Trastuzumab, a humanized monoclonal antibody against the HER2 receptor is currently being used in breast and other tumor types. Early studies have shown that a variable proportion of cervical carcinoma tumors overexpress the HER2 receptor as evaluated by diverse techniques and antibodies. Currently it is known that a tumor response to trastuzumab strongly correlates with the level of HER2 expression evaluated by the Hercep Test, thus, it seems desirable to evaluate the status of expression of this receptor using the FDA-approved Hercep Test and grading system to gain insight in the feasibility of using trastuzumab in cervical cancer patients.We analyzed a series of cervical cancer cell lines, the primary tumors of 35 cases of cervical cancer patients and four recurrent cases, with the Hercep Test in order to establish whether this tumor type overexpress HER2 at level of 2+/3+ as trastuzumab is currently approved for breast cancer having such level of expression.The results indicate that only 1 out of 35 primary tumors cases overexpress the receptor at this level, however, two out of four recurrent tumors that tested negative at diagnosis shifted to Hercep Test 2+ and 3+ respectively.The low frequency of expression in primary cases suggests that trastuzumab could have a limited value for the primary management of cervical cancer patients, however, the finding of "conversion" to Hercep Test 2+ and 3+ of recurrent tumors indicates the need to further evaluate the expression of HER2 in the metastatic and recurrent cases. Cervical carcinoma is a leading cause of death in women of reproductive age worldwide, particularly in developing countries. While curable in early stages, the treatment results of locally advanced disease are unsatisfactory. The current standard of treatment -cisplatin-based chemoradiation- fails to cure at least 15% to 45% of bulky IB to IIIB patients, and in addition, multimodality treatment incorporating chemotherapy, surgery and radiation at its best is unlikely to substantially increase the cure rate. Because of this, the logical step to follow is the testing of molecular targeted therapies trying to improve the prognosis of cervical cancer patients [Human papillomavirus infection is recognized as the stronger etiological factor for the development of this tumor; however, overexpression of the epidermal growth factor receptor family members is also common and seems to play an important oncogenic role . HER2  and grading scores but also from the antibody used. This issue was addressed by Press et al., who showed extremely variable results in 187 breast cancer specimens evaluated with 7 polyclonal and 21 monoclonal antibodies . HoweverBefore the introduction of the Hercep Test, it was known that a variable subset of cervical carcinomas ranging from 8% to 77% express HER2 as evaluated by diverse methods -14 and tThirty-five paraffin-embedded tumor tissues from patients FIGO staged as IB2 to IIIB, treated with standard radiation concurrent with weekly cisplatin. Diagnosis was made on the basis of routine hematoxilin-eosin examination under light microscopy according to the World Health Organization criteria. Tumor specimens at diagnosis were taken before any treatment was instituted whereas the tumors samples from the four recurrent cases were also taken before patients received any second line therapy.2. Cell lines were grown on two-chamber polysterene vessel Falcon® (Becton Dickinson, NJ.) and subsequently formalin-fixed for 24 hrs at room temperature, then rehydrated in graded ethanol. Afterwards immunochemistry was performed as below described.DMEM culture media and Fetal Calf Serum were purchased from Gibco BRL Life Technologies (Grand Island, New York). HeLa, CasKi, SiHa and C33A carcinoma cell lines were obtained from the ATCC. The cell line ViBo established from a Mexican patient with cervical cancer was kindly provided by Dr. Monroy (FES Zaragoza, UNAM, Mexico City). Cells were grown in DMEM supplemented with 10% FCS at 37°C and 5% COHercep Test was performed following the manufacturer's guidelines of HER2 protein expression as follows. Sections were deparaffinized in xylene and rehydrated through graded ethanols to distilled water. The sections were immersed in Dako Epitope Retrieval Solution (10 mM citrate buffer, pH6) that had been preheated to 95°C in a water bath and then heat-treated at 95°C for 40 min. After a 20-minute cooldown period at room temperature, the sections were washed with Dako Wash Buffer, a procedure that followed every subsequent incubation. Endogenous peroxidase was blocked with Dako Blocking Buffer (0.3% hydrogen peroxide containing 15 mM sodium azide) for 5 min at room temperature. The sections were incubated with the primary polyclonal antibody, an affinity-purified rabbit antihuman HER2 antibody supplied in the kit, for 30 min at room temperature. Bound primary antibody was labeled by incubating the slides with the Dako Visualization reagent (horseradish peroxidase-labeled dextran polymer conjugated to affinity-purified goat antirabbit immunoglobulins in Tris-HCl) for 30 min. Color development was achieved with 3,3'-diaminobenzidine (DAB) for 10 min. The sections were counterstained with hematoxylin and eosin. To confirm validation of the staining run, control cell slides, which were provided in the kit and consisted of three pelleted, formalin-fixed, paraffin-embedded human breast cell lines with known HER2 positivity , were also stained simultaneously. In the negative controls, the primary antibody was replaced by normal rabbit serum (Dako Negative Control Reagent) for the HER2 primary antibody. The antibody used in Hercep Test did not exhibit cross-reactivity to HER3 and HER4 in western blot analysis.Following the FDA scoring guidelines for breast carcinomas, only membrane staining intensity and pattern were evaluated using the 0–3+ scale as illustrated in the Hercep Test kit scoring guidelines . The analysis was performed by a pathologist (VP-S) familiarized in the use of Hercep Test for breast cancer patients. In accordance with the Hercep Test kit guide, HER2 overexpression was assessed as negative for scores of 0 or 1+ and positive for scores of 2+ and 3+.Her-2/neu was evaluated using the Path-Vysion DNA Probe Kit (Vysis), which uses a dual-color probe for determining the number of copies of both Her-2/neu (orange) and the chromosome 17 centromeres (green). The kit was used following the manufacturer's instructions with a few minor modifications. Slides containing 5μ thick paraffin-embedded tissue sections of studied cervical tumor cases and a known Her-2/neu amplified breast tumor were placed on a slide warmer overnight at 58°C, followed by deparaffinization in Xilol, dehydration in 100 ethanol, and drying on a slide warmer at 45 to 50°C. Slides were then pretreated with 0.2 N hydrochloric acid for 20 minutes, followed by washes in purified water and immersion in Vysis wash buffer. They were subsequently immersed in Vysis protease solution at 37°C for 10 minutes, washed in Vysis wash buffer, and dried on the slide warmer. The slides were then immersed in 10% buffered formalin at room temperature for 10 minutes, immersed in Vysis wash buffer, and dried on the slide warmer. Sections were denatured by placing the slides in formamide for 5 minutes at 72°C followed by dehydration in 70, 85 and then 100% ethanol. Slides were then dried on a slide warmer, and 10 μl of probe was applied. They were then coverslipped, sealed and placed in a prewarmed humid incubation chamber at 37°C for 21 hours. This was followed by immersion in prewarmed postwash solution at 72°C for 2 minutes. The slides were air-dried, and a 4',6-diamidino-2-phenylindole (DAPI) counterstain was applied.Amplification of Her-2/neu signals (orange) to chromosome 17 centromere signals (green) was determined with ratios of <2.0 considered nonamplified and those ≥ 2.0 amplified.The scoring system used is described in detail in the manufacturer's instruction. A minimum of 60 nuclei were scored by each of 2 observers using a Zeiss Axioskop-2 fluorescent microscope with V.2 filter. The ratio of The immunohistochemical expression of HER2 in the primary tumors of 35 patients at diagnosis was evaluated. Accordingly, these patients had no received any previous anticancer therapy; their mean age was 40.8 years; 5 were staged as IB2-IIA, 14 as IIB and 16 as IIIB; 31 and 4 were histologically classified as squamous and adeno/adenosquamous respectively. Overexpression of HER2 was demonstrated in only one out of 35 cases, which had a score of 3+ (not shown). The remaining cases were interpreted as negative [score of 0]. The case with HER2 overexpression at diagnosis was a 56-year old woman diagnosed with a FIGO stage IIB large cell poorly differentiated squamous cell carcinoma, who received treatment with 6 weekly courses of cisplatin concurrent with external beam radiation and brachytherapy. She is currently free of disease at 44 months of follow-up. At a median follow-up time 40 months, seven patients have relapsed.HER2 expression at recurrence could only be analyzed in four of these seven relapsed patients in whom whose recurrent disease was histopathologically confirmed. Two of these four tested positive with a staining intensity of 2+ and 3+ respectively, Figures  and 2, bThe five cervical cancer cell lines were negative.None of the four recurrent cases tested by FISH were HER2 amplified Figure .Molecular targeted therapies are currently being tested in a variety of tumor types with promising results. Because HER2 overexpression occurs and is related to a worse prognosis in cervical cancer, -9,13, itPrevious reports on cervical cancer using non-standardized methods for HER2 expression showed that up to 77% of cases express the receptor and that in general HER2 expression predominates in adenocarcinoma and adenosquamous carcinoma histologies -14 In thOn the other hand, the HER2 expression in breast cancer is relatively stable, with 95% concordance between the HER2 status of primary and metastatic lesions, being rare a shift from positivity in the primary to negativity in the metastases . Such beneu is rare in cervical adenocarcinomas and that low level chromosome 17q copy number gains are not associated with HER-2/neu overexpression [It is remarkable the finding that none of the four recurrent cases, including the two that converted to IHC positive analyzed by FISH showed HER2 gene amplification. This result is unlikely to be a false negative as the green signal was perfectly observed in most of the cells. Previous studies in cervical cancer have shown that the frequency of gene amplification as determined by FISH is low irrespective of tumor histology. Mark et al., reported only 2 out of 23 cases amplified using the Her-2/neu FISH probe (Vysis, Inc., Downers Grove, IL) both of which were adenocarcinomas . In a mopression . Such ovpression  and is npression .In conclusion, our study suggest that the clinical usefulness of anti-HER2 antibodies in the primary treatment of cervical cancer patients may be limited due to the low frequency of HER overexpression, nevertheless, it is desirable to further test a larger number of recurrent cervical cancer patients by IHC and FISH analyses regardless of the histological type, as a start point for clinical trials design using trastuzumab.None declared.A C-B, AG-F, carried out the tissue culture work and immunohistochemical analysis; VP-S and T V-C interpreted the histological data; MC critically analyzed and participated in manuscript; CP, contribute with the clinical data; SV performed the FISH analysis; and AD-G conceived the study and wrote the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


