Many different things combine to cause epidemics of disease. Among these factors are the characteristics of the infecting organism, the resistance of the host, and, as is increasingly realized, climatic conditions.El Niño, the best known climatic disturbance, is caused by a warming of the Pacific Ocean, which then affects the climate globally. Previous work has suggested that this recurring phenomenon can have a profound effect on the incidence of many diseases, including dengue, malaria, and diarrheal diseases.PLoS Medicine, Sultan and colleagues from a climate research institute and an infectious diseases center in France looked at the relation between climate and meningitis outbreaks in Mali in West Africa, a region that every year between February and May sees devastating epidemics of meningococcal meningitis affecting up to 200,000 people. The most important recurring climatic event in this region is a dry wind, known as the Harmattan, that blows throughout the winter, causing a drop in humidity and the production of vast quantities of dust.In a paper in this month's What the authors found was that over the years 1994–2002, the week of the onset of the yearly meningitis epidemic came at around the same time as the peak of one measure of the wind—the sixth week of the year. As Pascual and Dobson say in their Perspective article on this study, “Sultan and colleagues' study is exceptional in that it illustrates a clear relationship between an external environmental variable and the initiation of disease outbreaks.”How do climatic changes influence disease? In some cases, such as the role of flooding in spreading a waterborne disease, the causes are perhaps obvious, but why should a dry wind affect disease incidence? Previous works have suggested that the climate can work in a number of ways, by influencing the life cycle of both disease vectors and the disease-causing organism, and, as here perhaps, by affecting the resistance of the host. Sultan and colleagues speculate that the drying effects of the wind on the mucous membranes could increase the chances of the organism getting established in the human host. Whatever the causes, one very useful feature of climate is that, once the patterns are understood, they can often be predicted.A way of predicting these meningitis epidemics could be enormously useful. Sultan and colleagues looked at only a few years, but if these findings are confirmed over a longer time period, they could make preparing for an epidemic much more efficient.


Nutrition & Metabolism (N&M) will publish articles that integrate nutrition with biochemistry and molecular biology. The open access process is chosen to provide rapid and accessible dissemination of new results and perspectives in a field that is of great current interest. Manuscripts in all areas of nutritional biochemistry will be considered but three areas of particular interest are lipoprotein metabolism, amino acids as metabolic signals, and the effect of macronutrient composition of diet on health. The need for the journal is identified in the epidemic of obesity, diabetes, dyslipidemias and related diseases, and a sudden increase in popular diets, as well as renewed interest in intermediary metabolism.A new Open Access journal,  Nutrition & Metabolism (N&M) include 1) an awareness of an epidemic of obesity, diabetes, dyslipidemias and related diseases, 2) a sudden increase in the popularity of diets, such as low carbohydrate diets, to achieve weight loss and combat diabetes, and 3) a renewed interest in intermediary metabolism accompanied by the development of new tools and techniques for genomic and metabolic analysis.Recent events that provide the rationale for a new Open Access journal, ) and similar motions exist in the US congress [N&M feel that, at this point, the burden of proof is on proponents of perpetuating the current system. We are, however, not doctrinaire on this point and believe one should pay for a service if it is valuable. Beyond information, printed collections provide convenience and we intend to offer bound copies of articles on individual topics as the journal proceeds.With the considerable activity shown in these areas, rapid and easily accessible dissemination of new information is clearly valuable. Whereas articles in existing journals do discuss intermediary metabolism in a nutritional context, there is a need for a unique and explicit focus for this discipline. In addition, it is precisely because publications in nutritional biochemistry are spread over such a large number of existing journals, few libraries and almost no individual can subscribe to all. It is in areas like this that free, open access becomes important. There is a large published debate on open access (see, e.g. ). Most rcongress ,3. The eN&M has its own strengths and interests as indicated by the board of editors . Three areas of particular interest are lipoprotein metabolism, amino acids as metabolic signals, and the effect of macronutrient composition of diet on health. This is reflected in our opening research articles by Darimont, et al. on the control of obesity and lipid structure by adrenergic systems, and by Volek, et al. on the effectiveness of low carbohydrate diets, and differential effects on fat and lean mass.Nutrition and metabolism is a broad field and we welcome submissions from all areas of nutrition and related biochemistry. Like any journal, however, , while not recommending any particular diet, highlighted many of the relevant issues in macronutrient control of metabolism. In our initial publications, contributors to the conference will provide reviews of the various topics covered. In the first posting, Klaas Westerterp summarizes the importance of macronutrient composition in thermogenesis, and Stephen Phinney discusses the impact of ketogenic diets on physical performance. Kimball and Jefferson review the regulation of mRNA translation in general. The article provides a nice overview of various mechanisms involved in the control of protein synthesis when amino acids become limiting. Perhaps the most important from a practical standpoint, Nuttall and Gannon summarize potential benefits of higher protein diets in diabetes.The sudden popularity of low carbohydrate diets is one of the most remarkable phenomena in nutrition today. A recent editorial by Walter Willett points out how important it is that we understand them . SimilarN&M is free. Articles are included in PubMed and archived in PubMed Central. Online submissions can be made at .Nutrition & Metabolism welcomes contributions in all areas of research in which nutrition interacts with biochemistry and molecular biology. Emphasis will be on the molecular, biochemical, and physiologic understanding of various metabolic pathways. The journal will publish Original Research, Reviews, Commentaries and Perspectives, Brief Communications, Methods and Book Reviews. Access to all articles in


E. Coli genes is affected by changes in the level of chromosome supercoiling, suggesting that supercoiling transmits regulatory signals from the environment to many cellular pathways.Microarray analysis shows that transcription of 306  Escherichia coli is maintained in a negatively supercoiled state, and supercoiling levels are affected by growth phase and a variety of environmental stimuli. In turn, supercoiling influences local DNA structure and can affect gene expression. We used microarrays representing nearly the entire genome of Escherichia coli MG1655 to examine the dynamics of chromosome structure.The chromosome of We measured the transcriptional response to a loss of supercoiling caused either by genetic impairment of a topoisomerase or addition of specific topoisomerase inhibitors during log-phase growth and identified genes whose changes are statistically significant. Transcription of 7% of the genome (306 genes) was rapidly and reproducibly affected by changes in the level of supercoiling; the expression of 106 genes increased upon chromosome relaxation and the expression of 200 decreased. These changes are most likely to be direct effects, as the kinetics of their induction or repression closely follow the kinetics of DNA relaxation in the cells. Unexpectedly, the genes induced by relaxation have a significantly enriched AT content in both upstream and coding regions.The 306 supercoiling-sensitive genes are functionally diverse and widely dispersed throughout the chromosome. We propose that supercoiling acts as a second messenger that transmits information about the environment to many regulatory networks in the cell. Escherichia coli is a circular double-stranded DNA molecule that is maintained in a negatively supercoiled state. Supercoiling induces torsional tension in the DNA, and thus can influence processes that involve the opening of the double helix, such as replication initiation [The chromosome of itiation , DNA looitiation  and tranitiation . A numbeitiation ), suggesE. coli, supercoiling is maintained at a precise range during log phase growth by the topoisomerases DNA gyrase, topoisomerase I (topo I), and topoisomerase IV (topo IV) [gyrA and gyrB), and for topo I (topA). Relaxation of the chromosome upregulates gyrA and gyrB and downregulates topA as a form of feedback control [fis , ilvG (an amino-acid synthase subunit) and cydAB (an oxidase involved in aerobic respiration), has been found to be sensitive to supercoiling [In topo IV) -7. DNA gtopo IV) -10, whertopo IV) . Togethetopo IV) ,13. The  control -16. Thisrcoiling -19, suggrcoiling . If supercoiling ,17,18.E. coli K-12 genome to systematically identify those genes that respond to relaxation of the chromosome during log-phase growth. We used antibiotics and mutations in the topoisomerase genes to change supercoiling levels by independent mechanisms and thus discerned the general effects of chromosome relaxation. We classify supercoiling-sensitive genes, or SSGs, according to their response to DNA relaxation. Therefore, we call 'relaxation-induced genes' those genes whose expression is increased upon DNA relaxation, and 'relaxation-repressed genes' those whose expression is repressed by DNA relaxation.In this study, we used cDNA microarrays ,22 repregyrA or topA, and their expression patterns correlated with the supercoiling level of a reporter plasmid in the cells. SSG transcripts have the same rates of RNA decay as non-SSG transcripts, and thus the changes in expression were due to a change in the rate of RNA synthesis, rather than RNA decay.An extensive statistical analysis of our experimental results revealed 200 relaxation-repressed genes and 106 relaxation-induced genes; in total, around 7% of all genes in the genome were found to be significantly affected by supercoiling changes. Many of these genes are more sensitive to supercoiling than p < 0.0001) AT-rich in their upstream sequences, and also have AT-rich coding regions. Relaxation-repressed genes had a corresponding preference for GC-rich sequences. The SSGs are dispersed throughout the chromosome, and fall into many different functional classes. We propose that