Thomas Jefferson University Science Outreach Program brings the scientific method into the classroom If you ask the average ten year old in America what a scientist looks like, they almost always describe an older man with crazy white hair and a lab coat. If you ask a group of adolescents how many have looked through a microscope, few raise their hands. If you discuss the implications of genetic research with a group of high school students, they're likely to cut your next class. The reason why these students have such profound stereotypes of scientists and are less than enthusiastic about science's impact on society is simple—the lack of exposure they receive during their pre-college education. According to a preliminary study conducted at Leicester University in England, students are often repeatedly confronted with stereotypes of science and scientists via television, cartoon, and comic book characters as well as uninformed adults or peers .A university set in a major city has the resources to change the mindset of urban students and engage them in the exciting field of science. At Thomas Jefferson University (TJU), located in Philadelphia, Pennsylvania, a team of scientists and educators has developed a program that breaks down the stereotypes of the science field and allows students to engage in real, live experiments at their own schools.No Child Left Behind Act, which guides school funding policy, currently places an emphasis on literacy and math in schools, with the result that best programs and practices in education are increasingly directed toward these two areas. Unfortunately, science education has become a lesser priority. Teachers are not given adequate resources to allow students to “get their hands dirty” during science lessons. If we want a society that is interested and knowledgeable about the need for scientific research, the basic principles of the life sciences need to be integrated early in the pre-college curriculum , and our High School Mentorship program held each summer. One of the main goals of TJUSOP is to reach students from ethnic and economic groups that are underrepresented in the scientific community. We have successfully partnered with the School District of Philadelphia, where 84.9% of the students come from ethnic backgrounds other than Northern European and 80% of the students are eligible for free or reduced-cost meals. This district, among many others, receives pre- and post-instruction for all teachers and students at no cost to the district, allowing it to improve the quality of its science education. Although we target these school districts, it is important to note that the US is facing a problematic decrease in the number of Americans, of any background, entering the science and engineering workforce. According to a National Science Foundation report, “If action is not taken now to change these trends, we could reach 2020 and find that the ability of U.S. research and education institutions to regenerate has been damaged and that their preeminence has been lost to other areas of the world”  male and a wild-type (dominant trait) female zebrafish in order to observe what the offspring will look like. Students form hypotheses, such as that the young offspring will look like the mother and the older offspring will be striped. Throughout the week, students observe and record embryos developing a head, tail, and notochord and pigment development. By the end of the experiment, a live heartbeat can be seen as well as the individual blood cells flowing throughout the larvae using a stereomicroscope TJUSOP provides .Grade-specific scientific journals are given to the students. The journals contain an introduction to TJUSOP and the experiment, background information about zebrafish in research, scientific vocabulary words used throughout the unit, and a word search activity. Students are given the title of “Junior Scientists” in grades 4 and 7 and “Student Scientists” in grade 10 and are asked to record the research question, a hypothesis, daily observations, and the conclusion of the experiment .TJUSOP allows student participants to use scientific tools, talk with real scientists, and gain scientific knowledge so they can become informed members of their communities. Upon asking a fourth-grade student why she thought it was important to learn about science using zebrafish and the microscope, the student wrote, “I think it is important because we can find facts about oursefs [sic].” This sounds like a good start.Schaefer@mail.jci.tju.edu or visit http://www.kimmelcancercenter.org/scienceoutreachprogram.For more information about the program, or if you would like to get involved in the initiative, please contact Jamie Schaefer, at jamie. E-mail:


Blind individuals often demonstrate enhanced nonvisual perceptual abilities. However, the neural substrate that underlies this improved performance remains to be fully understood. An earlier behavioral study demonstrated that some early-blind people localize sounds more accurately than sighted controls using monaural cues. In order to investigate the neural basis of these behavioral differences in humans, we carried out functional imaging studies using positron emission tomography and a speaker array that permitted pseudo-free-field presentations within the scanner. During binaural sound localization, a sighted control group showed decreased cerebral blood flow in the occipital lobe, which was not seen in early-blind individuals. During monaural sound localization (one ear plugged), the subgroup of early-blind subjects who were behaviorally superior at sound