During their life, multicellular organisms are challenged with oxidative stress. It is generated by several reactive oxygen species (ROS), may limit lifespan and has been related to several human diseases. ROS can generate a wide variety of defects in many cellular components and thus the response of the organism challenged with oxidative stress may share some features with other stress responses. Conversely, in spite of recent progress, a complete functional analysis of the transcriptional responses to different oxidative stresses in model organisms is still missing. In addition, the functional significance of observed transcriptional changes is still elusive.2O2, two oxidative stressors, and by tunicamycin which induces an endoplasmic reticulum (ER) stress. Both specific and common responses to the three stressors were observed and whole genome functional analysis identified several important classes of stress responsive genes. Within some functional classes, we observed that isozymes do not all behave similarly, which may reflect unsuspected functional specificities. Moreover, genetic experiments performed on a subset of lines bearing mutations in genes identified in microarray experiments showed that a significant number of these mutations may affect resistance of adult Drosophila to oxidative stress.We used oligonucleotide microarrays to address the specificities of transcriptional responses of adult Drosophila to different stresses induced by paraquat and H2O2-induced oxidative stresses and ER stress is observed for a significant number of genes. Besides this common response, the unexpected complexity of the stress responses to oxidative and ER stresses in Drosophila, suggest significant specificities in protective properties between genes associated to the same functional classes. According to our functional analysis, a large part of the genome may play a role in protective mechanisms against oxidative stress in Drosophila.A long term common stress response to paraquat- or H Increased endogenous production, exposition to exogenous sources of ROS or reduction in antioxidant defense capacity cause molecular damages such as alterations in proteins, lipids and DNA, and may lead to cell death. Oxidative stress is believed to limit the lifespan of multicellular organisms [in vivo responses to oxidative stress is thus of major fundamental and practical importance.Cells are frequently submitted to exogenous or endogenous stresses. In aerobic cells, reactive oxygen species (ROS), produced by respiration and other biological processes, are a major source of endogenous stress. These ROS include the superoxide radical  but could be less useful to address the question of stress response in dividing cells. Secondly, limitations also arise from ROS-generating compounds delivery which in Drosophila is usually performed through food ingestion. This method severely limits kinetic studies of acute stress responses on flies, since, within a few hours, large fluctuations in the quantity of ingested food are observed in batches of flies transferred to a new medium. To overcome this problem some experiments were performed on starved flies. The major issue with such an approach is that the observed transcriptional changes could result from the starvation stimulus as well as from the effect of the studied compound. Therefore such experiments may in fact characterize the interference of two different stress responses with different induction times and kinetics rather than a bona fide oxidative stress response. A previous microarray study, performed with such a strategy on 4500 Drosophila genes, analyzed changes in transcription induced by paraquat, which produces superoxide radical (O2•-) intracellularly: 5.2% of the genes (n = 236) were found to be stress responsive. Kinetic analysis revealed that transcriptional modifications lead to the establishment of a more or less stable new expression profile 12 hours after stress induction, thus indicating the existence of a long term stress response (LTSR) [Considering its powerful genetic and genomic tools, e (LTSR) . This st2O2-induced oxidative stresses and tunicamycin-induced ER stress. This latter drug inhibits N-linked glycosylation, thus leading to an accumulation of misfolded proteins in the ER (referred to as ER stress) which is known to elicit a specific response: the Unfolded Protein Response (UPR) [From previous considerations, we chose to focus on comparisons of the long term transcriptional response (LTSR) of adult flies 24 h after ingestion of different stress-generating compounds. These responses may be representative of those of postmitotic tissue exposed to physiological chronic stress conditions (even if the level of stress is certainly higher in our experiments). Thus we investigated, at a full genome wide level, the transcriptional LTSR in adult Drosophila submitted during 24 h to three types of stresses: paraquat or Hse (UPR) . We showw+ transposon insertion, the line was outcrossed for 4 generations against a w- isogenic strain of Canton S background before stress experiments. For homozygous lethal mutations we analyzed flies heterozygous for the mutation issued from a cross between the mutant line and the same w Canton S strain.All the lines tested for paraquat stress resistance were collected from the Bloomington stock center. To minimize genetic background effects, when the mutation was linked to a 2O2, 5 or 15 mM paraquat, 12 μM tunicamycin  or no toxic compound (control tubes). These compounds were incorporated at 45°C to avoid loss of activity.We used 50 ml vials containing 1 ml of a solid medium composed of 1.3% low melting agarose, 1% sucrose and either 1% H3 day old males were placed in groups of 30 in these vials and maintained at 26°C with a 12:12 light-darkness alternation.w Canton S flies. We considered that a mutation had a significant effect on survival under oxidative stress when the mean of log10 (p-log-rank) for the three experiments was lower than -3 and at least two experiments had p-log-rank < 0.001. For microarray experiments, for each condition, 200 Canton S males were kept 24 hours on the corresponding medium and then frozen in liquid nitrogen for subsequent RNA extraction. Independent batches of males from separate experiments were used for replicate experiments. All fly manipulations were performed at the same stages of the 12:12 light cycle to prevent any undesirable effects from circadian variations.In survival tests, dead flies were counted twice a day until the end of the experiment. In each experiment at least 3 vials of 30 male flies were used. To test mutant lines we performed three independent experiments in order to minimize false positive detection. Survival data were submitted to a log-rank analysis to detect statistically significant survival differences between mutant and We analyzed 4 samples of control flies and 3 (paraquat 15 mM) or 2  samples of stressed flies. Total RNAs were purified by three rounds of Trizol reagent (GIBCO/BRL) extraction before precipitation. cDNA were synthesized from 10 μg total RNA aliquots and biotin-labelled cRNA targets synthesized using the BioArray high yield RNA transcript-labelling kit (Enzo Biochem) according to the manufacturer's instructions. Hybridizations on Drosophila Genome Arrays (Affymetrix) and subsequent washing were performed on a GeneChip Fluidics Station according to the manufacturer's instructions before scanning on a GeneArray scanner.p-values (DP) for each probe set. These data were loaded into an Access database for subsequent analysis. We retained only the experimental points that presented a mean value greater than 0.1 for the DPs of all the different samples of at least 1 of our 4 experimental conditions. This reduced the number of probe sets further analyzed from 14028 to 8976. The AV data from each microarray were then normalized against the AV mean value of the 4 control samples by a quantile method which performs optimally [Data extraction was performed first by the MAS5 Affymetrix program which provides absolute values (AV) and detection ptimally . Since aptimally . For thi. For each probe set, the ratios for all the combinations between stress conditions AVs and reference AVs were computed and Ln2 transformed. The SOTA algorithm used on this dataset with linear correlation distance with 0 offset, 1000 cycles and 1.01 variability threshold parameters, led to the detection of 19 clusters.A hierarchical divisive clustering of the data of these probe sets was performed using the SOTA  implemen to investigate which classes are over- or under-represented in the dataset of stress responsive genes. Briefly, according to the Gene Ontology hierarchical structure, each probe set was assigned, when possible, to its original annotation and to the associated parent annotations. The number of probe sets for the different GO terms was computed for groups of probe sets defined according to different criteria (such as whole microarray probe sets, detected probe sets or probe sets belonging to a given cluster).Information from the Gene Ontology (GO) database (December 2003, ) was comGD probe sets issued from a total of ND, probability PG = NGD/ND) and a group C of particular interest, such as a cluster  are compared. The hypothesis of equal repartition between these two groups would predict that, inside the NC probe sets of group C, NC*PG probe sets should be associated to the GO term. We computed the p-value PN for the null hypothesis of no association between the two distributions, with a binomial distribution with NC tries, a probability PG and NGC successes. Threshold values for PN helped to define the GO terms over- or under-represented in the group C.For each GO term G, the distribution between the group D of all the detected probe sets  or 12 μM tunicamycin (T12: 2 samples). Thus a minimum of 8 pairwise comparisons were made for each condition which ensured good statistical significance, as confirmed by quantitative PCR experiments (see below). Stresses were induced 24 h before collection of flies, which occurred at the same time (9 h) of the 12:12 hours light/dark cycle to eliminate the effect of circadian variations. Hybridizations were performed on Affymetrix GeneChips and the data processed as described in the Material and Methods. The statistical significance of transcriptional variations was assessed using the SAM program with a threshold of 1.5 [et al., 201 were associated to a detectable probe set on our chip, 56% of which were selected by SAM analysis and 72% of which displayed a fold change greater than 1.3 (not shown). The remaining discrepancies may arise from differences in statistical selections, in analyzed tissues (thorax and abdomen in [w1118 flies used in [vs 54% at 24 h) and presented an increased medium lifespan (48 vs 35 days at 26°C, on standard medium).RNA were obtained from separate experiments with 3 day old male flies reared at 26°C with a 12:12 hours light and dark (LD) alternation, on media containing no drug (4 reference samples), 15 mM paraquat (P15: 3 samples), 5 mM paraquat (P5: 2 samples), 1% Hd of 1.5 . A good Among the 8976 probe sets significantly detected in adult flies , 1111 were up or downregulated with P15 treatment, this number being reduced to 608, 72 and 221 for P5, H1 and T12 treatments respectively. Thus, even with similar effects on flies survival, the fraction of the genome detected as stress responsive on microarrays was highly dependent on the nature of the stress, varying about ten times from 7% (P5) to 0.7% (H1). This first analysis defined a total of 1368 probe sets and 1343 genes which are induced or repressed at least in one stress condition. They were used for further analysis.D. melanogaster. Lower correlations were observed for the linear regressions between P5 ratios and either H1 ratios  to anal2O2 on the transcriptome rather than fundamental differences in the responses to the two oxidants.Notably, in clusters 5, 6, 7, 9, 10, 13, 16 and 18 which regroup 642 probe sets, significant variations for H1 were observed in the same direction than for P5 or P15. This suggests that, for a large number of genes, both oxidative compounds induce similar transcriptional responses. Therefore, the fact that the number of probe sets validated by the SAM procedure as being significatively affected in the H1 condition is smaller than in the paraquat conditions may be a consequence of a similar but weaker effect of H2O2 compared to the ER stress response induced by tunicamycin? The 19 clusters from Tab.1  or with one mismatch (CG8993, CG13473 and CG3719). Only the Txl gene is significantly overexpressed over the 1.5 fold threshold, the other genes presenting no change or a weaker overexpression (Trx-2). This strongly argues for a specificity of these thioredoxins in the defense process with an important role for the Txl gene. Similarly, among the five genes presenting a thioredoxin peroxydase activity (GO:0008379), only two (CG12013 and CG1633) are overexpressed, the