the large number and functional diversity of the SSGs reflects the role of supercoiling as a second messenger that responds to environmental changes and feeds into different regulatory networks.We discovered that the sequences of the relaxation-induced genes are significantly (tetA gene in pBR322 anchors this plasmid to the membrane [We sought to determine the genes that are activated or repressed by relaxation of the (-) supercoils in the chromosome. To isolate the expression changes due to the loss of supercoiling from those due to the experimental approach, we used three different methods to relax the chromosome. In two of the methods we inhibited gyrase and topoIV with either quinolone or coumarin antibiotics, and in the third we used a temperature-sensitive strain in which gyrase is inhibited at 42°C. Because it is technically difficult to quantify the supercoiling state of the bacterial chromosome, we used a plasmid, pBR322, in the strains as a reference. Co-transcriptional translation of the membrane , and thumembrane . The supgyrA and gyrB) or topo IV (parC and parE) as controls, to separate expression changes due to undiscovered drug targets from those directly due to changes in supercoiling. When we inhibited gyrase by treating gyrA+ parCR cells with 15 μg/ml norfloxacin, the reporter plasmid in the cells was rapidly relaxed (Table gyrAR parCR strain remained (-) supercoiled. After 30 minutes, there was a 103-fold drop in viability in the sensitive strain, but only a 17% drop in the resistant strain. A norfloxacin concentration of 50 μg/ml fully inhibited both gyrase and topoisomerase IV in the wild-type strain (data not shown), while the resistant strain retained wild type levels of (-) supercoiling and showed only a slight drop (15%) in viability, indicating that we did not overcome the drug resistance. At bacteriocidal concentrations similar to these, quinolones cause a decrease in the sedimentation coefficient of the nucleoid, indicating relaxation of the chromosomal supercoils [t = 0) and was labeled with Cy3 (green). RNA samples taken 2, 5, 10, 20 and 30 minutes after drug addition were labeled with Cy5 (red). The labeled experimental and reference samples were mixed in equal amounts before hybridization to a microarray.The quinolone antibiotic norfloxacin selectively and immediately inhibits gyrase and topo IV -26. We upercoils . The refInhibition of topoisomerases by quinolones leads to double-strand breaks in the chromosome . Thus, nWe also relaxed the chromosome using novobiocin, a coumarin antibiotic that inhibits gyrase, and at a higher concentration, topo IV ,31. NovogyrB234 cells upon shift to the restrictive temperature and subsequent relaxation of the DNA  to determine which genes change significantly during an experiment can bias expression analysis towards genes with very low or variable expression levels . We usedet al. . Brieflyrocedure . The corp-value represents the robustness of the response to relaxation, whereas correlation with plasmid supercoiling may represent sensitivity to changes in supercoiling levels. For example, a gene that is always completely repressed in response to relaxation will have a low p-value, but may show little correlation with intermediate levels of supercoiling. Similarly, a gene with more variable expression may have a higher p-value, but may also have a higher sensitivity to intermediate supercoiling levels. Taken together, these metrics provide a detailed account of supercoiling sensitivity.As an independent metric of supercoiling sensitivity, we measured how closely gene expression followed the level of DNA supercoiling, by calculating the correlation of the expression of each gene across all of the experiments with the level of supercoiling in the reporter plasmid. Relaxation-induced genes showed a positive correlation with plasmid linking number (that is, as (-) supercoiling is lost, both linking number and gene expression increase), up to a maximum observed Pearson correlation coefficient of 0.91. Relaxation-repressed genes showed a negative correlation with plasmid linking number to a minimum Pearson coefficient of -0.88. The majority of genes (3,190, or 80%) showed no strong correlation with plasmid supercoiling, resulting in Pearson coefficients between 0.5 and -0.5. The p-values for all of the genes versus their correlation to plasmid supercoiling are plotted in Figure p-values and little correlation with plasmid supercoiling. Those genes with the lowest p-values (and thus, the most significant expression change upon relaxation) tended to be more strongly correlated (or anticorrelated) to plasmid supercoiling. The data for all genes can be found in Additional data file 2. Among all genes there is a continuous variation in both p-value and correlation to plasmid supercoiling. We found a total of 306 genes at p < 0.05, which we define as SSGs. Of these, 106 genes were induced by DNA relaxation and have a positive correlation with plasmid linking number, while 200 genes were repressed by relaxation and these have a negative correlation with plasmid linking number. The correlations of the SSGs with plasmid supercoiling are shown in Figure p-value. Just over half of the SSGs have high significance, p < 0.005. The high redundancy of our data (21 arrays measuring responses to DNA relaxation, and 14 control arrays with negatively supercoiled DNA) minimized the influence of any single array measurement. Thus we can be confident that the genes we classed as SSGs have a reproducible response to supercoiling changes.The p-value (from the top). Each colored entry in the diagram corresponds to one spot on one array (that is, expression of a gene for a point in a given experiment: red if expression increased during the experiment, green if it decreased). Conversely, these relaxation-repressed genes should have low ratios (and black squares) in the control experiments 1 to 14. The significant difference in SSG expression between the controls and relaxation experiments is reflected by the sharp contrast between the mostly black controls and the bright green relaxation experiments. At the top we have shown a model expression profile representing the supercoiling of the reporter plasmid in each experiment  are listed, along with their correlations to plasmid supercoiling levels.Figure p-values, correlations to supercoiling, and expression levels in each experiment can be found in Additional data file 3.The 106 genes that are induced by relaxation are similarly shown in Figure gyrA+parC+ cells, plasmid supercoiling levels changed dramatically within the first minute  should respond quickly to relaxation. We used a finer time-course experiment to determine which genes had the fastest response to chromosomal relaxation. When 15 μg/ml norfloxacin was added to e Figure . Signifie Figure . We rankE. coli mRNAs have a mean half-life of 5.2 ± 0.3 minutes in LB media [The speed of the transcriptional response to relaxation, combined with the strong correlations to supercoiling of the reporter plasmid in the cells, is strong evidence that the SSGs are directly regulated by supercoiling changes. Furthermore, given that LB media , RNA synp-value with the published values of RNA half-life [We found no correlation of alf-life  and in gp = 3E-4).We searched for a basis of supercoiling sensitivity at the nucleotide sequence level by examining the AT content in and around the SSGs. We considered only the first genes in an operon. Whereas relaxation-repressed genes have a slightly depleted AT content both upstream of their promoters and within the coding sequence, relaxation-induced genes have an emphatic elevation of AT content in similar regions. The AT content of relaxation-induced genes from 800 nucleotides before to 200 nucleotides after the start codon is 54.6%, compared with 51.7% for non-SSGs. To illustrate the very low probability of selecting by chance a set of genes with such an elevated AT content, we randomly selected groups of first-in-operon non-SSGs 50,000 times and calculated AT content within the same window. We never found a set with an AT content as high as the relaxation-induced genes .This is not the only region in which the AT content of SSGs deviates from the rest of the genome. Figure Striking as these differences in AT content are for SSGs as a group, they are not sufficient to distinguish an individual SSG from a non-SSG. That is, not all genes with high or low AT content were supercoiling sensitive in our experiments. Although such genes are rare in the non-SSG population, the greater size of the pool of non-SSGs results in many genes with wide variations in AT content. Also, supercoiling sensitivity cannot solely be due to differences in AT content, as a few SSGs were highly sensitive to supercoiling changes in spite of having an AT content similar to the rest of the genome.topA [gyrA [p-values. Accordingly, although the topoisomerase genes topA and gyrA both clearly respond to supercoiling . The omission of these topoisomerase genes from our list of SSGs reflects the conservative statistical approach we used to define the list. There are probably other genes that respond to supercoiling changes in different conditions from those we tested (log-phase growth in rich media). Also, we defined SSGs by focusing on the immediate effects of relaxation, and thus considered only primary transcriptional changes, rather than downstream effects mediated by other gene products . When downstream effects are considered, changes in supercoiling are likely to affect transcription of an even greater proportion of the genome.In this analysis of supercoiling effects on transcription, we identified 306 genes that quickly and reproducibly respond to chromosomal relaxation. The comprehensive nature of our investigation, with responses of 93% of the genome (4,003 protein-coding genes) in 21 different relaxation experiments and 14 control experiments, allowed us to be more stringent than previous studies in our definition of SSGs, and to identify those genes that had statistically significant changes after the chromosome was relaxed by different methods. Genes that are sensitive to relaxation but are also affected by temperature shifts (including topA  and gyrApA [gyrA ) showed E. coli. Two early studies used either nylon membranes or two-dimensional protein gels to compare topoisomerase mutants with slightly different homeostatic levels of supercoiling, and neither study found a large number of genes [There have been several previous attempts to measure the effects of supercoiling on gene expression in of genes ,44. ThisE. coli [ynhG, yrbL, otsB and yifE). Seven others had p < 0.1 in our relaxation experiments, and the rest had still higher p-values in this study. It is possible that these genes are only responsive to supercoiling changes in the context of osmotic stress.A more recent analysis by Church and colleagues used microarrays to gauge the osmotic stress response of E. coli . SurveyiJust as supercoiling is affected by many environmental changes, such as osmotic shock, oxygen tension, nutrient upshift