localization displayed two activation foci in the occipital cortex. This effect was not seen in blind persons who did not have superior monaural sound localization abilities, nor in sighted individuals. The degree of activation of one of these foci was strongly correlated with sound localization accuracy across the entire group of blind subjects. The results show that those blind persons who perform better than sighted persons recruit occipital areas to carry out auditory localization under monaural conditions. We therefore conclude that computations carried out in the occipital cortex specifically underlie the enhanced capacity to use monaural cues. Our findings shed light not only on intermodal compensatory mechanisms, but also on individual differences in these mechanisms and on inhibitory patterns that differ between sighted individuals and those deprived of vision early in life. Blind individuals who localize sound better than sighted subjects recruit visual cortical areas in the process Animals and humans deprived of vision have been shown to have enhanced nonvisual perceptual abilities. Indeed, many blind individuals are extremely efficient in tactile processing, including Braille reading; the talent manifested by some well-known blind musicians, singers, and even piano tuners is often in part attributed to the fact that they were blind since their youth. One may conclude that blind persons should be better in nonvisual tasks since they compensate for their lack of vision by focusing on their remaining modalities. Many studies have in fact shown that some early-blind human subjects outperform sighted persons in nonvisual tasks, such as speech perception ,2,3, unfAnimal studies provide some insight as to the neural substrates underlying such enhanced capacities (reviewed in ). For inSeveral studies using neuroimaging techniques have also established that posterior visual areas in blind individuals may be active during the performance of nonvisual tasks such as Braille reading ,22, memoLocalization of sound, a very important function for the blind, is one domain in which it is particularly useful to study the cross-modal interactions that may occur following visual deprivation. This task entails integration of binaural and monaural cues to derive spatial information. In accordance with the idea that nonvisual processing can be enhanced in the blind, a prior study demonstrated that a subset of early-blind subjects was more accurate than sighted controls (SIG) at localizing sound sources, specifically when using monaural cues . These fThus, in the present study, subjects were first studied in an anechoic chamber using binaural and monaural sound localization tasks. Depending on their performance at the monaural task, they were divided into three groups: (i) early-blind participants who could localize the sounds more accurately than control subjects (early blind with superior performance [EBSP]); (ii) early-blind participants who were unable to localize the sounds any more accurately than controls ; and (iii) SIG. The same localization task was next adapted so that it could be carried out within the positron emission tomography (PET) apparatus, using a speaker array which permitted pseudo-free-field presentations . Two conThe hypothesis tested was that blind persons showing supranormal performance do so because they recruit visual cortical areas to carry out the task. We therefore predicted that they would show activation in visual areas specifically during the MSL task, and not during the binaural or control tasks. The other blind group, which does not have enhanced MSL ability, should not show this activation pattern. We further hypothesized that the degree of visual cortical activity would be predictive of individual differences in the behavioral performance of the monaural task.30, 225 = 6.299, p < 0.01), localization being more accurate in the EBSP group, especially on the side of the obstructed ear. The performance of the EBNP did not differ from that of the SIG. These findings confirm the previous study of Lessard et al. [In the anechoic chamber experiments, only five of the 12 early-blind subjects could accurately localize the sounds monaurally, whereas most of the sighted subjects could not . In orde30,225 = 4.058, p < 0.01; see In contrast, all 19 subjects tested, whether sighted or early blind, could correctly localize sounds binaurally . DespiteDuring binaural localization of the sound, when compared to the control task , cerebraAnother finding from the binaural versus control task contrast was a small region of activation in the visual cortex  of the EBSP group, but not in the other two groups . FinallyOf greatest relevance to our hypotheses, during monaural stimulation (one ear plugged), as compared to control task, right-hemisphere striate and ventral extrastriate areas showed increased CBF only in the EBSP subset of blind subjects . For theWhile parietal and frontal activations were also seen in the three groups, temporal activations were not found for monaural stimulation, the EBNP group showing even some deactivation in temporal areas. The EBSP group also showed some activation in the right cerebellum  but two other significant foci were found in right dorsal extrastriate cortex , and striate cortex . Two of these foci are close to the ones identified in the analyses presented for MSL. These findings support the hypothesis that the visual cortex is directly involved in localizing a sound stimulus in the monaural condition.In order to assess whether occipital activations have a functional role in auditory localization, independent