others (CG12174, CG5826 and CG6888) being unaffected in the studied conditions. Among the related genes only the peroxyredoxin CG11765 is overexpressed, while the glutathione peroxydase-like CG15116, very similar to the thioredoxin peroxydase CG12013, is significantly repressed. These specificities strengthen the concept of a functional diversification of these proteins in spite of their common ability to confer resistance to oxidants in Drosophila cells [The thioredoxin system plays a major role in oxidative stress defense and needs to be better functionally characterized. In Drosophila, the peroxiredoxin proteins show thiol-dependent peroxidase activity and use thioredoxin, but not glutathione, as a source of reducing power. Indeed, Drosophila lacks glutathione reductase  and its 2O2 by the Fenton reaction, its concentration must be tightly controlled. Transferrin and ferritin proteins play a major role in this control [irp1B) are overexpressed, while the transferrin 1 (tsf1) gene is severely repressed. Nevertheless, neither the irp1A nor the tsf2 and tsf3 genes show any significant transcriptional change. This suggests that each isoform of these families plays a specific role in iron homeostasis in the organism.When the organism is challenged to oxidative stress, in addition to performing direct enzymatic detoxification of toxic compounds, it must also limit the appearance of the most toxic species. Therefore, since free iron catalyses the production of the highly toxic hydroxyl radical (OH•) from H control . Further2O2-induced stress. Moreover, among the 16 GTs overexpressed in paraquat-induced stresses, 7 are overexpressed and 3 underexpressed in ER-stressed flies, while 6 show no other significant transcriptional variation. Interestingly, all the GTs overexpressed in both paraquat and tunicamycin experiments are also slightly induced in H2O2-stressed flies. Overall, our data suggest that both "generalist" GTs that are able to protect the organism against various stresses and more specialized GTs, required only for protection against well defined stresses, coexist inside the cell.More complex specificities can be observed in larger functional classes. The glutathione transferases  play important roles the detoxification process after genotoxic stresses . As expe2O2-stressed flies. One cytochrome gene (FBgn0015035) displays peculiar behavior since it is induced by paraquat but strongly repressed by H2O2. Another gene (FBgn0015039) is induced specifically by tunicamicyn. Quantitative RT-PCR experiments confirmed the specificities observed on microarrays . Among 58 detectable P450 cytochromes, 12 are underexpressed and 12 overexpressed during paraquat stress, 4 of these latter being also upregulated in tunicamycin-stressed flies  analysis – given as Tab.S5  are either significantly more resistant (6 lines) or more sensitive (7 lines) to paraquat than their wild-type counterparts  For the genes studied there is no clear correlation between the observed induction or repression under paraquat treatment and the effect of the mutation on the paraquat resistance or sensitivity phenotypes , thioredoxin reductase (Trxr-1), fatty acid elongase , phosphatase (aay) and phosphatase regulator (CG9238), transcription factor (Xbp1) and peptidase (Acer). Interestingly, among these 13 mutants, only 2 were previously known to be associated to oxidative stress resistance (Sh and Trxr-1) and most of them had no known function in adult flies. Coupling between microarray and genetic experiments is thus a powerful way to extend our knowledge on the biological function of Drosophila genes without biased hypothesis and to provide some clues on the function of mammalian homologues.When a fly experiences an oxidative stress we can expect that the subsequent transcriptional modifications may arise from several mechanisms. Firstly, the organism can mount a protective response, for instance by inducing proteins which will reduce adverse consequences of the toxic compound. Only a few functional classes (such as GTs, electron transporters, chaperones) identified in our functional analysis of stress-regulated genes can be clearly associated to such known protective mechanisms from oxidative stress , H2O2 or tunicamycin. Experiments on yeast submitted to several types of stress including oxidative stress have shown that fast transient responses occurring during the first three hours are followed by stable long term (>12 hours) changes [et al. similar results were obtained when the transcriptome was analyzed 12 hours  or 24 hours after ingestion of 15 mM paraquat. Furthermore, when functional analysis was performed, we were unable to detect significant differences in the signature of the genes detected in the P5 and P15 experiments, which should be the case if the level of lethality plays an important role for gene transcription. We thus conclude that the secondary effects linked to the levels of lethality in the Zou et al. experiments and in our work do not significantly affect the transcriptome and that the variations observed are primarily due to the stresses experienced by the flies.In this paper we present the characterization of the  changes ,6. Simil changes . Since, 2O2-induced flies. We think that this may be due to a smaller number of cells experiencing stress when flies ingest H2O2. Additional data for comparison with various stress responses (immune stress [Our data present clear evidence of a common long-term stress response (CLTSR) in transcription of Drosophila genes: at least 237 genes contained in clusters 9, 10 and 13 show similar changes in transcription for the three stressors studied. This number could be a minimum estimation of the extent of the CLTSR, since it is mainly limited by the weaker transcriptional variations observed in the He stress , starvate stress  and, dure stress ) are preThor is induced under stress conditions and that mutations in this gene confer a slight but significant sensitivity to paraquat-induced stress. However, our finding that tRNA ligases are upregulated in oxidative and ER stress may indicate a requirement for increased protein synthesis under sustained stress conditions. Kinetic studies using another oxidative stress paradigm are needed to clarify this point. In view of our results, it would be also interesting to investigate possible variations in stress response in mammalian tissues either mitoticaly active or quiescent.The CLTSR shows certain similarities with the common environmental response (CER) described in yeast ,6: in bo2O2- or tunicamycin-induced stresses. It is also noticeable that, in contrast to proteasome constituents, ubiquitin protein ligases are under-represented among paraquat responsive genes. The 20S proteasome, inactive in its native form, is able to specifically degrade oxidized proteins in vitro and in vivo, and has been considered to be the main actor in this process [in vivo for oxidized proteins degradation.Besides their similarities, the LTSRs also display marked differences. One of the most striking specific expressions is displayed by the genes encoding for the proteasome subunits. These proteins belong to the two large complexes 19S (regulatory complex) and 20S  which, together, form the 26S proteasome . Most of process . Neverth process . This se process ,32. The 2O2-stressed Drosophila. This result is coherent with previous studies shoving that the proteasome subunit are not transcriptionally regulated in cultured mammalian cells treated with H2O2 [2O2 treatment [2O2 induces an increase in proteasome activity and E1 ubiquiting activation enzyme levels without any increase in E1 mRNA levels [D. melanogaster to deal with oxidative challenge and increase proteasome activity: one response, induced by H2O2, would rely on post-transcriptional mechanisms as shown in mammalian cells; while the other response, induced by paraquat, would rely on coordinated increase of transcription of the proteasome genes of both 19S and 20S subunits.We observed no such induction of proteasome components in Hith H2O2 . Howeverreatment . Recent A levels . In viewJafrac2, which codes for a thioredoxin peroxidase, has been recently assigned an unexpected role in caspase-regulated cell death [A number of functional classes are clearly over-represented among the genes involved in the LTSRs. The analysis of these specific functional classes revealed an important heterogeneity of stress-specific responses among their members. For instance, we have shown that only a subset of genes potentially involved in the thioredoxin pathway are upregulated during paraquat stress. Whether the remaining genes are involved in an earlier phase of the stress response, in a subset of tissues or in other processes unrelated to stress protection needs to be addressed. Interestingly, in agreement with this last hypothesis, one of these genes, ll death . The P45ll death  we foundOne important issue is whether our findings are representative of long term transcriptional responses in Drosophila submitted to real physiological chronic stresses. Indeed, the stress levels experienced by flies in this work are probably much higher than those experienced in real life. Nevertheless, the tight correlation that we observe between P5 and P15 experiments demonstrates that most of the genes undergoing transcriptional changes at a high concentration of paraquat display similar changes  when the concentration is threefold lower. This suggests that many genes identified in this study may also be induced in low intensity chronic stress.in vivo. This contrasts with two previous genetic screens to detect paraquat hypersensitive mutants, which concluded that only a few genes are involved in paraquat hypersensitivity [w+; UAS}- based screen we found that a large proportion of P-element insertions may confer H2O2 or paraquat resistance or sensitivity , we took a particular care to ensure that there was a controlled genetic background in these experiments. We found that 45% of the mutations tested were associated with either resistance or sensitivity to paraquat, which confirms this idea of a highly polygenic process. It should be stressed that, since many of the tests were performed on heterozygous flies, the proportion of genes functionally involved in oxidative stress resistance may be higher. Extrapolation of the results obtained with this small subset of 29 genes to the 1107 genes found to be regulated by paraquat, suggests that some 500 genes may modulate paraquat sensitivity sitivity ,38. Thesitivity , the paraquat also induces transcriptional changes that play a role in its toxicity. In mammalian cells, several transcription factors may be regulated by oxidative stress, either by direct modification by the ROS or through signaling pathways, and have either pro- (Jun, p53) or anti-apoptotic effects (NF-κB, HSF1) . In addiXbp1 gene resulted in increased sensitivity of flies to paraquat-induced stress. Xbp1 is known to be involved in ER stress response in mammals [Xbp1 in Drosophila challenged with tunicamycin but it is overexpressed in oxidative stress conditions. Our in vivo genetic study suggests that this regulation is functionally relevant to oxidative stress protection in Drosophila. Thus Xbp1 may protect against different stress conditions through different modes of regulation . In agreement to the conservation of this mechanism between flies and mammals, it has been shown recently that, in a mammalian dopaminergic cell line, Xbp1 is induced by the parkinsonian mimetic 6-hydroxydopamine which is known to induce oxidative stress [Interestingly, in our genetic experiments, halving the dosage of the  mammals . It has e stress .in vivo is Acer. This gene encodes one of two Drosophila proteins homologous to the mammalian angiotensin converting enzyme (ACE) gene family. Controversial findings have linked Ace to stress resistance and aging >i,j where AVstressi and AVrefj correspond to the average valuemeasured for the ith sample in the stress condition and the jth sample respectively in thereference condition. To facilitate visual inspection, we used a color code (red correspondingto upregulation, green to downregulation) with thresholds corresponding to fold changes of1.8 (dark colors), 1.5 (medium) and 1.25 (light). The standard error for each measurement isgiven in parenthesis. For each probe set, the mean