and temperature change, so too do changes in supercoiling affect genes in a large number of classes. Not surprisingly, a substantial fraction (6.9%) of the SSGs encode genes involved in DNA replication, recombination, modification and repair. However, the SSGs span many other classes, and thus are well positioned to feed into many different regulatory networks. Thus, supercoiling can act as a second messenger that quickly translates environmental changes to transcriptional programs, inducing and repressing specific genes independently of protein synthesis.smtAmukBEF operon on loss of supercoiling is intriguing, given the importance of mukB, mukE and mukF in chromosome supercoiling and segregation [E. coli [Several of the SSGs warrant further inspection. For example, the repression of the regation ,46. Cons[E. coli , these gyrdD, a 'putative topoisomerase'. yrdD encodes a 19 kilodalton (kDa) protein 30-40% identical to the carboxy-terminal domain of topoisomerase I from Bacillus subtilis, Helicobacter pylori and Methanococcus jannaschii. The function of YrdD is unknown, but the repression by chromosomal relaxation provides an intriguing lead.Another relaxation-repressed gene that may be involved in chromosomal maintenance is cls (cardiolipin synthase) and ileS (isoleucine tRNA synthetase), which is consistent with the earlier discovery that these genes were involved in sensitivity to gyrase inhibitors [deoA and deoC are induced on relaxation. For these genes, DNA relaxation may be a signal of DNA damage, and their induction would allow the cell to recycle nucleotides necessary for DNA repair. Finally, the induction of rpoD, the σ70 subunit of RNA polymerase, may help the cell compensate for the increased difficulty of melting the relaxed DNA template.Chromosomal relaxation leads to the repression of hibitors . Also, wlacps and ilvGP [in vitro. The more relevant issue is promoter regulation on chromosomes in vivo, where other factors may dominate. The CRP protein increases lac operon transcription at the low to moderate superhelicities found in vivo, and the nucleoid-associated protein IHF is implicated in the supercoiling sensitivity of the ilvGMEDA operon [Salmonella leu-500 promoter by supercoiling requires that the promoter is either on the chromosome or on a plasmid anchored to the cell membrane by transcription and translation of a gene such as tetA [What is the basis of supercoiling sensitivity? Most of the well controlled analyses of supercoiling-sensitive promoters, notably of the nd ilvGP ,49-51, wA operon . Also, tA operon -55. We f as tetA . FurthergyrA and gyrB promoters, Menzel and Gellert [gyrA and gyrB. Promoter clearance has also been invoked in the mechanism of supercoiling sensitivity of some promoters in vitro [in vivo, and propose that promoter clearance is generally a key regulatory step for supercoiling sensitive transcription.It is striking that there are so many relaxation-induced genes that are relatively repressed when the chromosome is (-) supercoiled. This is surprising because (-) supercoiling should favor formation of an open promoter complex. The promoter regions of many of the genes induced by relaxation are AT rich, which will make it easier to form an open promoter complex even when the DNA is relaxed and the energy required is greater. Alternatively, the difference in AT content could reflect structural features such as curvature or flexibility. Curved sequences of DNA can influence the position of plectonemic supercoils, and thus could serve to localize a promoter sequence to the apex of a superhelical loop . We note Gellert  found thin vitro . As our The AT-rich regions of our relaxation-induced genes extend downstream of the translational start site, and thus may involve transcription elongation in addition to promoter clearance. There is growing appreciation of the regulation of transcription elongation -61. The E. coli chromosome [The SSGs are useful as topological probes of the chromosome in living cells. While the SSGs are scattered throughout the chromosome, they are not evenly spread, but rather have regions of high and low density. The SSGs are plotted on a chromosomal map in Figure romosome . The disromosome . Relaxatromosome . Here weWe have shown that supercoiling acts as a transcription factor, with positive and negative effects on specific genes while leaving the majority of the genome unchanged. Like other transcription factors such as TrpR  and ArcAE. coli K-12, strain MG1655 (Sigma-Genosys), were generously supplied by Fred Blattner (University of Wisconsin) and Carol Gross . ORFs were amplified from MG1655 genomic DNA using ExTaq polymerase (PanVera) and failed PCR reactions were attempted again using Platinum Taq (Invitrogen) or previous successful reactions as the DNA template. Ninety-six percent of the ORFs were successfully amplified. PCR conditions were set according to those supplied with the primers. DNA was precipitated with isopropanol and prepared for microarray printing as described in [tyrT, have been shown to respond to changes in supercoiling [Amino- and carboxy-terminal primers for protein-coding open reading frames (ORFs) of ribed in . We did rcoiling .Detailed instructions on slide preparation, microarray printing and processing microarrays can be found online . 384-welE. coli cells were grown with shaking in LB media to an OD600 = 0.45-0.55 at 37°C, or at 30°C for temperature-sensitive strains. Samples of cells were withdrawn at intervals and added to a 1/10 volume of either 95% ethanol plus 5% phenol or 2 M NaN3 to stop transcription. Cells were then quickly harvested by centrifugation in a microcentrifuge. The supernatant was aspirated and the pellets frozen in liquid N2. Total RNA was prepared using the Qiagen RNeasy mini kit, except that 4 mg/ml lysozyme and a 30 sec incubation was used in the first step. For each microarray, 20 μg total RNA was primed with 1-2 μg of random hexamers and labeled by reverse transcription in the presence of Cy3- and Cy5-conjugated dUTP (Amersham Biosciences). For each experiment or condition, a Cy5-labeled experimental sample was combined with a Cy3-labeled reference sample and hybridized to a processed microarray as described [escribed . After 5Scanned array images were visually inspected, and non-uniform spots were excluded from further analysis. The background was subtracted from the images that were then (median) normalized using the Scanalyze 2.0 program, v. 1.44  such that the total fluorescence in each channel was equal.E. coli genes, that could be considered for further study. Because we were interested in changes in expression levels due to variations in supercoiling rather than to drug or genetic effects, we used a two-sample comparison approach  rather than a factorial analysis approach. We tested two commonly used methods to determine differentially expressed genes in the comparison of two samples. We found that the method of Dudoit et al [et al [We tested several methods of imputation to estimate the values of spots missing due to hybridization defects (described in ), and afit et al , which cl [et al .32P-labeled DNA probes for gyrB and asnB (as a loading control) were synthesized from their respective PCR products, and radioactivity was quantified by a phosphorimager.Samples were run on formaldehyde-MOPS 1% agarose gels and blotted onto a nylon membrane . 32P-labgyrB234 mutant are in a C600 strain background, but all strains used have been described in greater detail elsewhere [ΔacrA strain that greatly slows drug efflux [0, where Lk0 for pBR322 = 4,361 bp/10.5 bp/turn = 415. Samples were run on parallel 20-cm gels containing 0, 2.8 or 10 μg/ml chloroquine for 18-26 h at 48-52 V with constant buffer recirculation, which allowed visualization of the entire distribution of topoisomers. Gels were southern blotted [32P-labeled probe made from random priming of pBR322. Radioactive blots were quantified using a phosphorimager.Plasmid DNA was extracted from cells by the alkaline lysis method  or the Qlsewhere ,35,73. Tg efflux . The supg efflux  from the blotted , and hybgyrB by both methods 5 min after addition of the gyrase inhibitor novobiocin to ΔacrA cells at 5, 20, 50 and 200 μg/ml. The microarray ratios for these concentrations were 2.3, 4.8, 4.9 and 6.3, respectively, while the ratios from northern hybridizations were 2.8, 4.7, 4.7 and 5.1. Second, as an internal control we compared the transcription of genes in 153 known polycistronic operons. We found no operons with genes that changed expression more than 1.5-fold in opposite directions (data not shown). Third, we compared two identically grown cultures with the same microarray . We used two strains that were isogenic, except that one had point mutations conferring norfloxacin resistance on gyrase and topo IV. The correlation coefficient of the gene-expression levels was 0.982, indicating the negligible variation between the cultures. In contrast, when we treated cells with the gyrase inhibitor norfloxacin , the correlation coefficient with respect to the untreated cells was only 0.391 and hundreds of genes showed large differences in expression. We conclude that gene-expression changes resulting from slight genotypic changes or experimental repeats were negligible compared with the changes resulting from topoisomerase inhibition, and that the E. coli microarrays are a reliable method for quantifying these changes.We tested the validity of our microarrays in three ways. First, we compared gene expression ratios measured with microarrays to values obtained by northern hybridization. We measured induction ratios for p-values < 0.05) over a total of 35 experiments, in which DNA gyrase was inhibited with novobiocin, norfloxacin or by a mutation that rendered gyrase temperature-sensitive. Next we determined the correlation of gene expression with the σ of a reference plasmid in the same cells. To calculate the correlation of gene expression to plasmid supercoiling, we created a model profile made up of the ratio of plasmid σ in each experiment to plasmid σ in the (supercoiled) reference for that experiment (Table We limited the list of SSGs to those whose expression difference between treatments and controls was statistically significant (nt Table . The maxThe following additional data files are available with the online version of this paper: Additional data file Data on the induction of the SOS response to DNA damageClick here for additional data fileGene-expression ratios for all genes across all experimentsClick here for additional data fileGene-expression ratios for supercoiling-sensitive genes across all experimentsClick here for additional data fileData on the reproducibility of microarray measurement of RNA levelsClick here for additional data file


Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction.We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space) and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP) allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny.The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations. Using genomic sequences, an estimate of the relative time separating branching events, previously supported by geological records, could be formalized using mathematical models. The use of proteins for evolutionary reconstructions was vastly explored as soon as the first amino acid sequences were made available -9. The rA or RNA ; they alA or RNA ,13. WhenA or RNA , based oAs summarized by Otu and Sayood , the tect), also called genetic distance or evolutionary time, is calculated from the p-distance, depending on assumptions derived from evolutionary models [T = (tab) where a and b represent the homologous sequences from the analyzed dataset. Tree reconstruction algorithms are then applied to these matrices [In phylogeny inference based on distance methods, features separating related proteins are used to estimate an observed distance, also called the p-distance, the simplest measure of which is just the number of different sites between proteins. Divergence time (y models ,24. For y models ,25,26. Bmatrices ,24. Evena and b) is determined from the optimal score s(a,b) [p-distance can be given as a function of fid, the fraction of identical residues: p-distance = 1 - fid. To take into account that multiple mutations can happen at the same site, an expression of fid was proposed by Doolittle et al. [s(a*,b*), the score obtained from randomized a and b sequences [sid, the average score of the sequences compared with themselves [Doolittle et al.  have proe s(a,b) ,27, compe s(a,b) ,33. In aequences  and sid,emselves ,25,27:To connect pair-wise alignments and phylogeny, divergence time has been approximated:t(a,b) = -λ log[fid (a,b)]     (2)introducing a Poisson correction  as a reap-distance between two homologous sequences, based on fid, as a source for geometric positioning, divergence time calculations and evolutionary reconstruction. We based our model on mathematical properties that alignment scores should respect; i) information theory [Here, we re-examine the estimate of the n theory ,38 applin theory  and iii)n theory . We usedi and j, the similarity function s(i,j) was set as:Criteria to measure the variation/conservation balance between proteins should embody as much as possible the structural and functional potentiality within sequences of amino acids. In the absence of explicit physical criteria, amino acid similarity was solved empirically by measuring amino acid substitution frequencies in alignments of homologous sequences ,40. Give is the observed frequency of substitution of i by j or j by i, and πi and πj are the frequencies of i and j in the two aligned sequences. The   frequency is the estimate of the probability of substitution of i by j in real alignments; whereas πiπj is the estimate of the probability of substitution under the independency hypothesis. The similarity function gives a 20 × 20 similarity matrix usable to score protein sequence alignments, that can be interpreted in the information theory [where n theory ,38 accorAmino acid substitution matrix values are estimates of the mutual information between amino acids in the sense of Hartley ,38. ConsP that characterizes a random variable, the Hartley self-information h is defined as the amount of information one gains when an event i occurred, or equivalently the amount of uncertainty one loses after learning that i happened:Given a probability law h(i) = -log(P(i))     (4)i, the more we learn about the system when i happens. The mutual information I between two events, is the reduction of the uncertainty of one event i due to the knowledge of the other j:The less likely an event Ij→i = h(i) - h(i/j)     (5)i.e. Ij→i = Ii→j, and in the following will be expressed by I. The self and mutual information of two events i and j are related:Mutual information is symmetrical, h(i ∩ j) = h(i) + h(j) - I     (6)If the occurrence of one of the two events makes the second impossible, then the mutual information is equal to - ∞. If the two events are fully independent, mutual information is null. The empirical measure of the similarity between two amino acids described in equation (3) can therefore be expressed in probabilistic terms:Pϖ is the joint probability to have i and j aligned in a given alignment and Pπ the measure of probability that amino acids occur in a given sequence. From equations (4) and (6), equation (7) becomes:where s(i, j) = h(i) + h(j) - h(i ∩ j)     (8)that iss(i, j) = I     (9)As a consequence, the similarity function (or score) is the mutual information between two amino acids. Additionally, the score between sequences (the sum of elementary scores between amino acids, ,33,41,42a and b (a = a1a2...am and b = b1b2...bn), where s = s(a,b) the maximal score of a pair-wise alignment obtained with any alignment method, b* the variable corresponding to the shuffled sequences from b, and given P{S(a,b*)≥s} the probability that an alignment by chance between a and b* has a higher score than s, then whatever the distribution of the random variable S(a,b*) the TULIP theorem states:Once two sequences are aligned, we pose the question whether the alignment score is sufficient to assess that the proteins are conceivably alike and thus evolutionarily related? The theorem of the upper limit of a sequence alignment score probability (TULIP theorem, ), sets tk > 1, μ the mean of  and σ its standard deviation. The unique restriction on S(a,b*) is that it has a finite mean and a finite variance. A first corollary of the TULIP theorem is that the Z-score is a statistical test for the probability of a sequence alignment score. We additionally state the following new corollary.with  be the Z-score [z(a,b*) is the greatest possible value for k (k∈]1,+∞[), which holds relation (10) true. In consequence, with k = z(a,b*), thenGiven the TULIP theorem conditions, let  Z-score . Then, zP{S(a,b*)≥s} is termed . From the TULIP theorem and corollaries, the comparison of a protein to a given reference a, weighed by an alignment score, is characterized by a bounded probability that the alignment is fortuitous.The best upper bound value for a and b in a given space E [f(a,b): E × E → + such that f(a,b) = 0 ⇔ a = b and f(a,b) = f(b,a); the second is the distance per se, that is a dissimilarity such that the triangle inequality is respected: ∀ a,b,c ∈ E, f(a,c) ≤ f(a,b) + f(b,c); and the third is the similarity defined as a function f(a,b): E × E →  such that  and f(a,b) = f(b,a). Representing objects in a space is convenient using the notion of distance. When the optimal alignment is global, i.e. requiring that it extends from the beginning to the end of each sequence [per se, that is to spatially organize the compared sequences [Since the optimized alignment score of two protein sequences allows an access to both the mutual information between proteins and an upper bound that the alignment is not fortuitous, one would expect that it is an accurate way to spatially organize proteins sets. A simple relation would be "the higher the mutual information, the nearest". There are three ways to assess the proximity between two objects  space E . The firsequence , it is tequences . Howeverequences ,31. In cequences .f(i, j): E × E → , owing to the local alignment optimization algorithms, the computed score is a function f(a,b): E × E → +, requiring the existence of at least one positive score in the similarity matrices. Thus, when constructing an alignment with the Smith and Waterman [s(a,b)>0  > 0) is imposed. This condition is consistent with proposition 1: if two sequences are homologous, knowledge about the first has to bring information about the second, that is to say, the mutual information between the two sequences cannot decrease below zero: I > 0 (i.e. s(a,b) > 0). As a consequence, in the following geometric construction we sought a refined expression for the proximity of proteins.Although amino acid similarity is a function Waterman  method, a can be selected as a reference, noted aref, in respect to which the others are compared. A geometric representation of objects relatively to a fixed frame is known as a configuration space (CS). In physics, a CS is a convenient way to represent systems of particles, defined by their positional vectors in some reference frame. Here, given n similar sequences, it is therefore possible to consider n references of the CSHP. In a given (CSHP, aref), each amino acid position aligned with a position in the aref sequence, corresponds to a comparison dimension (CS dimension). Proteins are simply positioned by a vector, the coordinates of which are given by the scores of aligned amino acids. Gaps are additional dimensions of the CS. When considering that local algorithms identify the space of biological interest, i.e. a CSHP, the gap penalty is a parameter that maximizes the shared informative dimensions. Thus, given the amino acids mutual information, alignment optimization methods define the relative positions of proteins.In a set of homologous proteins, any sequence aref is sufficient for the full positioning, then positioning of proteins in a given (CSHP, aref) is unambiguous, unique, and is not altered when proteins are added or removed. In other words, a (CSHP, aref) is a univocal space.At this point in our construction, a first important property of the CSHP can be deduced. Since mutual information with a and b, if b occurs in (CSHP, aref), then a also occurs in (CSHP, bref). The pair-wise alignment of a and b having no order , the positions of b in (CSHP, aref) is dependent of the position of a in (CSHP, bref). Thus, once a (CSHP, aref) has been built, ∀b ∈ (CSHP, aref), part of the geometry of (CSHP, bref) is learnt. Thus, in a CSHP, information needed for the position of n sequences is totally contained in the geometry of the n (CSHP, aref). This geometric stability is not observed with multiple alignments, which can be deeply modified by addition or removal of sequences. In the CSHP, protein position is unaltered by additions or removals of other proteins. In practice, the construction of CSHP is therefore completely deduced from any all-by-all protein sequence comparison [Given two sequences mparison ,46 and cper se based on mutual information is reduced ad absurdum (For demonstration, see methods). To define a proximity function i) sharing properties of distance, i.e. increasing when objects are further apart, ii) deriving from similarity and iii) relying on mutual information, particularly the property "f(a,a) ≠ f(b,b) is possible", we introduce a fourth notion of proximity. Such proximity was called q-dissimilarity (for quasi-dissimilarity), a function f(a,b): E × E → + is defined such thatIn the CSHP, the definition of a distance a ∈ E, ∀ b ∈ E, f(a,b) = f(b,a)     (13)∀ s be a similarity, then q = es -is a q-dissimilarity, named the 'canonical q-dissimilarity' associated to s. Accordingly, the TULIP theorem allows a statistical characterization of q(a,b) the canonical q-dissimilarity between two sequences a and b.Let From the TULIP corollary 2, relation (14) is simply deduced:Q(a,b*) being the random q-dissimilarity variable associated with S(a,b*). Given a (CSHP, aref), each sequence b aligned with a is characterized by a q-dissimilarity q(a,b). In geometric terms, b can be represented as a point contained in a hyper-sphere B of radius q(a,b).with aref) shown in Figure Q(a,b*). When a is compared to itself, it is set on a hyper-sphere A of radius q(a,a), which is not reduced to one point. In the context of information theory, it is therefore possible to express that the proximity respects the property "q(a,a) ≠ q(b,b) is possible". Considering Figure  is the probability for a random sequence b* to be in the hyper-sphere B. In conclusion, the q-dissimilarity is therefore a proximity notion that allows a rigorous geometric description of the configuration space of homologous proteins, real or simulated, (CSHP, aref, q).The representation of a  is theoretically the summed elapsed times separating u to a and to b. Without any empirical knowledge of u, the simplest approximation for t(a,b) was sought as a function of the fraction of identical residues fid, thus of the p-distance. With the hypothesis of the molecular clock, this function can be given as equation (2), where the transmutation of a and b is a consequence of a Poisson process. By using relation (9) on the equivalence between score similarity and mutual information, then the fundamental postulate "the closer in the evolution, the more alike and conversely, the more alike, probably the closer in the evolution" can be reformulated:A geometric space is a equences . Given ua and b, the closer in the evolution, the greater the mutual information between a and b ) and conversely, the greater the mutual information between a and b, probably the closer in the evolution.Given two homologous proteins Whereas the first part of the postulate is a consequence of the conservational pressure on mutual information, the second assertion founds the historical reconstruction underlying a set of biological sequences on statistical concepts. A corollary is that evolution of two homologous proteins is characterized by a loss of mutual information.knowing that they share some features (the observed sequences a and b) and that they were identical before the speciation event (sequence u).In the CSHP, this formulation of the fundamental postulate allows a novel mathematical formalization of the p-distance in probabilistic terms. Basically, the p-distance is the divergence observed between two sequences fid in probabilistic terms, considering the fraction of shared features  knowing the observed data and the existence of a common ancestor. Given two proteins a and b, let us consider the random variable Q(a,b*), defined in TULIP corollary 3. In (CSHP, aref), shown in Figure P{Q(a,b*)≤ρ} as the probability that the q-dissimilarity between b* and aref is lower than ρ. The hyper-sphere of radius ρ contains therefore the b* random sequences sharing informative features with a accordingly. The probability pid/a that b* shares identity with a, knowing that the q-dissimilarity between b* and a is lower than that between the real sequences b and a, is:Looking back to equation 1), we can re-formulate , we can pid/a (b*) = P{Q(a,b*) ≤ q(a,a) / Q(a,b*) ≤ q(a,b)}     (15)fid in respect to the reference aref. According to the Venn diagram in Figure pid/a (b*) = P(A/B)which is a probabilistic expression of Using the Bayes theorem, equation (15) can be expressed as:In consequence:which can be expressed asQ(a,a*) ≈ Q(a,b*) andAssuming that substitution rates are independent of lineages , then rapid/a, and symmetrically pid/b, provide a probabilistic expression of fid knowing the data, i.e. the observed mutual information between a and b expressed as Q(a,b).Thus a and b, when their optimal score is s(a,b) ≥ μ + ψ with ψ being a critical threshold value depending on the score distribution law , owing to the TULIP corollary 2, we can state that pid/a is bounded above:Given two homologous sequences This expression can also be developed as:μ1, σ1, μ2 and σ2 are the mean and the standard deviation of S(a,b*) and S(a,a*) respectively. The right term in relation (21) exhibits analogies with fid given by equation (1), showing that the pragmatic approach by Feng and Doolittle [where oolittle  could bet(a,b) is given as the linear combination of the two corrections of the p-distance deduced from pid/a and pid/b :Using the Poisson correction, an expression of t(a,b) = -[log(pid/a (b*)) +log(pid/b (a*))]     (22)a* and b* the random variables corresponding to the shuffled sequences of a and b respectively. The sum of the logarithms corresponds to the product of the two probabilities, an expression of the hypothesis of independence of lineage. Interestingly, equation (22) provides an expression of the symmetric effect of time on the variations that independently affected a and b.with t(a,b) appears as a function of Z-score ratios. For any set of homologous proteins, it is therefore possible to measure a table of pair-wise divergence times and build phylogenetic trees using distance methods.From relation (20), , where numbrand is the number of randomizations. In the case studies presented here, we set numrand = 2000 (see Methods). By contrast, stability of MAB trees is sought by bootstrapping approaches and consensus tree reconstruction. MAB trees appear as the result of a complex learning process including possible re-adjustment of the multiple alignments after eye inspection pragmatically applied to assist the reconstruction. Alternatively, Bayesian analyses have been recently proposed for phylogenetic inference [We compared the trees we obtained, called TULIP trees, to phylogenetic trees built using classical methods, for instance the popular PHYLIP  or PUZZLnference , estimatnference  and Simmnference  providedWhenever a TULIP classification was achieved on a dataset that led to a consensual MAB tree, both were always consistent. For example, Figure Chlorarachnion and Arabidopsis. However, they could not conclude about alveolates, finding a conflict between the distribution of the insertion and the MAB phylogenetic position . Group II: Bacteria Actinobacteria . Group III: Bacteria Cyanobacteria . Group III: Bacteria Bacillus . Group IV: Bacteria Proteobacteria . Group V: Bacteria Chlamydiae . Group VI: Others Bacteria . Group VII: Fungi . Group VII: Eukaryota Viridiplantae . Group VIII: Eukaryota Alveolata Apicomplexa . Group IX: Animals . Group X: Other Eukaryota .The 41 Glucose-6-phosphate isomerase (EC 5.3.1.9) sequences studied in the paper are taken from several representative groups, as provided from the Swiss-prot database. Group I: Archae . Group II : Animals and Fungi . Group III: Amoebae . Group IV: Alveolates . Group V: Trypanosomatidae . Group VI: Hexamitidae . Group VII: Archaebacteria . Group VII: Eubacteria .Enolase sequences used for the case-study presented in this paper were taken from eight major groups previously studied by . Group IIn the simplest case, building a distance between amino acids (that would lead to distance between sequences) on the basis of computed similarity values would have to respect the condition:i ∈ E, ∀j ∈ E,d(i,j) = 0 ⇒ i = j     (a)∀i and j, two given amino acids and d the distance function. Using this condition in the proposition, any organization of the CSHP with a geometric distance is reduced ad absurdum.for d(i,j) = (φ ○ s)(i,j), where s is a similarity function and φ a bijection, is impossible without a loss of mutual information. Moreover, two proteins from distinct organisms can have the same configuration, being like "twins", and d(i,j) = 0 does not imply i = j.Building a distance between amino acids derived from the composed function φ (s(i,i)) = φ (s(j,j)) = 0. This equality imposes that s(i,i) = s (j,j) and, following equation (7) of main text, that I = I. Considering for example tryptophan (W) and glutamic acid (E), if W occurs in a sequence, the mutual information gained about the occurrence of W at the aligned position would be the same as that gained in the case of E about the occurrence of E at the aligned position in the homologous protein. This statement is easily rejected on the basis of biochemical concerns. On one hand, aspartic acid (D) shares common biochemical properties with E, particularly a carboxylic acid, and easily substitutes in homologous sequences. By contrast W, exhibiting a unique biochemical feature, is less substitutable without altering the function. Thus the mutual information I is necessarily lower than I. This that can be checked in scoring matrices such as BLOSUM 62 [I = 5, I = 6 and I = 11. Condition d(i,i) = 0 leads to an obvious loss of information. The second assertion of the proposition is obvious.Condition (a) implies that LOSUM 62  where I ≥ μ + ψ , owing to the TULIP corollary 2, we can state that pid/a is bounded aboveAn important basis of the reconstruction of a probabilistic evolutionary topology in the CSHP is based on the demonstration that, given F(s) = P(S ≤ s), its derivative f(s) known as the probability density function defined as dF(s) = f(s)ds, and the positive delta function δ (s) = (s - μ)2(1 - F(s)). Since δ (s) = (s - μ)2(1 - F(s)) is null for s = μ and , the Rolle's theorem implies that ∃s0 ∈]μ,+∞[ such as [s0 corresponds to a maximum of δ (s) and is therefore the solution of the equationTo the purpose of this demonstration, we considered the cumulative distribution function  such as ; s0 corrF(s)) - (s - μ) f(s) = 0     (b)2, conditional to the fact that the pair-wise alignment score is at least equal to s. The hazard function is also defined by . A critical hypothesis is that φ (x) function is strictly increasing and conversely that  is strictly decreasing. Considering , equation 2 has only one solution s0 and this solution is bounded above:The function , that isδ (s) reaches its maximum for a s0 (s0 ≤ μ + ψ) and it is strictly decreasing on ]μ + ψ ,+∞[.In consequence, s0 is not trivial because it depends on the knowledge of the cumulative distribution function. Extensive studies provided experimental and theoretical supports for an extreme value distribution of alignment scores [i.e. the Gumbel distribution [The estimation of t scores ,43,44. Uribution , the cumθ and β (β > 0) the location and scale parameters. The probability density function g(s) is defined by dG(s) = g(s)ds. We observe with  that . Using the Taylor's polynomial formula, i.e. :with In consequence, for a Gumbel score probability distribution:ψ from a Gumbel distribution is illustrated in Figure A graphical determination of a and b is relatively high, that is s(a,b) ≥ μ + ψ, then the trivial inequality s(a,a) ≥ s(a,b) impliesIf a pair-wise alignment score of two sequences s(a,b) - μ)2(1 - F(s(a,b))) ≥ (s(a,a) - μ)2(1 - F(s(a,a)))     (h), we deduce that a and b, alignment was achieved with the Smith-Waterman method and the BLOSUM 62 scoring matrices, using the BIOFACET package from Gene-IT, France [z(a,b*), z(a,a*), z(a*,b), z(b*,b*), with 2000 sequence shuffling. For all computations, an estimation of the Gumbel parameters θ and β was made using the computed μ and σ of any S(a,b*) and the formula  and θ = μ - βΓ'(1), where