voxel-wise covariation analyses were carried out across the entire group of blind individuals. Irrespective of the group to which they had been assigned, the individual absolute error score was entered as a regressor in the analysis examining covariation with CBF change between overall accuracy at the localization tasks and activation across the entire brain volume, following the procedure outlined by Paus et al. . For MSLThe imaging results of this study support our hypothesis that blind persons recruit occipital areas in the context of auditory localization and, more importantly, the correlation observed with MSL performance strongly suggests that individual differences in reorganization of the occipital cortex have behavioral consequences. Hence, this relationship does not support the possibility that the recruitment is a nonspecific coactivation or a pathological response. Instead, these results suggest that visual cortex is specifically recruited to process subtle monaural cues more effectively.Functional activation of the visual cortex by nonvisual stimulation in the blind has already been shown in several previous studies. Activation of primary and secondary visual areas was observed during Braille reading and other tactile discrimination tasks in early-blind persons . This taOf greatest relevance for the present findings are the results of Weeks et al.  in whichIt is interesting to consider the function of the right occipital areas, which seem to be important for MSL. Monaural cues  are involved in the localization of sounds when one ear is obstructed, or in unilaterally deaf subjects . HoweverCross-modal plasticity may not necessarily be the only mechanism to explain the present results. Some studies have also shown intramodal plasticity in auditory cortex of the blind. For example, the tonotopic region of area A1 in blind persons seems to be enlarged compared to that of sighted subjects , presumaExperience-driven improvements in auditory localization can occur without necessarily invoking cross-modal recruitment. Indeed, some studies have shown that in MSL tasks, practice may lead to increased performance in the case of unilaterally deaf patients  or even Why do some blind persons and not others acquire superior monaural sound localization skills? It may be that these changes are entirely experience-driven. That is, some blind persons may have had more practice navigating or using auditory cues to explore their environments. On the other hand, the individual differences we observed in the degree of cross-modal plasticity could reflect innate factors that remain to be identified. An additional explanation may be that the blind persons who are not better than normal at sound localization may be superior in other nonvisual tasks, such as Braille reading or other somatosensory discrimination, because the visual cortex was preferentially recruited to carry out these tasks instead of auditory ones. If this is the case, it is possible that cross-modal plasticity is limited to a certain extent, such that recruitment of these areas by one modality inhibits recruitment by another. These speculations will have to be explored systematically in future studies.What is the nature of the mechanism implied in the processing of auditory stimuli by visual cortical areas? The specific areas of visual cortex recruited may provide a clue. The analyses yielded one peak in the right V1 area. V1 has already been shown to be activated in other studies examining Braille reading ,22,34, vThe parietal activation observed in all groups during BSL suggests that these areas are important when carrying out the task used in the present experiment see . This fiDuring BSL, the sighted control group (SIG) showed a deactivation in both extrastriate and striate areas of the occipital lobe, a phenomenon that was not observed in either subset of early-blind individuals. Many previous studies with sighted subjects have shown that following stimulation in one modality, cross-modal inhibition occurs in the unattended modalities ,64,65,66Deactivation of primary visual areas has also been seen in sighted subjects during a tactile discrimination task, whereas in blind subjects activation was shown in the same area . Within The present study establishes for the first time in certain early-blind persons a clear relationship between monaural sound localization performance and increased CBF in occipital areas. Indeed, some of the blind persons showed occipital activation that appeared to be functional, since this phenomenon was correlated with a supranormal performance in MSL. This finding suggests that visual deprivation from an early age could lead to important cross-modal plasticity and give blind persons an advantage in using spectral cues to carry out a crucial everyday task, sound localization. Moreover, we report that inhibitory patterns differ between early-blind and sighted individuals. Under binaural conditions, the SIG seemed to inhibit part of the occipital areas when localizing sounds, but this was not the case for either group of blind persons. This differential pattern may provide clues as to how different parts of the brain normally interact during unimodal stimulation, and further suggests that these interactions may be modified in the absence of a sensory modality.It may also be important in future studies to investigate whether blind persons can recruit visual areas in other auditory tasks, for example in a task in which spectral and level cues are relevant but in a nonspatial context. Along the same lines, a spatial discrimination task not requiring the