detection p-value from MAS5 analysis ofreference samples is reported in column 4 and cluster assignment in column 9..For each of the 1368 probe sets identified as stress responsive in our data analysis, wecalculated and reported in this table, for each stress condition, the mean ratio <Stresscondition> ≤ (AVClick here for file2O2 or c)tunicamycin we analyzed the distribution in biological processes (as defined by the GeneOntology (GO) database) of the genes selected by the SAM analysis (responsive genes) andcompared it to the same distribution for all the genes significantly detected on ourmicroarrays . We report here the significantly over- or under-represented(P < 0.005) biological process and the number of analysed and responsive genes found insidethese classes, for the different stress conditions. The p-value P associated to the nullhypothesis of no association with a binomial distribution hypothesis is given for each class,(only classes with P < 0.005 were retained). For clarity of the figure some redundant branchesof the tree were removed. Color codes for the classes: dark blue: classes present in the 3 stressresponses; yellow: classes present in the two oxidative stress responses; green: classes presentin paraquat and tunicamycin stress responses; light cyan: classes present in H2O2 andtunicamycin stress responses.34Color code for statistical analysis: orange: underrepresented class, blue: over-representedclass..For stresses induced by a) paraquat (5mM and 15mM experiments), b) HClick here for fileFor over or under-represented molecular functions classes we report here the number ofanalyzed (column 3) and responsive genes found inside the 7 groups of clusters A to G(columns 4 to 10, see text for details on the definition of these groups). A schematic responseto oxidative and ER stress of the genes included in these groups is given in the first two lines.The number of genes inside each group is given in line 3.A color code identifies cases when the number of genes differs statistically (p<0.005) from arandom distribution: orange: under-represented class, blue: over-represented class.Click here for fileFor over or under-represented biological process classes we report here the number ofanalyzed (column 3) and responsive genes found inside the 7 groups of clusters A to G(columns 4 to 10, see text for details on the definition of these groups). A schematic responseto oxidative and ER stress of the genes included in these groups is given in the first two lines.The number of genes inside each group is given in line 3.A color code identifies cases when the number of genes differs statistically (p<0.005)from arandom distribution: orange: under-represented class, blue: over-represented class.Click here for fileStress response for the peptidasesClick here for file2, old, infection, starvationO: expression data from the different experiments compared toour data.All: List of 26 genes which are responsive to at least 4 stresses in these independentexperimentsList of stress responsive genes detected in aging and hyperoxia, immune orstarvation stress experiments were compared with our data. Lines 1 to 3 indicate the numberof genes found to be repressed (-), induced (+) or either  in the different experiments.Lines 5 to 7 indicate the number of genes in each of these categories found among our 139735stress responsive genes (classes A to F) and the corresponding percentage from the initialnumber. In lines 8, 9 (respectively 10, 11) the same analysis is reported for genes included inthe A (respectively B) classes defined as common stress responsive classes in our analysis.Click here for fileRelationships to other stressesClick here for filek inside a cluster we calculated, for each stress condition, the mean ratio Rk = <Ln2 (AVstressi / AVref j )>i,j where AVstressi and AVref j denote the average value measured for the ith sample in the stress condition and the jth sample respectively in the reference condition. The mean of the Rk values provides a measurement of the mean intensity of variation for the genes inside a cluster, which is reported in this table. To facilitate visual inspection, we used a color code (red colors corresponding to upregulation, green colors to downregulation) with thresholds corresponding to fold changes of 1.8 (dark colors), 1.5 (medium) and 1.25 (light). The number N of probe sets in each cluster is also reported. From these values we identified groups of clusters (named from A to G) which present close behavior and were used for statistical functional analysis. Clusters corresponding to the common long term stress response (CLTSR) are outlined in red.Table 1: Stress response characteristics of clusterized genes.The 1368 probe sets retained after statistical analysis were submitted to a divisive clustering algorithm (SOTA) which predicted 19 clusters. For each probe set Click here for file p-value P associated to the null hypothesis of no association with a binomial distribution hypothesis is given for each class, (only classes with P<0.005 were retained). For clarity of the figure some redundant branches of the tree were removed. Color codes for the classes: dark blue: classes present in the 3 stress responses; yellow: classes present in the two oxidative stress responses; green: classes present in paraquat and tunicamycin stress responses; light cyan: classes present in H2O2 and tunicamycin stress responses.Color code for statistical analysis: orange: under-represented class, blue: over-represented class.Table 2: Functional analysis of stress responsive genes.For stresses induced by a) paraquat (5mM and 15mM experiments), b) H2O2 or c) tunicamycin we analyzed the distribution in functional classes (as defined by the Gene Ontology (GO) database) of the genes selected by the SAM analysis (responsive genes) and compared it to the same distribution for all the genes significantly detected on our microarrays . We report here the significantly over- or under-represented (P<0.005) molecular functions and the number of analysed and responsive genes found inside these classes, for the different stress conditions. TheClick here for fileTable 3: Analysis of stress responses for members of some functional classes.From the 1368 stress responsive probe sets we extracted the subsets associated with genes annotated in the GO database as a) glutathione transferases (GO:0004364), b) P450 cytochromes (from list at http://p450.antibes.inra.fr/) and c) proteasome component (GO:0004299). For each probe set k within one of these subsets, we calculated and reported in this table, for each stress condition, the mean ratio <Stress condition >k =<(AVstressi / AVref j)>i,j where AVstressi and AVrefj correspond to the average value measured for the ith sample in the stress condition and the jth sample respectively in the reference condition. To facilitate visual inspection, we used a color code (red corresponding to upregulation, green to downregulation) with thresholds corresponding to fold changes of 1.8 (dark colors), 1.5 (medium) and 1.25 (light). The standard error for each measurement is given in parenthesis. For each probe set, the mean detection p-value from MAS5 analysis of reference samples is reported in column 3 and cluster assignment in column 8. In column 9 additional information is reported for each class: in a) we indicate the GT class deduced from sequence comparison with human and mouse GTs and from [44] (D: delta, O: omega, T: theta, T2: distantly related to theta, Z: zeta); in b) the name of the genes are reported; in c) we indicate the proteasome subunit to which the genes defined in column 2 belong. Note that in c) a large number of genes not retained by SAM analysis (without cluster number) seem to be upregulated in P15 condition.Genes used for comparison between microarray and quantitative RT-PCR  are outlined in bold character.Click here for filew/+ reference flies. The results are presented in this table. Column 1 contains the tested genotype . The corresponding genotypes are described in column 6. The symbol for the gene affected is reported in column 2. Information from FlyBase about the allele used in this study is given in column 7 with a one character code: A: amorph; H: hypomorph; N: non complementation of deficiency; L: letal; R: recessive mutation; 5: insertion in the 5' regulatory region, 5'UTR or intron; C: insertion in the coding region. Column 8 indicates whether the tested line was outcrossed or not before the test. Column 4 is the result of a log-rank analysis of the second survival experiments shown in figure 4. A confidence index which refers to the mean of log10 (p-log-rank) for the three experiments is given in column 5. We considered that a strain had a significant effect on survival under oxidative stress conditions when this confidence index was lower than -3 and at least two experiments presented p-log-rank < 0.001. Under these stringent conditions 13 genotypes are shown to confer resistance (R) or sensitivity (S) to paraquat as indicated in column 3.Table 4: Analysis of mutant flies' resistance to paraquat-induced oxidative stress. Paraquat resistance of 29 mutant lines was assayed in three independent experiments as described in Fig. 4. The survival data were submitted to a log-rank statistical analysis by comparison with Click here for file


A few chromosomal regions can account for variation in all three aspects of the lateral plates, with one major locus contributing to most of the variation in lateral plate pattern and number. Genetic mapping and allelic complementation experiments show that the same major locus is responsible for the parallel evolution of armor plate reduction in two widely separated populations. These results suggest that a small number of genetic changes can produce major skeletal alterations in natural populations and that the same major locus is used repeatedly when similar traits evolve in different locations.How many genetic changes control the evolution of new traits in natural populations? Are the same genetic changes seen in cases of parallel evolution? Despite long-standing interest in these questions, they have been difficult to address, particularly in vertebrates. We have analyzed the genetic basis of natural variation in three different aspects of the skeletal armor of threespine sticklebacks  In stickleback populations separated over 10,000 years ago, dramatic morphological evolution appears to result from a relatively small number of genetic changes At the end of the last ice age, marine sticklebacks colonized newly formed freshwater environments throughout the Northern Hemisphere. Over the last 10,000 to 15,000 years, these fish have adapted to a wide range of new ecological conditions, giving rise to diverse populations with striking differences in morphology, physiology, and behavior  , partial (Aa), or low-plated (aa) fish  or low-plated (aa) fish, but no partials (A, a and B, b). In one of these models, both major loci contribute equally to plate phenotype, and the total number of A and B alleles determines whether fish develop as complete , partial , or low-plated fish   . The A aaa) fish . In othepartials . More co allele) . Additio allele) . All of  allele)  and haveTo directly analyze the number and location of genetic loci that control plate phenotypes, we crossed a completely plated marine fish with a low-plated benthic fish from Paxton Lake, British Columbia. Three hundred sixty progeny from a single F2 family (Cross 1) were examined in detail for the pattern, number, and size of lateral plates and then genotyped for the inheritance of different alleles at 160 polymorphic loci distributed across all linkage groups. The segregation of plate phenotypes was compared to the segregation of all genetic markers using quantitative trait loci (QTL) analysis MapQTL; . Signifi“AA” animals) showed the complete pattern, whereas fish that carried two alleles from the low morph grandparent in this region  showed the low pattern. In contrast, most fish with one allele from the complete grandparent and one allele from the low grandparent  developed as either complete or partial fish  4 was detected  or complete (AA) morphs, while the modifier loci affect the actual number of plates, particularly in Aa and aa animals.When total plate number was scored, the same major LG 4 chromosome region accounted for more than 75% of