Γ'(1) ≈ 0.577216 is the Euler constant. In all computations, both Gumbel parameters were very close (in the case of enolases, mean(θ) = 35.04, SD(θ) = 0.12, mean(β) = 3.92, SD(β) = 0.08). As a consequence, the assumption Q(a,a*) ≈ Q(a,b*) was verified for any pair of sequences. We used the parameters to estimate μ = θ + βΓ '(1) (in the case of enolases, μ = 37.33), and μ + ψ ≈ μ + 10.5178 ≈ 47.85. As any pairs of computed scores are higher than this critical threshold, we used relation [To build PHYLIP trees, multiple sequence alignments were created with ClustalW . PHYLIP , France . We comprelation . Trees wCSHP, configuration space of homologous proteins, TULIP, theorem of the upper limit of a score probabilityOB conceived the main theoretical model, designed and developed the method to build phylogenetic trees and drafted the manuscript. SR and PO participated in the theoretical model refinement and in the design and development of computational methods to build TULIP trees. EM contributed to the conception of this study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Should we be considering the social and economic ramifications of a society where life-span could be limitless? Sinclair suggested, as have other experts, including his mentor Lenny Guarente and the National Institute on Aging advisory council member Elizabeth Blackburn, that Kass and other bioconservatives are creating a false alarm about life extension, because only a modest (say, 30%) increase in human life span is achievable by biomedical intervention, whereas Kass's apprehensions concern extreme or indefinite life extension. Kass retorted that science isn't like that: modest success tends to place the bit between our teeth and can often result in advances far exceeding our expectations.The biogerontologist David Sinclair and the bioethicist Leon Kass recently locked horns in a radio debate  of age-related molecular and cellular damage—consisting of just seven major categories of ‘rejuvenation therapy’ (I agree with these predictions in two respects: that the degree of life extension achieved by first-generation drugs of this sort may well approach the (currently unknown) amount elicitable by caloric restriction itself in humans, and that it is unlikely to be much exceeded by later drugs that work the same way. In two other ways, however, I claim they are incorrect. The first error is the assumption of proportionality: I have recently argued , from evescribed , 2004 a therapy’ —that appCoping with Methuselah and by other commentators is demographic. Life expectancy is typically defined in terms of what demographers call a period survival curve, which is a purely artificial construction derived from the proportions of those of each age at the start of a given year who die during that year. The ‘life expectancy’ of the ‘population’ thus described is that of a hypothetical population whose members live all their lives with the mortality risk at each age that the real people of that age experienced in the year of interest. The remaining life expectancy of someone aged N in that year is more than this life expectancy minus N for two reasons: one mathematical  and one biomedical . My spirits briefly rose on reading Aaron and Harris's explicit statement (p. 69) of the latter reason. Unfortunately, they didn't discuss what would happen if age-specific mortality rates fell by more than 2% per year. An interesting scenario was thus unexplored: that in which mortality rates fall so fast that people's remaining  life expectancy increases with time. Is this unimaginably fast? Not at all: it is simply the ratio of the mortality rates at consecutive ages (in the same year) in the age range where most people die, which is only about 10% per year. I term this rate of reduction of age-specific mortality risk ‘actuarial escape velocity’ (AEV), because an individual's remaining life expectancy is affected by aging and by improvements in life-extending therapy in a way qualitatively very similar to how the remaining life expectancy of someone jumping off a cliff is affected by, respectively, gravity and upward jet propulsion . The escape velocity cusp is closer than you might guess. Since we are already so long lived, even a 30% increase in healthy life span will give the first beneficiaries of rejuvenation therapies another 20 years—an eternity in science—to benefit from second-generation therapies that would give another 30%, and so on ad infinitum. Thus, if first-generation rejuvenation therapies were universally available and this progress in developing rejuvenation therapy could be indefinitely maintained, these advances would put us beyond AEV. Universal availability might be thought economically and sociopolitically implausible (though that conclusion may be premature, as I will summarise below), so it's worth considering the same question in terms of life-span Coping with Methuselah is representative, is the most significant because of its urgency. First-generation rejuvenation therapies, whenever they arrive, will surely build on a string of prior laboratory achievements. Those achievements, it seems to me, will have progressively worn down humanity's evidently desperate determination to close its eyes to the prospect of defeating its foremost remaining scourge anytime soon. The problem  is that this wearing-down may have been completed long before the rejuvenation therapies arrive. There will come an advance—probably a single laboratory result—that breaks the camel's back and forces society to abandon that denial: to accept that the risk of getting one's hopes up and seeing them dashed is now outweighed by the risk of missing the AEV boat by inaction. What will that result be? I think a conservative guess is a trebling of the remaining life span of mice of a long-lived strain that have reached two-thirds of their normal life span before treatment begins. This would possess what I claim are the key necessary features: a big life extension, in something furry and not congenitally sick, from treatment begun in middle age.The third oversight that I observe in contemporary commentaries on life extension, among which ten years, given adequate and focused funding (perhaps $100 million per year). And nobody in Coping with Methuselah said so. This timeframe could be way off, of course, but as Wade notes (p. 57), big advances often occur much sooner than most experts expect. Even the most obvious of these lifestyle changes—greater expenditure on traditional medical care, avoidance of socially vital but risky professions—could severely destabilise the global economy; those better versed in economics and sociology than I would doubtless be even more pessimistic about our ability to negotiate this period smoothly. Overpopulation, probably the most frequently cited drawback of curing aging, could not result for many decades, but the same cannot be said for breadth of access irrespective of ability to pay: in a post-9/11 world, restricted availability of rejuvenation therapies resembling that seen today with AIDS drugs would invite violence on a scale that, shall we say, might be worth trying to avoid.It is the prospect of AEV, of course, that makes this juncture so pivotal. It seems quite certain to me that the announcement of such mice will cause huge, essentially immediate, society-wide changes in lifestyle and expenditure choices—in a word, pandemonium—resulting from the anticipation that extreme human life extension might arrive soon enough to benefit people already alive. We will probably not have effective rejuvenation therapies for humans for at least 25 years, and it could certainly be 100 years. But given the present status of the therapies listed in Coping with Methuselah (p. 143). Once AEV is achieved, there will be no going back: rejuvenation research will be intense forever thereafter and will anticipate and remedy the life-threatening degenerative changes appearing at newly achieved ages with ever-increasing efficacy and lead time. This will bring about the greatest economic change of all in society: the elimination of retirement benefits. Retirement benefits are for frail people, and there won't be any frail people. The graph just mentioned amply illustrates how much wealth will be released by this. My hope, therefore, is that once policy makers begin to realise what's coming they will factor in this eventual windfall and allocate sufficient short-term resources to make the period of limited availability of rejuvenation therapies brief enough to prevent mayhem. This will, however, be possible only if such resources begin to be set aside long enough in advance—and we don't know how long we have.Am I, then, resigned to a future in which countless millions are denied many decades of life by our studied reluctance to plan ahead today? Not quite. The way out is pointed to in


The initiation of re-entrant cardiac arrhythmias is associated with increased dispersion of repolarisation, but the details are difficult to investigate either experimentally or clinically. We used a computational model of cardiac tissue to study systematically the association between action potential duration (APD) dispersion and susceptibility to re-entry.+ channel gKmax set to 0.004 mS mm-2. Within the central 40 × 40 mm region we introduced square regions with prolonged APD by reducing gKmax to between 0.001 and 0.003 mS mm-2. We varied (i) the spatial scale of these regions, (ii) the magnitude of gKmax in these regions, and (iii) cell-to-cell coupling.We simulated a 60 × 60 mm 2 D sheet of cardiac ventricular tissue using the Luo-Rudy phase 1 model, with maximal conductance of the KgKmax in regions with prolonged APD from 0.003 to 0.001 mS mm-2 increased APD dispersion from 22 to 70 ms, and the susceptible window from <1 to 56 ms. Decreasing cell-to-cell coupling by changing the diffusion coefficient from 0.2 to 0.05 mm2 ms-1 increased APD dispersion from 57 to 88 ms, and increased the susceptible window from 41 to 74 ms.Changing spatial scale from 5 to 20 mm increased APD dispersion from 49 to 102 ms, and the susceptible window from 31 to 86 ms. Decreasing We found a close association between increased APD dispersion and susceptibility to re-entrant arrhythmias, when APD dispersion is increased by larger spatial scale of heterogeneity, greater electrophysiological heterogeneity, and weaker cell-to-cell coupling. Cardiac disease remains an important cause of sudden death in the industrialised world, and in many cases the lethal events are the cardiac arrhythmias called ventricular tachycardia (VT) and ventricular fibrillation (VF). Spontaneous episodes of VT and VF occur in patients where cardiac disease or congenital abnormality has remodelled either the structure or function of cardiac cells and tissue. There is abundant experimental evidence to support the idea that VT and VF are sustained by re-entry ,2, but tSlow conduction and unidirectional block have long been known to facilitate re-entry , and exp-1 were associated with block and re-entry [Regional differences in repolarisation are often described