = 0.00404; μg(I) = 0.00041; for the CT method, μg(B) = 0.00263; and μg = 0.00304.G = 3.804×107. Using the old mutation data (see ad-3AB, μb = 4.11×10−11 and μg = 0.00172. For mtr, μb = 9.15×10−11 and μg = 0.00383. The average values are μb = 6.63×10−11 and μg = 0.00278.data see , for ad-


In a landmark paper, Nadeau and Taylor  formulat Rearrangements are genomic “earthquakes” that change the chromosomal architectures. The fundamental question in molecular evolution is whether there exist “chromosomal faults” (rearrangement hotspots) where rearrangements are happening over and over again. The random breakage model (RBM) postulates that rearrangements are “random,” and thus there are no rearrangement hotspots in mammalian genomes. RBM was proposed by Susumo Ohno in 1970 and later was formalized by Nadeau and Taylor in 1984. It was embraced by biologists from the very beginning due to its prophetic prediction power, and only in 2003 was refuted by Pevzner and Tesler, who suggested an alternative fragile breakage model (FBM) of chromosome evolution. However, the rebuttal of RBM caused a controversy, and in 2004, Sankoff and Trinh gave a rebuttal of the rebuttal of RBM. This led to a split among researchers studying chromosome evolution: while most recent studies support the existence of rearrangement hotspots, others feel that further analysis is needed to resolve the validity of RBM. In this paper, we develop a theory for analyzing complex rearrangements (including transpositions) and demonstrate that even if transpositions were a dominant evolutionary force, there are still rearrangement hotspots in mammalian genomes. In 1970, Susumu Ohno came up with two fundamental models of chromosome evolution that were subject to many controversies in the last 35 years ,3. The oRearrangements are genomic “earthquakes” that change the chromosomal architectures. The fundamental question in molecular evolution is whether there exist “chromosomal faults” (rearrangement hotspots) where rearrangements are happening over and over again. RBM postulates that rearrangements are “random,” and thus there are no rearrangement hotspots in mammalian genomes.synteny blocks are constructed correctly, and (2) chromosomal architectures mainly evolve by the “standard” rearrangement operations , then every evolutionary scenario for transforming the mouse genome into the human genome must have a very large number of breakpoint re-uses. This result implies that the same regions of the genome are being broken over and over again in the course evolution (rearrangement hotspots), a contradiction to RBM . Consequently, Pevzner and Tesler  is the permutationInitially, genome rearrangements were modeled by a combinatorial problem of sorting by reversals, as described below. The order of genes in two organisms is represented by permutations ρ has the effect of reversing the order of iπι+1…πjπ and transforming 1…πi-1πi…πjπj+1…πnπ into π⋅ρ = 1…πi-1πj…πiπj+1…πnπ. Given permutations π and σ, the reversal distance problem is to find a series of reversals 1ρ,2ρ,…,tρ such that 1·ρ2 ...·ρt = σπ·ρ and t is minimal. We call t the reversal distance between π and σ. Sorting π by reversals is the problem of finding the reversal distance d(π) between π and the identity permutation (12...n).The reversal 1π2…πnπ = π by adding 0π = 0 and n+1π = n + 1. We call a pair of elements , 0 ≤ i ≤ n, of π an adjacency if i - πi+1||π =1, and a breakpoint if i|π − i+1|π > 1. It is easy to see that d(π) ≥ b(π) / 2, where b(π) is the number of breakpoints in π. However, the estimate of reversal distance in terms of breakpoints is very inaccurate. Bafna and Pevzner  changes both the order and the signs of the elements within that fragment. We are interested in the minimum number of reversals d(π) required to transform a signed permutation π into the identity signed permutation (+1+2...+n).Finding a maximal cycle decomposition is a difficult problem. Fortunately, in the more biologically relevant case of d(π) ≥ n +1 − c(π) approximates the reversal distance extremely well. Hannenhalli and Pevzner [h(π) is the number of hurdles in π.The concept of a breakpoint graph extends naturally to signed permutations by mimicking every directed element by two undirected elements, which substitute for the tail and the head of the directed element . For sig Pevzner  showed tπ and Γ, we are interested in a most parsimonious scenario of evolution of Π into Γ . We assume that Π and Γ contain the same set of genes.In the model of the multichromosomal genomes we consider, every gene is represented by an integer whose sign (“+” or “–”) reflects the direction of the gene. A chromosome is defined as a sequence of genes, while a genome is defined as a set of chromosomes. Given two genomes Π be a multichromosomal genome. Every chromosome π in Π can be viewed either from left to right ) or from right to left ), leading to two equivalent representations of the same chromosome . The four most common elementary rearrangement events in multichromosomal genomes are reversals, translocations, fusions, and fissions, defined below.Let π = 1…πnπ be a chromosome and 1 ≤ i ≤ j ≤ n. A reversal ρ on a chromosome π rearranges the genes inside π = 1…πi-1πι…πjπj+1…πnπ and transforms π into 1…πi-1π − j…π−iπj+1…πnπ. Let π = 1…πnπ and σ = ι…σmσ be two chromosomes and 1 ≤ i ≤ n +1, 1 ≤ j ≤ m + 1. A translocation ρ exchanges genes between chromosomes π and σ and transforms them into chromosomes 1…πi-1σj…σmπ and ι…σj-1πi…πnσ with (i – 1) + (m – j + 1) and (j – 1) + (n – i + 1) genes, respectively. We denote as Π·ρ the genome obtained from Π as a result of a rearrangement  ρ. Given genomes Π and Γ, the genomic sorting problem is to find a series of reversals and translocations 1ρ,2ρ,…,tρ such that 1·ρ2·…·ρtΠ·ρ = Γ and t is minimal. We call t the genomic distance between Π and Γ. The genomic distance problem is the problem of finding the genomic distance d between Π and Γ.Let ρ concatenates the chromosomes π and σ, resulting in a chromosome 1…πnσ1…σmπ and an empty chromosome Ø. This special translocation, leading to a reduction in the number of (nonempty) chromosomes, is known in molecular biology as a fusion. The translocation ρ for 1 < i < n “breaks” a chromosome π into two chromosomes: (1…πi-1π) and (i…πnπ). This translocation, leading to an increase in the number of (nonempty) chromosomes, is known as a fission.A translocation Text S1(123 KB PDF)Click here for additional data file.


In the original publication of this article , some er“Creating, umil/L” should be “Creatine, umol/L”“Glucose, nmlo/L” should be “Glucose, nmol/L”“Urea, nmo/L” should be “Urea, nmol/L”The original publication has been corrected.