the total variance in plate number of F2 fish. Three additional QTL were detected that had significant effects on plate number in  animals . Increas animals . Increasw morphs A and 3D. animals B and 3C.The size of individual lateral plates varies significantly between different stickleback populations . AlthougAa genotype at the major locus on LG 4 were completely plated in this cross, suggesting that Aa fish develop more plates in Cross 2 than in Cross 1. This could be due to differences in the dominance relationship of the particular alleles at the LG 4 locus in the Friant population . Overall, these results suggest that both plate morph and plate number are controlled by similar chromosome regions in different populations.Some of the differences in previously published models of stickleback plate genetics could be due to different genetic mechanisms operating in different populations. To compare the genetic architecture of armor plate patterning in a separate population located over 1300 km from Paxton Lake, we crossed fish from an unusual stickleback population in Friant, California, which is largely dimorphic for complete and low fish with very few partials. A cross between a Friant complete and a Friant low-plated fish resulted in nearly equal numbers of complete and low progeny see , Cross 2pulation , or to mTo further test whether the same major locus on LG 4 controls armor plate reduction in both populations, we carried out genetic complementation crosses between two low female fish from Friant and one low male fish from Paxton Lake. All 84 progeny developed as low morphs, suggesting that the low-plated phenotype in both populations is likely to be due to the same major locus on LG 4.This study reports the first genomewide linkage mapping of lateral plate phenotypes in crosses between major stickleback plate morphs. Our results confirm previous suggestions that dramatic changes in lateral plate patterning can be controlled by one locus of major effect . This maPlate number within the complete, partial, and low morphs also varies between fish from different locations. Previous studies suggest that sticklebacks with small changes in plate number show differential survival when exposed to predators, suggesting that selection may fine tune the exact number of plates in different environments . We havePostglacial freshwater stickleback populations are thought to be derived from completely plated marine ancestors . At all Our mapping and complementation results suggest that the same major locus on LG 4 causes major changes in plate pattern in both the Paxton benthic and Friant populations. Phenotypic reduction of lateral plates almost certainly evolved separately in these different locations, given the geographic distance between them (over 1300 km), the presence of completely plated fish in the marine environment separating the sites, and previous studies showing that sticklebacks in nearby lakes have independent mitochondrial haplotypes . AdditioThe present study provides the first genetic mapping evidence that some of the chromosome regions controlling smaller quantitative variation in plate number may also be used repeatedly in different populations. The QTL on LG 26 in Cross 1 maps to a similar position as a QTL influencing plate number within low morph fish from Priest Lake, British Columbia . This QTRecent studies suggest that the same genes are also used repeatedly when pigmentation and larval cuticle phenotypes have evolved in parallel in different fly populations  or when Why might some genes be used preferentially when similar phenotypes evolve in parallel in wild populations? Alleles that cause plate reduction may already be present at low frequency in marine populations. In that case, parallel phenotypic evolution could occur by repeated selection for the same preexisting alleles in different freshwater locations. Alternatively, some genes may be particularly susceptible to de novo mutations, either because of the size or structure of coding and regulatory regions, or the presence of hotspots for recombination, insertion, or deletion. Finally, only a limited number of either old or new mutations may actually be capable of producing a specific phenotype without also causing deleterious effects on fitness. Mutations with the largest positive selection coefficients will be fixed most rapidly in evolving populations, and this may lead to parallel selection for mutations in the same genes in different populations.A major goal for future work will be to identify the actual genes and mutations that cause parallel evolution of adaptive traits in wild sticklebacks. This study identifies specific markers that are closely linked to chromosome regions that control the pattern, number, and size of lateral plates. With the recent development of BAC libraries and physical maps of the stickleback genome, it should be possible to use forward genetic approaches to identify the genes responsible for the repeated evolution of major morphological transformations in stickleback armor . CloningFor Cross 1, a wild-caught, completely plated marine female from Onnechikappu stream on the east coast of Hokkaido Island, Japan, was crossed to a wild-caught, low-plated benthic male from Paxton Lake, British Columbia. Both parents showed morphologies typical of the marine and benthic populations at their respective collecting sites. The specific populations were chosen because the large average body size of both parents and the estimated divergence between eastern and western Pacific Ocean fish  were expPitx1, Pitx2 (Stn220), and Tbx4 (Stn221), . A polymorphism within the 3′ UTR of the Bmp6 gene was genotyped using single strand conformation polymorphism analysis with MDE Gel Solution (BioWhittaker Molecular Applications, Rockland, Maine, United States). PCR bands were visualized using autoradiography. PCR conditions were the same as for the microsatellite markers except 2.5 mM MgCl2 and 10% DMSO were used. Primers for Bmp6 genotyping are: Bmp6F1: 5′ CCCGGTTTAAATCCTCATCC and Bmp6R1: 5′ AGGAGGTGATTGACAGCTCG.Genotyping of microsatellite markers was performed and analyzed essentially as described in Stn221),  and 13 nFish were stained with alizarin red to detect skeletal structures as described in Gac4174, Stn40, and Stn47 on LG 4; Stn210, Stn71, and Stn76 on LG 7; Stn211 and Stn121 on LG 10; and Stn218, Stn219, and Stn222 on LG 26). LOD scores between LG 4 markers and the major plate locus in Cross 2 were calculated using Map Manager v2.6.6 (All morphological traits in Cross 1 were analyzed with MapQTL 4.0  using thr v2.6.6 . Mean toBmp6 gene is AY547294 and for the 12 additional new microsatellites Stn 210–219, 222–223 are BV102488–BV102499.The GenBank accession numbers for the


Various scales have been used to assess palliative outcomes. But measurement can still be problematic and core components of measures have not been identified. This study aimed to determine the relationships between, and factorial structure of, three widely used scales among advanced cancer patients.Patients were recruited who received home or hospital palliative care services in the south of England. Hope, quality of life and palliative outcomes were assessed by patients in face to face interviews, using three previously established scales – a generic measure (EQoL), a palliative care specific measure (POS) and a measure of hope (Herth Hope Index). Analysis comprised: exploratory factor analysis of each individual scale, and all scales combined, and confirmatory factor analysis for model building and validation.Of 171 patients identified, 140 (81%) consented and completed first interviews; mean age was 71 years, 54% were women, 132 had cancer. In exploratory analysis of individual means, three out of the five factors in the EQoL explained 75% of its variability, four out of the 10 factors in POS explained 63% of its variability, and in the Hope Index, nine out of the 12 items explained 69% of its variability. When exploring the relative factorial structure of all three scales, five factors explained 56% of total combined variability. Confirmatory analysis reduced this to a model with four factors – self-sufficiency, positivity, symptoms and spiritual. Removal of the spiritual factor left a model with an improved goodness of fit and a measure with 11 items.We identified three factors which are important outcomes and would be simple to measure in clinical practice and research. Measurement of the effect of illness and its treatment on patients is now an accepted part of clinical trial design . Such meIn response to these difficulties, different measures have been developed and tested among patients receiving palliative and hospice care in different countries and contexts ,11. ThesSecondary analysis of a prospective observational study.Patients living in Chichester in the South of England receiving home or hospital palliative care support, from community, hospice or hospital palliative care team staff, were approached to take part in the study. Local research ethics committee approval was obtained. The local hospice was planning to develop a day care unit and patients were recruited during this period. A historical group was recruited before the day care unit was established. Consecutive consenting patients were recruited for both series. Patients were eligible if they were in the care of the hospice home care team, or neighbouring home care teams, that had access to the day care unit. Patients were excluded if they were judged by staff to be too ill for interview, if staff felt it would distress them, or they lived outside the catchment area of the hospice day unit. Two concurrent groups were recruited after day care was established – patients who did (Group AD) and did not (Group AN) choose to receive day care.Data was collected using trained interviewers. Interviews took place in the patients' preferred location, usually their own home. Interviewers explained the background to the study and used a structured schedule to collect clinical, demographic and use of service data. They then administered three scales. All were short, taking less than 10 minutes on average to complete, and were acceptable to advanced cancer patients. Scales were administered in the order they are listed below.1. EQoL EQ-5D. This generic questionnaire defines health in five dimensions: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. Each dimension is divided into three categories – whether the respondent has no problem, a moderate problem, or an extreme problem. A sixth item scores the person's overall health on a visual analogue (0 – 100) scale. The questionnaire has been validated and applied in a wide range of patient groups -16.2. Palliative care Outcome Scale (POS). This 10 item scale (plus an open question) was specifically developed and validated for palliative care and covers physical symptoms, patient and family or caregiver anxiety/fears and well being. The effect of the items on the daily life of the patient over the past three days is scored on a five-point Likert scale ranging from 'none' (0) to 'overwhelmingly' (4). For example: "over the past 3 days, have you been feeling anxious or worried about your illness or treatment? (0) not at all – (4) overwhelmingly" ,18. In t3. Herth Hope Index (Hope). This 12 item instrument assesses hope in adults in clinical settings, and is designed to assess change. For example: "I have a positive outlook toward life? strongly disagree (1) to strongly agree (4) -". Patients are asked how much they agree with the statement right now .Full details of the scales are shown in the Appendix 1 see . PatientData were analysed separately for the historical and concurrent (post day care) groups. The relative factorial structure of the three scales was explored in two steps. First, we performed a preliminary exploratory factor analysis (EFA) on each individual scale and on all the items of the three scales combined, using Principal Component Analysis on the historical sample. Second, we performed further exploration and final validation using confirmatory factor analysis (CFA) on the combined historical and concurrent samples. The EQS software  was used171 patients were identified and asked to take part in the study, 82 in the historical group, and 89 in the concurrent group (40 were AD). Of these, 140 (81%) were