as action potential duration (APD) dispersion. The difference between the longest and shortest observed APD is a conceptually simple and easily obtained quantity and has widely been used to measure APD dispersion, although other indices have been proposed . Some exre-entry ,19,20. Sre-entry ,21,22. T+ channel conductance between regions with short and long APD, and (iii) strength of cell-to-cell coupling.Computational models offer a powerful research tool for addressing these questions, because the properties of a virtual tissue can be controlled precisely and independently in a way that would be extremely difficult to achieve experimentally. The purpose of this study was therefore to investigate systematically how measured APD dispersion and vulnerability to re-entry in a computational model of ventricular tissue are related to: (i) the spatial scale of heterogeneity, (ii) the magnitude of differences in KWe simulated electrical activation in a 2 D isotropic monodomain virtual tissue Vm is membrane voltage, Cm specific membrane capacitance, D a diffusion coefficient and Iion current flow through the cell membrane per unit area. We used the Luo-Rudy phase 1 (LR1) model [Iion,where 1) model  to give INa, ICa  and IK are time and voltage dependent currents flowing through Na+, Ca2+, and K+ channels, IK1 a time-independent K+ current, IKp a plateau K+ current, and Ib a background current. We changed two parameters from the original Luo and Rudy paper [+ conductance from 0.23 mS mm-2 to 0.16 mS mm-2 as in the later version of the model [-2 to 0.0005 mS mm-2 to produce an APD comparable to that in the canine ventricle. We controlled repolarisation by varying maximum K+ conductance (gKmax) from the default value of 0.00282 mS mm-2 to a value between 0.001 mS mm-2 and 0.004 mS mm-2 (see below).where dy paper . We reduhe model , and we Cm to 0.001 μF mm-2, and set D to between 0.05 and 0.2 mm2 ms-1. We used an adaptive timestep of either 0.02 or 0.1 ms depending on the magnitude of dVm/dt at each grid point [2 ms-1 we obtained a conduction velocity (CV) for a stable plane wave of 0.56 m s-1, with a speedup of about two times and an error in CV of 2.5 % compared to computations with a fixed timestep of 0.01 ms. Simulations with smaller fixed and adaptive timesteps yielded plane waves with a comparable CV. Changing the space step to 0.25 mm and 0.15 mm resulted in a change of CV for a plane wave of <5 % compared to the CV computed with a space step of 0.2 mm. These findings indicated the stability of our numerical method.We solved equation 1 and the LR1 equations using an explicit Euler method, with both a lookup table of the voltage dependent parameters in the LR1 model, and an adaptive operator splitting technique . We applid point . With a gKmax. In each case a propagating action potential could be elicited with a minimum diastolic interval of about 10 ms, indicating that the refractory period was very close to the APD.Figure gKmax set to 0.004 mS mm-2. The central 40 × 40 mm region was heterogeneous, and was subdivided into squares. Within alternate squares gKmax was set to either 0.004 mS mm-2 or to a specific lower value (see below). Thus the virtual tissues had a border region with short APD, and a central heterogeneous region divided into a chequerboard with alternating squares of either short or long APD  of 0.003 mS mm-2. The diffusion coefficient in the whole virtual tissue was set to 0.1 mm2 ms-1.In our reference virtual tissue, the 40 × 40 mm heterogeneous region was divided into 16 squares giving heterogeneity with a spatial scale of 10 mm. In half of the 16 squares spatial scale of heterogeneity by changing the size of heterogeneity in the reference virtual tissue from 10 mm to 20 mm and 5 mm  at every grid point. We estimated APD dispersion during steady pacing with three measured that have been used in experimental studies [APDdiff). Second we measured the standard deviation of APD (APDSD) across the whole virtual tissue. Finally we measured the APD difference between each grid point and its neighbours 1 mm above, below, left and right, and determined the maximum value of measurements throughout the whole virtual tissue (maxLD) [Action potentials were initiated in each virtual tissue by holding the membrane voltage along one edge at 0 mV for 2 ms. We measured APD to 90% recovery (APD studies . First w (maxLD) .gKmax supported propagating plane waves following S1 stimulation along one edge. These propagating plane waves had a depolarising wavefront and a repolarising wave back both aligned parallel to the edge that was stimulated. A premature S2 stimulus delivered to the same edge as the S1 stimulus therefore resulted either in block or in a propagating plane wave.A spatially homogenous control virtual tissue with uniform block if the S2 stimulus failed to propagate, re-entry if the S2 stimulus elicited re-entry that completed more than one cycle, wavebreak if the S2 stimulus elicited a wave that broke but did not re-enter, or propagation if the S2 stimulus elicited a wave that propagated without wavebreak.In the heterogeneous virtual tissues the wave back was not a plane wave because some regions repolarised more quickly than others. A premature S2 stimulus could produce a wavefront that would encounter a mixture of recovered and refractory tissue, and hence elicit wavebreak and re-entry. We therefore assessed vulnerability to re-entry in the heterogeneous virtual tissues by delivering two S1 stimuli to one edge at 500 ms intervals, and then a premature S2 stimulus to the same edge. We varied the timing of the S2 stimulus in steps of 1 ms. The virtual tissue response was characterised as either susceptibility to re-entry, and to the range of S2 intervals as the width of the susceptible window.Vulnerability to re-entry is typically estimated by applying a local premature stimulus that interacts with repolarising tissue, and the vulnerable window is the range of stimulus strength and timing that elicits re-entry. In this study, we investigated the initiation of re-entry by S1 S2 stimulation from the same stimulus site, and we estimated the vulnerability of each virtual tissue to re-entry from the range of S2 intervals that resulted in either wavebreak or re-entry. To avoid confusion we refer to our estimate of vulnerability as + channel and conductance of the time independent K+ channel gK1.We made a preliminary investigation into two candidate mechanisms for reducing susceptibility, inactivation of the Na+ current to support a propagating wavefront [+ channels have not recovered from inactivation, then a propagating wave blocks and dissipates [+ channel inactivation is controlled by the j-gate in the LR1 model [+ channels from inactivation throughout the virtual tissue by multiplying the time constant of Na+ channel inactivation τj by 10 [Block of an action potential occurs if there is insufficient Naavefront ; when Nassipates . Na+ chaR1 model . We prolτj by 10 .+ current iK1 is a voltage dependent current that holds the membrane at its resting potential. It is activated during repolarisation and at rest, and is also activated close to the core of re-entrant waves [iK1 throughout the virtual tissue.The time independent Knt waves ,31. We i90 in variants of the virtual tissue during pacing at a cycle length of 500 ms from the bottom edge. The three columns show the effect of changing spatial scales  or re-entry (orange), and hence an increase in the width of the susceptible window The lower bound of the susceptible window where S2 was blocked depended on the refractory period of the border tissue with short APD, and was not greatly affected by changes in spatial scale, Δgkmax or strength of cell-to-cell coupling. The upper bound showed a similar trend to the measures of APD dispersion shown in Figure gkmax and weak cell-to-cell coupling associated with a wide susceptible window.Figure The virtual tissue with a spatial scale of 20 mm showed a different pattern of susceptibility compared to the others, with two ranges of S2 that initiated wavebreak and two ranges of S2 that initiated re-entry. This behaviour is illustrated in Figure APDdiff and reveals an approximately linear relationship. The correlation coefficient R2 = 0.99, which indicates a strong association between APDdiff and susceptibility. The interception of the line with the APDdiff axis also suggests that for the S1 S2 configuration used in this study, the susceptible window falls to zero for APD dispersion less than 20 ms. The association between the other measures of APD dispersion  effect on maximum and minimum APD, the width of the susceptible window was decreased from 56 ms to 39 ms. The lower bound of the susceptible window moved from 198 to 216 ms, reflecting an increase in the refractory period of the virtual tissue as well as more prominent conduction velocity restitution.Although prolonging τgK1 increased current flow across the membrane during repolarisation, shortening APD by about 10% and decreasing APDdiff from 70 ms to 49 ms. The width of the susceptible window was also reduced from 56 ms to 42 ms when gK1 was doubled. The lower bound of the susceptible window moved from 198 ms to 175 ms, as a result of the shorter APD.Doubling gkmax, and strength of cell-to-cell coupling on APD dispersion and susceptibility to re-entry. Wavebreaks and re-entry can be created in cardiac tissue when an activation wavefront encounters a gradient of recovery. Experimental studies have therefore found an association between increased APD dispersion and greater susceptibility to re-entry because a premature stimulus is more likely to be blocked in tissue with regions of prolonged APD. In this study we found that large spatial scale heterogeneity, large Δgkmax, and reduced strength of cell-to-cell coupling all increased both APD dispersion and susceptibility to re-entry. Tissue heterogeneities produce APD dispersion, and APD dispersion is modulated by electrotonic current flow [gkmax affected APD dispersion directly, whereas changing the strength of cell-to-cell coupling affected electrotonic current flow. This study indicates that each of these factors could be an important component in arrhythmogenesis, and that the susceptibility of a heterogenous tissue to re-entry can be estimated from simple measures of APD dispersion.In this study we have used a computational model of cardiac tissue to dissect out the effects of spatial scale, Δent flow . In thisgkmax and strength of cell-to-cell coupling in the experimental study are difficult to establish. Nevertheless the initiation of re-entry by block and retrograde activation in the region with prolonged APD followed a broadly similar pattern to our simulations, although the activation pathways in the experimental study were more complex, presumably due to anisotropic conduction in the rabbit ventricle. A decrease in strength of cell-to-cell coupling results in slowed conduction, and this is a common finding in tissue damaged by ischaemia, infarction [Although the spatial scale of heterogeneity has been identified as potentially important