explicit localization of the sounds may also be of interest. One can thus verify that this special competence of some blind persons can be generalized in different auditory contexts other than MSL. Indeed, it would be interesting to know whether this ability is related to more complex tasks such as navigation, obstacle detection, or analysis of sound flow, for example when the subject moves or objects move around the subject. Similarly, it may be pertinent to investigate whether special training or substitution devices, frequently described in the literature, not only improve the relevant behavior but also facilitate cross-modal plasticity.The participants were seven healthy sighted volunteers and 12 early-blind subjects who had lost their vision before puberty, most of them in the first few years of life see . In eachSubjects were asked to localize sounds binaurally or monaurally while they were seated in the anechoic room. The acoustic apparatus used to test sound localization, previously described in detail , consistThe stimuli were broadband noise bursts that lasted 30 ms . The sound pressure level (SPL) was maintained at 40 dB. A stimulus was delivered through a randomly selected loudspeaker and repeated five times for each position. A buzzer warned the subjects that a sound was about to be presented and that they should maintain a stable head position and fixate straight ahead. Compliance with all instructions was ascertained by an experimenter remaining in the chamber behind the subject. The response consisted of pointing with the dominant hand toward the apparent source of stimulation. Lines graduated in 1° steps were drawn on the perimeter, and the response of the subject was recorded by the experimenter.For monaural testing, one ear was plugged with a combination of an ear plug (mean attenuation = 37.5 dB SPL) and a hearing protection muff (mean attenuation = 29 dB SPL). In order to compare the overall accuracy in localization between the subjects, the absolute error score was utilized in both the anechoic chamber and scanner experiments. This value is the average of the difference  between the correct position and the response for each trial. To allow combining of data from subjects with left or right ear plugged, the behavioral results were transformed such that the left side was arbitrarily assigned to correspond to the obstructed ear. Thus, in the behavioral data presented, negative angles on the abscissa correspond to the obstructed side, while positive angles correspond to the unobstructed side.In this part of the experiment, subjects were asked to localize sounds binaurally or monaurally while they were lying within the scanner. Monaural testing was carried out using the same ear attenuation procedure as used in the anechoic chamber. All conditions, localization tasks, and their specific control task, were part of a larger study. These conditions were counterbalanced across subjects for the order of scan conditions. Approximately half of the subjects within each group received the ear plug in the left and the other half in the right ear during the monaural part of the scanning session. Auditory stimuli were presented using a circular array of nine speakers, positioned 15° apart from ±60°, and having a radius of 24 cm . The arrn = 6) wearing binaural earplugs and ear muffs could not localize any of the stimuli from the speakers. The control task consisted of pointing in alternation to the left and right , after hearing a stimulus pair presented always in the frontal (0°) position. Two control tasks were tested, one binaural and one monaural; in the monaural case the same ear was plugged as was used for the localization task for that individual. Thus, four conditions were tested in all subjects:BSL, binaural control task, MSL, and monaural control task.The stimuli were two broadband noise bursts that lasted 30 ms and were separated by a 0.5-s intrapair interval while the intertrial interval was 2.5 s. Each pair of stimuli was presented from a single speaker at 60 dB SPL, as measured at the center of the array. Each of the nine speakers was utilized 12 times in random order for a total of 108 trials for each condition. The behavioral tasks were started around 15 s before the beginning of data acquisition with the scanner. The response consisted of pointing with a joystick, placed at the subject's side, to the apparent source of stimulation. It was ascertained before the experiments that all subjects were familiar with the use of the joystick and with the task requirements. In a series of preliminary experiments, it was verified that subjects (2O15 water bolus method [http://www.mrc-cbu.cam.ac.uk/Imaging/). Statistical analysis was performed applying the method described by Worsley et al. [t value of 3.5 was used for significant changes in CBF during exploratory searches. However, a t value of 3.0 was used for a priori regions of interest, such as occipital areas.PET scans were obtained with a Siemens Exact HR+ tomograph  operating in three-dimensional acquisition mode. The distribution of CBF was measured during each 60-s scan using the Hs method . MRI scas method . Each mas method , resultiy et al. ; covariay et al. . A t valFigure S1Sagittal and coronal images contrasting BSL to the control task. All three groups showed increased CBF in the right inferior parietal lobe (as shown by the red arrows), consistent with other neuroimaging studies of auditory localization. X and Y coordinates refer to standardized stereotaxic space.(2.2 MB TIF).Click here for additional data file.Table S1(35 KB DOC).Click here for additional data file.