successfully approached, agreed to take part in the study, and completed the first interview. As shown in Table Summaries of the distribution of scores on the three instruments assessing hope, quality of life and palliative outcomes for the combined sample as well as results of the exploratory factor analysis, are shown in Table 4 see . On prinfirst principal factor, explaining 25% of the total variability of the combined scales, was determined by the three items of positivity contained in POS (share-feelings, feel-good and life worthwhile), together with all the Hope items and the anxiety items in both, POS and EQoL. The EQoL items "General Health" and items of "self-sufficiency", which constituted the most important factor of the EQoL scale, loaded together into the second factor, explaining 10% of the total variability of the combined scale. The third most important principal component, explaining 8% of the variability, comprised a general measure of patient anxiety (measured by both EQoL and POS), and family anxiety (measured by POS). The fourth principal component explained 6% of the variability and was defined by pain (measured by both EQoL and POS). The POS items "information" and "time-wasted" loaded together into the fifth factor, explaining only 6% of the total variability. In addition, the POS item "symptom control" did not load into any of these five factors and appeared to be acting independently.The exploratoty factor analysis of EQoL, POS and Hope on the historical sample alone gave rather consistent results for different extraction methods. Table 4 see  shows thOne of the extractions explored was principal axis factoring with a varimax rotation. This provided a better definition of the structure, with items loading more exclusively onto one of the factors. The first factor that we had obtained with the unrotated matrix essentially separated into two. The first axis, explaining 29% of the variability, was defined by the POS item "life worthwhile" loading with those items of Hope that reflected positivity and direction: positive outlook, goals, inner strength, loving, sense of direction, days have potential and life has value. The second axis, explained 11% of the variability and contained the anxiety items of EQoL and POS, the "feel good" and "share feeling" items of POS and the items of Hope that reflected pessimism or anxiety: "alone", "scared of future" and "past memories". The third factor was the EQoL general health and self-sufficiency and explained 7% of the variability. The fourth factor was solely defined by the pain items in EQoL and POS and explained 6% of the variability. The rest of the items played only a minor role. The POS items: practical matters, information and time wasted loading in a minor factor while the POS item "family-anxious" and the Hope items "tunnel" and "faith" disappeared altogether from the rotated matrix. Therefore, the model derived from this data is one in which the following items are omitted: POS2 and POS4 from the POS scale and Hope4 Hope5 from the Hope scale, leaving the rest of the items to define four major latent factors in the following manner: Spiritual, positivity, symptoms and self-sufficiency.family-anxious did not particularly disrupt the identifiability of the model but its presence reduced the goodness of the fit. Similarly, four Hope items were discarded – alone, light at the end of the tunnel, faith and scared of future – to give a total of seven items discarded. We arrived to two models, exhibited in Table Several models were explored and the most relevant are presented in Table In all the models presented, the matrix was positively definite, the test of independence was significant and the frequency distribution of the standardised residuals was symmetrical around 0. Models 3 and 4, not only omit the superfluous items but also separate the factors on clinical considerations. Both provide a huge improvement over the first two models. Model 3 allowed for a high correlation between the positivity and spiritual factors. More remarkably, the results show that Model 4, which disposes completely of the spiritual factor defined by the remaining Hope items, is an enormous improvement on the other models. The chi-square statistic was greatly reduced and almost reached the threshold indicating that no lack of fit was detected.An important next step in quality of life measurement is the translation of measurement into clinical practice to improve patient care . One impA factor which we entitled 'positivity' appeared to be highly important among people with advanced illness. Spirituality/positivity has also been related to communication outcomes . In the When attempting to develop a reduced scale we identified two models, one with four factors (19 items) and another one with three factors which provided a good fit (11 items). All the Herth Hope items are excluded from the latter model, which captured self-sufficiency, symptoms and positivity. Positivity appeared very close to spirituality, as measured by the Hope index. Further work is needed to determine the relationship of these questions with specific spirituality scales -29.Symptom control was absent in the structures obtained by EFA. We suspected that this was because this item was not a structured question designed for any specific symptom, but elicited in an open way what symptoms had troubled the patient. In the CFA this item loaded with the General Health status factor. Measures which specifically address symptoms are available and have been used in palliative care populations ,30. WorkSpecial attention was given to the three items forming the practical factor in POS. The information item in POS (POS-5) was constant in the concurrent sample and only a few patients in the historical sample recorded non zero for this item. The time wasted item of POS was also essentially constant. It may be that the grading for these items needs to be reviewed to ensure that they give greater sensitivity to change. In our analysis this could have contributed to the poor fit shown when attempting to fit a general POS factor containing these items. It may also be because that none of the three items is an indicator of QOL; they are rather items of the provision of health care. In this study all of the patients were in receipt of a wide range of services, including specialist care teams and their practical needs were likely to have been met. Research among patients in different circumstances has shown deficiencies in these aspects of care ,33.The POS item family-anxious was intriguing. It did not disrupt the validity of the model but if kept and loaded in the positivity factor, it reduced the goodness of fit. This item also showed erratic behaviour in the exploratory factor analysis. Family anxiety may be related to a large number of factors, some of which are determined by the circumstances of the patient and some of which are determined by other events. Family needs often increase as patients deteriorate and are difficult to resolve. Further work is required directly targeting the needs of families .Our study was limited by the comparison of these scales among patients in one setting: we do not know if similar results would be found if patients were not in receipt of specialist palliative care available in the United Kingdom. However, our findings are consistent with other work assessing quality of life measurement in palliative care and in advanced cancer . CorrelaOur reduced model suggests that clinicians may sensibly target quality of life measurement in advanced cancer towards three main components, positivity, self-sufficiency, and symptoms. This might be achieved by the 12 items in model 4 of our factor analysis. Such a scale would be short and simple to use in both clinical practice and research, improving the measurement of outcomes in this population.Appendix 1: Full details of the three palliative care outcome scalesClick here for fileTable 4: Summary Statistics and Principal Component Analysis (unrotated) of the three scales (POS-EQoL-Hope) in the historical sample.Click here for file


Alternative splicing (AS) is now considered as a major actor in transcriptome/proteome diversity and it cannot be neglected in the annotation process of a new genome. Despite considerable progresses in term of accuracy in computational gene prediction, the ability to reliably predict AS variants when there is local experimental evidence of it remains an open challenge for gene finders.ab initio gene prediction. This method relies on the analysis of genomically aligned transcript sequences (ESTs and/or cDNAs), and has been implemented in the dynamic programming algorithm of the graph-based gene finder EuGÈNE. Given a genomic sequence and a set of aligned transcripts, this new version identifies the set of transcripts carrying evidence of alternative splicing events, and provides, in addition to the classical optimal gene prediction, alternative optimal predictions (among those which are consistent with the AS events detected). This allows for multiple annotations of a single gene in a way such that each predicted variant is supported by a transcript evidence (but not necessarily with a full-length coverage).We have used a new integrative approach that allows to incorporate AS detection into ab initio gene finding offers an ideal integration of alternatively spliced gene prediction inside a single annotation pipeline.This automatic combination of experimental data analysis and  Alternative splicing (AS) is a biological process that occurs during the maturation step of a pre-mRNA, allowing the production of different mature mRNA variants from a unique transcription unit. AS is known to play a key role in the regulation of gene expression and transcriptome/proteome diversity . First cintrinsic or ab initio, the only type of information used for gene prediction lies in the statistical properties of the various gene elements . On the contrary, so-called extrinsic approaches essentially rely on the existence of similarities between the sequence to annotate and other known sequences (either proteins, transcripts or other genomic sequences). Several existing gene finding tools are essentially intrinsic (or ab initio): this is the case for Genscan [k best predictions. This approach has been applied eg. in HMMgene or in FGENES-M (unpub.). Another way to obtain suboptimal solutions from a HMM is to do HMM sampling [a posteriori likelihood is larger than a given threshold.In the context of structural gene prediction, two classes of approaches are usually considered. In the first approach, usually denoted as  Genscan , HMMgene Genscan  or SLAM  Genscan . For sucsampling . This meintrinsic approaches cannot take into account transcript evidences, they suffer from two major problems of sensibility and specificity:In addition to the fact that all these exclusively eg. shifts from a strong to a weak or a non-consensus splice site or shows a complete coding exon skipping event, it is quite unlikely that the probability will remain in the neighborhood of the optimum since it will not be able to incorporate the corresponding splicing or coding score.First of all, these methods assume that predictions representing AS variants should have a probability which is very close to the optimal probability according to the underlying gene model. This is however quite arguable, especially when the alternative structure significantly differs from the optimal one. Actually, when an AS variant in silico false positives. To perform this, and as long as AS sites dedicated prediction tools are unavailable, the probability of a prediction alone cannot be sufficient and additional evidence is required.Moreover, a strong specificity problem has been observed for this approach. Since a very large number of alternative predictions can always be produced for any sequence, it is essential to be able to distinguish those reflecting real AS variants from intrinsic approach, the analysis of experimental data can provide useful information. More specifically, sequences of mature transcripts resulting from AS provide reliable evidence of the existence of the AS event. Large scale studies have already been undertaken to detect AS evidences from transcript alignments and to collect them in databases such as eg. HASDB [extrinsic annotation tools include GeneSeqer [In opposition to the purely g. HASDB , ASDB [9g. HASDB , ASD [11g. HASDB  or ProSpg. HASDB . Some soeneSeqer , ASPic [eneSeqer , TAP [16eneSeqer , and PASeneSeqer . Except eneSeqer , has beeeneSeqer  to identintrinsic methods, extrinsic approaches take advantage of transcript information. However, they also suffer from some limitations : first they entirely depend on the availability of transcribed sequences which bounds their sensitivity. With little exceptions (like TAP that exploits genomic sequence properties to identify gene boundaries, including eg. a polyA site scanning step, or GeneSeqer, that contains an intrinsic splice sites scoring method), they cannot predict a splice site if it is not represented in a transcript-to-genome alignment and therefore require a total coverage of each gene with all exon-intron boundaries. This can be problematic considering the ESTs fragmented nature. Moreover, when such methods can take advantage of a total gene coverage, the CDS localization remains to be done and the pure transcript predictions may not respect elementary coding gene properties (such as the presence of an ORF w.r.t. a given frame). Furthermore, overlapping transcripts are sometimes assumed to come from the same mature mRNA and are therefore merged. This may lead to the fusion of two overlapping transcripts coming from exclusive inconsistent mRNA variants, thus forcing the prediction to respect a chimeric virtual assembly.Unlike intrinsic and extrinsic information are needed inside an annotation pipeline [Finally, and because experimental transcripts cannot exist for every existing gene, both pipeline . The prepipeline . A fullypipeline  seems topipeline ,18. The intrinsic/extrinsic integrative annotation method with the following aims:To extend the domain of application of gene prediction to alternatively spliced gene structure prediction, we have designed an • For a given genomic sequence, an optimal gene structure prediction is produced, as usual.• In addition to this optimal prediction, for every transcript sequence providing evidence of AS, an optimal prediction consistent with this splicing form is also provided.• Each additional or alternative gene structure prediction has to be supported by some biological evidence.• Full-length transcript coverage is not required for a complete gene structure identification.• Each prediction satisfies the usual constraints on gene structure. A correct proteic coding gene is defined by a succession of one or more exons separated by introns flanked by splice sites. It contains a CDS between a start and a stop codon, and no in-frame stop in coding exons.intrinsic and extrinsic approaches in an integrative system allowing for AS detection based on the analysis of genomic aligned transcript sequences. The method has been implemented inside EuGÈNE-M, a new version of the Arabidopsis thaliana EuGÈNE gene finder [Our aim is to combine the advantages of the e finder ,23, and spl7 Arabidopsis thaliana gene. This gene codes for the squamosa promoter-binding protein-like 7, has 10 exons and two known alternative mature mRNA variants, both supported by a distinct full-length cDNA  show incompatible alignments (see Methods), EuGÈNE-M computes two additional predictions, each being consistent with one of them. The first alternative prediction is the same as the optimal one and corresponds to one variant; the second corresponds to the other variant.Given only the genomically ESTs alignments, we applied EuGÈNE-M on the genomic sequence containing the Arabidopsis thaliana curated genes recently used in the assessment of GeneSeqer [alternative predictions, that were carefully analyzed. This is summarized in Table in silico expertise, require experimental verifications to be confirmed. If we assume the 7 remaining genes are effectively subject to AS, this yield to an AS rate of ~4.2%, a ratio in the same order as previously estimated, from 1.5% [For a more extensive test, we applied EuGÈNE-M on AraSet , a data eneSeqer . Since EeneSeqer , the aimrom 1.5%  to 6.5% rom 1.5% ).Arabidopsis thaliana Genome Database AtGDB [In the recent assessment of GeneSeqer on this AraSet data set, only three AS cases were reported . Howeverse AtGDB ,28. We nse AtGDB ,29 and ose AtGDB ,30. OnlyA. thaliana than in Homo sapiens. Nevertheless, this AS ratio estimation is expected to increase in the future with the growth of transcript data availability. Another interesting point is the nature of the variants: on this gene set, the majority of AS cases involves a simple acceptor or donor alternative splice site. Notice however that since EuGÈNE-M's underlying model allows arbitrary alternative gene structure to be predicted, it is not limited to the prediction of such simple AS events and can perfectly cope with complex AS events, as found in mammals. This methodology can also be integrated in other existing gene finders where the score of a gene structure is defined as the sum of elementary scores of the signals and nucleotides involved in the gene structure (this includes HMM-based gene finders).The analysis of these AS cases confirms that AS seems to be much less frequent in intrinsic and an extrinsic process to incorporate AS detection into ab initio gene finding. We showed that this method allows the discovery of new alternative spliced genes, with the reliability of extrinsic annotation and the potential exhaustiveness of ab initio gene prediction.In this paper we have presented a new method to deal with alternative splicing in annotation and gene prediction. This integrative approach combines the advantages of an The process that goes from the original genomic sequence and associated aligned transcripts to the AS prediction is composed of three steps which we rapidly describe here :• first, the set of genomically aligned transcripts is analysed to detect AS evidences on the basis of splicing inconsistency between transcripts variants.• Then, the graph-model used in EuGÈNE to model potential gene structures is modified to take into account these aligned transcripts. For each transcript variant, the graph used in EuGÈNE for gene structure prediction is connected to an additional parallel graph subunit where local constraints are injected according to the exon-intron information provided by the corresponding transcript alignment.• Finally, an extended version of the dynamic programming algorithm used for obtaining an optimal prediction allows to identify, for each graph subunit, the best prediction consistent with the corresponding transcript alignment.Since EuGÈNE already exploits transcripts information to improve the gene prediction process , the AS a priori imposed by the method. Transcript sequences in our analysis were extracted from the A. thaliana section of dbEST [Transcript sequences are first aligned against the genomic sequence using a spliced alignment tool. The choice of the source transcript database and the alignment tool is not of dbEST  .A first filtering step is performed on the basis of the transcript sequence and alignment quality. To be considered, an alignment has to satisfy some constraints defined by filtering parameters. For A is labeled as included in B if and only if for each genomic position of A the same genomic position in B shares the same alignment information (either gap or match). Every transcript included in another transcript is ignored by default. Transcript alignments A and B are labeled as incompatible if and only if there is a genomic position for which both ESTs are informative and give an inconsistent information, that is a gap (representative of the presence of an intron) is faced with a match (representative of the presence of an exon, coding or not). Examples of incompatible ESTs are displayed in Figure incompatible after all pairwise comparisons. Considering orientation of ESTs, the information on the clone-sequencing orientation that can be found with ESTs is totally ignored in this filtering process because of its unreliability. In practice, spliced EST can be reliably oriented by looking for splice sites on the hit-match frontier of the EST alignments and by choosing the strand for which such splice sites exist. The parameters of these two automatic filtering steps can be modified by the user through a simple text file. We will denote the resulting transcript alignments kept as alternative transcripts.The second filtering step depends on the relation between transcript alignments. To detect AS evidences, every pair of overlapping transcript alignments is analyzed. We consider two special types of pairwise relation : a transcript alignment EuGÈNE is a gene finding software based on a directed acyclic graph gene model . For eack tracks that represent the possible annotations that can be attributed to each nucleotide (coding, intronic, intergenic and UTR, with specific strand and frame). Let ℓ be the genomic sequence length. For a given nucleotide's position i with (1 <i < ℓ) and for each track j with (1 <j <k) the two flanking vertices  and  are defined. Two edges are also built : a contents edge  linking  to  and a transition edge  linking  to   is present, as illustrated in Figure  and  are added at the extremities of the graph. They are respectively connected to all  and all . Initially, all edges are oriented from left (5') to right (3'). It is easy to see that all possible gene structure can be represented by a path from  to .Each path through the graph represents a potential gene structure prediction for the genomic sequence Figure . The gra  Figure . Additioc and t are penalized respectively by weights Wc and Wt according to a weighting function characterized by parameters specifically set for the corresponding source of evidence. The set of parameters is optimized on a learning dataset by maximizing the overall accuracy of the software. For more information about the weighting methods, please refer to [The weight of a path is the sum of all the weights of the edges in the path. The edges are weighted according to the evidences used. EuGÈNE can combine several sources of evidence such as probabilistic coding models, output of splice site or start codon prediction software and sequence similarities with transcripts, proteins, or other genomic sequences . Contentrefer to .A transcript-to-genome alignment can easily be taken into account by weighting the appropriate edges of the graph. To favor a gene prediction in the alignment region, the intergenic track edges included in this region can be penalized by increasing their weight. More finely, the exon and the intron tracks edges can also be penalized at all positions involved respectively in a gap and in a match in the alignment. Thus, all gene structure prediction inconsistent with the transcript alignment information tends to be penalized. More drastically, it is possible to force the prediction to be consistent with the alignment by applying infinite penalty weights. Note that there are several such predictions since the start codon used is unknown and the transcript may be incomplete. a variable  which contains the weight of the optimal path from  to  and a variable  which contains the vertex that precedes  in this optimal path. The weight of this path can be computed recursively from 5' to 3' as:To identify the optimal path defined by the lowest weight, EuGÈNE uses a dynamic programming algorithm inspired from Bellman's shortest-path algorithm , also us that minimizes this value provides the previous . At vertex , the best path is retrieved by a simple backtracing procedure through all π. This algorithm is linear in time and space in the length of the sequence (O(ℓ) complexity). It is important to note that the same algorithm can be used in a backward version (from  to ), by computing at each vertex  the weight  of the best path from  to  as .A short example is displayed in Figure alternative transcript genomic alignment, any prediction which is optimal among all the predictions that are consistent with the alignment evidence will be called an alternative prediction. Given the set of the previously detected alternative transcripts, we want EuGÈNE-M to produce a set of alternative predictions such that every alternative transcript has a corresponding prediction in this set. A simple way to produce such an alternative prediction would be to inject the exon-intron structure