in other studies , there ifarction , and othfarction . Severalfarction ,35-37.Recent computational studies have addressed the influence of heterogeneous acetylcholine distribution on the vulnerability to and stability of re-entry in the atria ,32. The The dynamical behaviour of APD is recognised as important not only for the stability of re-entry  but alsoThe effect of heterogeneity on the stability of spiral waves has been investigated by Xie et al . This stNormal ventricular tissue is remarkably resistant to the initiation of re-entry, but this robustness is greatly reduced by actions that increase the spatial dispersion of refractoriness. In this computational study we have shown that regional differences in repolarisation have an interlinked effect not only on the initiation of re-entry but also on measures of APD dispersion. Measures of APD dispersion are valuable in clinical practice because they could provide an estimate of arrhythmia risk, and various indices have been developed in experimental studies . In our + channels from inactivation can be prolonged, or if the conductance of the iK1 channel can be increased. The effect of this kind of intervention in the intact heart may however be more complex. Other computational studies have shown that modifying the kinetics of the Na+ channel can have a pro-arrhythmic effect. Delaying recovery of Na+ channels from inactivation can increase the slope of the APD restitution curve and hence the likelihood of alternans and re-entry [+ channel conductance increases the vulnerable window [iK1 channel in experimental studies [iK1 channel conductance on susceptibility to re-entry investigated in the present study, it does highlight the potential importance of this channel for the mechanisms of re-entry.These preliminary investigations suggest that, in our model, susceptibility to re-entry could be reduced if recovery of Nare-entry , and rede window . Differe studies . AlthougThe influence of individual ion channel currents on the initiation and subsequent behaviour of re-entry is an important direction for future research, but will require more biophysically detailed cell models than the LR1 model used in this study.The electrical behaviour of cardiac tissue is complex, and depends on processes that act at tissue, cell, sub-cellular, and molecular levels. Computational models of electrical activation and conduction in the heart aim to simulate processes that are relevant to the research question, and simplifications are made accordingly. This study involved a large number of computations to establish susceptibility to re-entry, and so we chose to use a model that was a compromise between fidelity to real cardiac tissue and computational requirements.2+ storage and release have been developed [2+ handling may become heterogeneous in addition to APD, and so it is possible that susceptibility to re-entry could also be modified if this additional feature is taken into account.More detailed versions of the LR model and others incorporating a fuller description of ion channels, pumps, exchangers, as well as Caeveloped ,25,45,46In the present study we chose to use an idealised geometrical heterogeneity based on square regions because this approach allowed us to assess the initiation of re-entry under well controlled conditions. In real cardiac tissue we would expect the heterogeneities to be much more irregular in shape and gradient, and the conditions that favour re-entry to be dependent on the relative location of the heterogenous region and the stimulus site.The behaviour of re-entry in 3 D tissue is more complex than in 2 D, especially when the effects of rotational anisotropy and transmural differences in action potential shape and duration are taken into account . StudiesRHC conceived and designed the study, wrote the simulation code, and ran the simulations. AVH participated in the study design, and helped draft the manuscript. Both authors read and approved the final manuscript.Movie relating to Figure 5aClick here for fileMovie relating to Figure 5bClick here for fileMovie relating to Figure 5cClick here for fileMovie relating to Figure 7aClick here for fileMovie relating to Figure 7bClick here for fileMovie relating to Figure 7cClick here for file


Vif functions to counteract an anti-retroviral cellular factor in non-permissive cells named APOBEC3G. The current mechanism proposed for protection of the virus by HIV-1 Vif is to induce APOBEC3G degradation through a ubiquitination-dependent proteasomal pathway. However, a new study published in Retrovirology by Strebel and colleagues suggests that Vif-induced APOBEC3G destruction may not be required for Vif's virus-protective effect. Strebel and co-workers show that Vif and APOBEC3G can stably co-exist, and yet viruses produced under such conditions are fully infectious. This new result highlights the notion that depletion of APOBEC3G is not the sole protective mechanism of Vif and that additional mechanisms exerted by this protein can be envisioned which counteract APOBEC3G and enhance HIV infectivity.The viral infectivity factor, Vif, of human immunodeficiency virus type 1, HIV-1, has long been shown to promote viral replication  Retrovirology article [In contrast to most animal viruses, infection with the human and simian immunodeficiency viruses results in prolonged, continuous viral replication in the infected host. Remarkably, viral persistence is not thwarted by the presence of apparently vigorous, virus-specific immune responses. Several factors, including the evasion of an innate cellular anti-viral defense by HIV-1 as discussed in a recent  article . Interes article -5. Earli article ,6.APOBEC3G is a virion-encapsidated cellular protein that deaminates dC to dU in minus-strand viral cDNA during reverse transcription -10. The vif+ virus are not known; but, current observations are that APOBEC3G confers a major deleterious effect to the HIV-1 genome when the Vif protein is absent. Historically, Vif has been known to play a dramatically important role in HIV-1 infectivity [vif-defective virus can replicate in some permissive cells such as Jurkat and SupT1 cells, but cannot replicate in other non-permissive cells such as macrophages, primary human T cells, and some restrictive T cell lines [The effects of APOBEC3G and its G-to-A deaminase activity on the survival of wild type HIV-1 ectivity ,13. Vif ectivity -17. HIV-ll lines -20. For ll lines .Following on the heels of that initial observation, an enormous amount of effort emerged from several laboratories directed at elucidating how Vif mechanistically counteracts APOBEC3G in order to protect HIV-1 . These results strongly support a new mechanistic function of HIV-1 Vif protein, complementing the model where Vif counteracts the inhibitory effects of APOBEC3G by enhancing its degradation via ubiquitin-proteasome pathway  may indicate an alternative protective mechanism used by HIV-1 to eliminate innate cellular immunity. Nevertheless, we cannot exclude that HIV-1 uses Vif to exert multiple mechanisms to synergistically and more effectively inhibit the anti-viral activity of APOBEC3G.m pNL-A1 . Even thytoplasm ,46. If AIn conclusion, the existence of two different mechanisms may represent two faces of the same coin, with the common goal of inhibiting APOBEC3G's anti-viral activity. As is often the case with new findings, new questions are posed. The link between APOBEC3G's enzymatic function, its degradation pathway, and its incorporation into virions in the presence of Vif is certainly to require additional attention. Answers to these questions are likely to keep many of us busy for the foreseeable future.None declared.The abbreviations used are: HIV-1, human immunodeficiency virus, type 1; Vif, Viral Infectivity Factor; SIV, simian immunodeficiency virus; NC, nucleocapsid protein; APOBEC3G, apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3G; APOBEC3F, apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3F PBMC, peripheral blood mononuclear cells; Cul5, Cullin type 5; SCF, skp1-cullin-F-box protein ligase.


PLoS Medicine, Ben Lopman and colleagues argue that although it is right to criticize the lack of evidence on unsafe medical injection, field data are hard to collect. They note that in the only published study addressing this issue, Kiwanuka and colleagues found no link between unsafe injections and HIV spread in rural Uganda. In an effort to “inform the debate” further, Lopman and colleagues looked at the association between HIV and unsafe injection practices in rural Zimbabwe.Of the 40 million people worldwide with HIV, 30 million live in the developing world. By far the worst hit region is sub-Saharan Africa, where nearly four million children have lost one or both parents to HIV/AIDS since 2000. Is heterosexual transmission the driving force behind the HIV epidemic in sub-Saharan Africa? In a controversial debate, some researchers have suggested that other factors such as unsafe medical injection practices may also be to blame, and that by overlooking, and even suppressing, analysis of this possible route of transmission, the current focus on preventing sexual transmission may be misguided. In this month's The team analyzed data from adults in Manicaland, a rural part of Zimbabwe, who were taking part in the Manicaland HIV/STD Prevention Study. In 1999 and 2000, eligible patients were tested for HIV and surveyed (86.7% were HIV negative at the start of the study), and were followed up three years later. The team collected survey data on injections in the patients, who were male and female adults aged 15 to 54 years old, and tested for an association between injection exposure and HIV infection. In 2002 and 2003, 505 of the men and 1,342 of the women, representing a 69.7% follow-up, were again interviewed and tested for HIV infection. Of these, 40% reported having had an injection or needle prick during the study period. A total of 67 patients developed HIV during the study; of these 13 (19%) said they had not had sex during the study period and 40 (60%) said they had not had an injection. The statistical analysis found no significant association between injections and HIV infection in men or women.Patients who had HIV when the study began did not have higher rates of injections. Instead, injections were highly associated with childbirth and pregnancy. But since HIV–positive women have reduced fertility, a reduction in the use of maternal services may partially explain why injections were not more common in these HIV-positive patients. In this study, the strongest predictor of HIV infection was symptoms of sexually transmitted disease.Despite problems of recall bias and under-reporting of sexual activity—a particularly difficult problem in studies in Africa—sexual behavior is consistently linked with HIV incidence. Where does this leave the debate over injections in Africa? Certainly, for this community, they do not seem to be a major source of HIV infection, and local policy-makers would therefore do best to concentrate on the prevention of sexually transmitted infections.