information given by the transcript alignment into the graph as described above , and then to execute EuGÈNE on the resulting graph. However, obtaining all alternative predictions would require one execution for each alternative transcript. n being the number of transcripts and l the genomic sequence length, this would result in a O(ln) time complexity, which is not appropriate for long genomic sequences and numerous transcripts.Given an Hopefully, this complexity can be drastically reduced. The general idea to achieve a realistic complexity is to duplicate the subsection of the graph region involved in an alignment to create a so called local "Parallel Graph Subunit" (PGS), connected to the main graph at its extremities. Each alignment information is taken into account as constraints in the corresponding PGS, in such a way that finding the optimal path going through the PGS provides a corresponding optimal alternative prediction.g to h on the genomic sequence, the entire subsection of the graph between g and h is duplicated to create a Parallel Graph Subunit (PGS)  Figure . This PG to  and applies the recursive formula described above to compute all , branching into each PGS  are reversed, and the backward version of the same shortest-path algorithm is used from A, if we now consider the vertices  at the rightmost extremity of A, then the weight of an optimal path that goes from  to  through A can be computed as . From the given vertex, backtracing in both directions provides an optimal path that represents an optimal prediction in accordance with the transcript alignment evidence.For a given PGS alternative prediction the name of the corresponding transcript is mentioned and the region that differs from the optimal prediction is displayed. Besides, if several predictions are identical (regarding their predicted CDS only, UTR length differences being ignored), a single representative is displayed, along with the list of its associated transcripts.Predictions are produced in the standard GFF format. The entire optimal annotation is first displayed, followed by the alternative ones. To enhance the readability and to avoid redundancy, for each incompatible transcripts identification requires O(n2) pairwise comparisons. Each comparison is itself linear in the maximum number of introns in the transcript compared, which is typically bounded by a small constant and the whole process is therefore in O(n2).The initial filtering and L is the total nucleic sequences length , the weights of all  predictions can be computed in O(L).The step that corresponds to the two dynamic programming scans (application of the recursive formula) requires a time and space complexity which is linear in the size of the input data. Indeed, if For the backtracing and output step, since each alternative prediction has to be displayed in the region where it differs from the optimal one, and because this can extend beyond the alignment region, it is not possible to obtain an algorithm which is linear in the size of the input. However, it is possible to reach a linear complexity in the size of the output. This can be done by a simple modification of the standard backtracing procedure to avoid a full backtrace for each prediction. This is yet not implemented in the current version of the software.A typical run of EuGÈNE-M on an AMD Athlon 1.7 GHz takes 47 sec. for a 500 kb BAC .SF designed and implemented the filtering algorithm as well as the double dynamic programming algorithm for AS prediction. He also ran the experiments on the Araset dataset. TS directed the research. All authors read and approved the final manuscript.EuGène's predictions on AraSet. The additional file (SupplementaryFiles.tar.gz) contains gene structure predictions in the standard GFF text format for every AraSet sequence. ESTs detected as Incompatible by the method are displayed at the top of each prediction (if any).Click here for file


Drosophila robusta have been characterized by well-defined latitudinal, longitudinal, and elevational clines, but – until at least ten years ago – stable, local population frequencies. Recent biogeographical analyses indicate that D. robusta invaded North America from southeast Asia and has persisted in eastern temperate forests for at least 20–25 my without speciating. The abundant chromosome polymorphisms found across the range of D. robusta are thus likely to be relatively ancient, having accumulated over many well known climatic cycles in North America. Sufficient long-term data are now available such that we can now gauge the rate of these evolutionary changes in natural populations due to environmental change.Studied since the early 1940's, chromosomal polymorphisms in the deciduous woods species Recent local collections have revealed significant changes in the frequencies of several chromosomal forms. New data presented here extend the range of these changes to six states, three in the northeastern United States and three west of the Mississippi River. These data reinforce recent directional changes in which the frequencies of three gene arrangements have reached percentage levels typical of distant southern populations consistent with regional climatic changes. Another gene arrangement has been steadily decreasing in frequency at a number of the sites studied. Meteorological records from 1945 to 2003 indicate temperature increases at all study sites, particularly average minimum air temperatures.D. robusta are evolutionary responses to environmental change. Since these chromosomes are known to be sensitive to ambient temperature, regional climatic shifts associated with global warming are likely to be responsible.Observation of parallel genetic flux suggests that these long-term temporal frequency shifts in widely disparate populations of  AVTEMP and MAXTMP varied significantly from site to site in different years, but MINTMP significantly increased over the 58-year period (P < 0.0001) with no heterogeneity among sites. Thus, the most consistent change in climate across sites in this study was due to significant increases in minimum monthly temperatures.Regional patterns of climate change were revealed from ANCOVA analysis of temperature and precipitation data from 1945 – 2003. No long term tends were detected for precipitation except for a significant increase in Central Park, NY (data available from the authors). Significant long-term temperature changes were apparent at all six 2003 study sites . Causes for these decreases are obscure, but in New Jersey may be due to the exaggerated temperature fluctuations in the early 1990's . X chromosome combinations 1S and S1 show substantial frequency changes with time, with S1 doing so in a site-specific manner , tends to be higher in southern populations than in northern ones [Like 2L-1 and 3R-1, the frequency of gene arrangement XL , and 3R-1 from South Carolina, Alabama, and Mississippi were released in midsummer in the Englewood, NJ woods. In other years the released flies carried XL-1, 2L-3, and 3R from Minnesota and Michigan. Despite evidence of hybridization of the introduced flies with the local population, in each case the following spring saw return to frequencies of the previous year.Evidence of strong natural selection maintaining earlier observed genetic stability has also come from perturbation experiments in the wild . At fourTL and PP of D. pseudoobscura mentioned above, since high frequencies of inversion homozygotes would undermine the structural stability of the new adaptive complex. The sudden rises of D. robusta X-chromosome combination S1 in New York and New Jersey and of S2 in Missouri could be cases in point, but it would not explain the near doubling of S1 in Arkansas from a 35% base between 2001 and 2003 nor the three most consistent directional changes, those of 3R-1, 2L-1, and XL , Little Falls and Ridgefield for Englewood (r = 0.84 – 0.98, P < 0.0001); see Fig. Temperature and precipitation data were obtained online from the National Climatic Data Center ML collected the Allentown, New York, and New Jersey flies and carried out the chromosomal analyses. WJE contributed the Fayetteville flies. Both authors contributed to planning the study, analyzing the data, and writing and revising the manuscript.


Ever since the early Greeks recast humans as the center of the universe and remade God in their own image, Western philosophers and poets have grappled with the limits of human mortality. Philosophers found relief from Keats's “unwilling sleep” by dividing human existence into body and soul and asserting that the true essence of humanity lies in the immortal soul, not in the body. Ironically, as this decidedly nonscientific subject has lost favor with modern-day philosophers, it has captured the imagination of scientists. But, for now at least, the interest is in prolonging life rather than escaping mortality.SIR2. Now, Brian Kennedy and colleagues show that calorie restriction and SIR2 promote longevity through distinct genetic pathways—and that aging in yeast and higher organisms may be more similar than previously thought.Over the past twenty years, mounting evidence from a wide range of organisms indicates that a longer life awaits those who eat less. In yeast, calories can be restricted directly, by limiting yeast's glucose supply, or indirectly, by inhibiting yeast's ability to metabolize glucose. Either way, many studies have suggested that the increased longevity associated with calorie restriction is linked to increased activity of a gene called FOB1 gene, for example, dramatically reduce ERC levels and increase the reproductive life span of cells by 30%–40%. In contrast, mutations that disrupt SIR2 increase ERC levels and cut life span in half, while increasing SIR2 activity increases life span by 30%–40%.One of the causes of aging in yeast is the accumulation of coiled bits of DNA, called extrachromosomal ribosomal DNA circles (ERCs), in the nucleus of a mother cell . An overabundance of these rDNA circles wreaks havoc on a cell and eventually kills it. Genetic mutations that reduce their levels are linked to increased life span. Mutations that disrupt the SIR2, and the accumulation of ERCs. The idea is that calorie restriction somehow activates the protein encoded by SIR2, which in turn decreases ERC accumulation. Now, Kennedy's team has found that the combination of calorie restriction and FOB1 mutation increases life span more than either approach does alone. This finding was unexpected because previous studies showed that combining increased SIR2 activity with FOB1 deletion mutations did not extend life span. If calorie restriction extends life through SIR2, then combining either caloric restriction or SIR2 overexpression with FOB1 mutations should produce the same result.In previous experiments, several groups have identified a link between calorie restriction, SIR2. In support of this view, caloric restriction enhances life span to a greater extent in FOB1 mutants lacking SIR2 than in FOB1 mutants with an intact SIR2 gene. This and other genetic experiments indicate that calorie restriction does not always work through SIR2.This contradiction raised the possibility that calorie restriction operates through another mechanism, independent of FOB1 mutants is not related to ERCs, because these yeast strains already have low ERC levels. Since calorie restriction is the only demonstrated approach to increasing life span in a diverse range of organisms, including mammals, and since there's no evidence that ERCs affect the aging of any organism besides yeast, these results bode well for understanding how calorie restriction works in higher organisms. And the finding that calorie restriction and SIR2 operate through genetically distinct pathways in yeast, the authors conclude, suggests that certain aspects of both pathways might have been conserved through evolution. Working out the details of these pathways in yeast is the first step toward understanding which, if any, of these components might enhance longevity in humans. Of course, as any student of Greek mythology knows, longevity without eternal youth comes with a price.That suggests, the authors explain, that calorie restriction functions either by regulating ERC levels or by some still unknown molecular pathway. They conclude that the enhanced longevity seen in calorie-restricted