The relationship between body mass (M) and standard metabolic rate (B) among living organisms remains controversial, though it is widely accepted that in many cases B is approximately proportional to the three-quarters power of M.The biological significance of the straight-line plots obtained over wide ranges of species when B is plotted against log M remains a matter of debate. In this article we review the values ascribed to the gradients of such graphs , and we assess various attempts to explain the allometric power-law phenomenon, placing emphasis on the most recent publications.Although many of the models that have been advanced have significant attractions, none can be accepted without serious reservations, and the possibility that no one model can fit all cases has to be more seriously entertained. In 1932, Kleiber published a paper in an obscure journal , but itThe main conclusions from this section are: (a) different organs make different contributions to the scaling of whole-organism metabolic rates; (b) differences at the cellular level make relatively small contributions to scaling at the organ level; (c) these differences at cellular level might disappear altogether after several generations in culture. The most striking conclusion is (b). It implies that allometric scaling of metabolic rate does not after all, for the most part, reside in cellular function but at higher levels of physiological organisation. If this is the case, then the alleged applicability of Kleiber's law to unicellular organisms is called into question.0.25 (see previous section). Coulson's approach contrasts with traditional biochemical measurements: the principal variable is not the concentration of a resource but the supply rate; metabolic activity depends on encounter frequency not concentration. This perspective merits further development, particularly by extension to the cell internum b = 0.75.example, ,73,80.et al. accept that some of their proposed hierarchical supply networks might be "virtual" (as in mitochondria) rather than explicit , but it is not clear why such networks must always have the same geometry. For instance, why should the intracellular network discussed by Hochachka .et al. made no attempt to explain why the values of b are typically around 0.75, as West et al. and others have done. The model is phenomenological, not physical and mathematical; their equations are not derived from any fundamental principle(s). Moreover, their data cover only some three orders of magnitude of body mass, whereas many studies have involved much wider ranges. This might make their overall b values misleading [b values are obtained, not necessarily consistent with published data; but on the other hand, the published data might not be correct.However, Darveau sleading  or, altesleading . When thiMb(i), where a is a constant coefficient, cI is the control coefficient of the ith step in the cascade and b(i) is the exponent of the ith step. By definition, the sum of all the cI values is unity. Darveau et al. did not derive this equation; they stated it. They also stated that the overall exponent, the b term in the Kleiber equation, is a weighted average of all the individual b(i) values, the weighting being determined by the relevant control coefficients. It has been suggested that this leads to untenable inferences. For example, since the units of B and a are fixed, the units of cI must depend on those of b(i); but by definition, both b(i) and cI must be dimensionless. Also, according to the basic equation, the contribution made by each step to the overall metabolic rate depends on the units in which body mass is measured. If this criticism is valid then it is impossible to evaluate the model as it stands, because any attempt to align its predictions with experimental data would be meaningless. Another reservation about this model is that it does not purport to apply to all taxa, as the West et al. model does; it relates only to metazoa, and in particular to homoiotherms. However, most of the relevant data in the literature concern homoiotherms.In the first published account of this model  the mathI(M/m)b(i), where the constant a is described as the "characteristic metabolic rate" of an animal with characteristic body mass m. This eliminates the problem of mass units, because the mass term has been rendered dimensionless; and it is mathematically simple to express control coefficients in dimensionless form. The revised equation might therefore be immune to some of the criticisms levelled at its predecessor. However, some of the earlier reservations remain: the equation remains phenomenological, not physical or geometrical; and the restriction in its range of application is explicit. Nevertheless, these considerations by no means invalidate the model. Indeed, it is supported by data from experiments in exercise physiology [A subsequent publication from this group  re-stateysiology .et al. [et al. [et al. [The models of Darveau et al. ,115, Ban [et al. ,95 and W [et al. ,102 all b) on an intra-specific basis from a total of 113 species. He came to the conclusion that there was no single universal value of b. This evidence alone must make us more sceptical of there being some unifying law involved that demands that b holds close to 0.75. There is clearly no consensus otherwise Nature, Science and the Proceedings of the National Academy of Sciences USA would cease to publish so regularly many of the articles to which we have referred. The subject is not only unresolved, but remains very much within the general interest of biologists.Several explanatory or quasi-explanatory models have been proposed for the allometric scaling of metabolic rate with body mass. Most of them have significant attractions, particularly the most recent ones, but none of them can be unreservedly accepted. The variability of experimental data leaves room for doubt that Kleiber's law is universally or even widely applicable in biology ,117, yetKleiber's law remains a fascinating mystery; possibly a delusion, possibly a widespread or even ubiquitous biological phenomenon for which no entirely satisfactory account has yet been offered. Recent developments, though mutually conflicting as they stand, have the potential to lead to new insights and to uncover one or more general biological principles that will have a profound impact on our understanding of the living world.


Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function.PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis.We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics. Caenorhabditis elegans has established that coordinate transcriptional regulation of functionally related genes occurs on a broader scale than was previously recognized, encompassing at least half of all cellular processes in yeast Click here for additional data file175 lists of genes that are expressed in individual tissues, highest in individual tissues, or specific to individual tissuesClick here for additional data file


New York City health officials announced on February 11, 2005 that a patient rapidly developed full-blown AIDS shortly after being diagnosed with a rare, drug-resistant strain of HIV-1. The New York City Department of Health issued an alert to all hospitals and doctors and a press conference was held to announce the emergence of an aggressive HIV-1 strain that may be difficult to treat and that appears to trigger rapid progression to AIDS. Is the panic justified? The two phenomena of rapid disease progression and multi-drug resistance, which are combined in the aggressive HIV-1 strain, are not unique. HIV-1 causes a persistent infection and this virus is generally not a fast killer. Within the Amsterdam Cohort Studies on HIV-AIDS, it takes on average 8.3 years from the time a person is first infected with HIV-1 for AIDS to develop, and another 17 months from AIDS to death -5. ThereDrug-resistant HIV-1 variants usually have reduced replication capacity compared to a wild-type virus due to the mutations in the Reverse Transcriptase and Protease enzymes . This loOverall, this case seems relatively rare but not necessarily alarming. Increased attention is not necessarily bad, but press conferences should be reserved for situations when a cluster of such transmissions is apparent. The current hype about super aggressive HIV-1 strains seems unfounded.The author(s) declare that they have no competing interests


PLoS Medicine Editors set out their vision for the journalWhy does the world need a new medical journal? In this first editorial the  Take monthly, at no cost; reaches six billion Today the possibilities for a medical journal are almost limitless. The first medical journals reflected the needs of a closed group of doctors. But medicine, its place in the world, and the dissemination of information have changed utterly. So in starting afresh, what should a new medical journal retain, and what should it ditch?PLoS Medicine. You can download and distribute articles without restrictions .Most obviously, we should throw out the old way of disseminating information. In today's electronic age, it is no more difficult, and it is only minimally more costly, to provide access to one million people than it is to one person. So the revolutionary idea of anyone being able to read any article is possible. This idea—open access—which completely challenges the old subscription-based publishing model, is the driving force behind the launch of We have also changed the way we involve the academic community in our journal. Our large global editorial board reflects the diversity of medicine today and is intimately involved in what we do. In particular, members of the editorial board are a crucial part of our peer review process. As academic editors they, along with a senior editor at the journal, take research papers through the peer review process in a way that we believe provides the most constructive and fair review. We are delighted that members of our editorial board have also shown their support for our journal by submitting papers to us, even before we launched.What will we publish? The research article on malaria in this issue reflects our priority of publishing papers on diseases that take the greatest toll on health globally. But we will also publish papers reporting a substantial advance in any specialty, whether that advance is in public health, such as the paper on the global burden of disease; drug effects, such as the paper on the effect of HIV drugs on lipids; or the molecular understanding of disease, such as the paper dissecting out the immune responses in lung disease caused by smoking.PLoS Medicine will be devoted to comment, lively debate, and diverse opinions, in particular giving neglected voices and diseases a place in the limelight. In this issue's magazine section you will see articles from five continents that cover a huge range of topics, from basic sciences (such as the pathology of emphysema) to global public health . You will find diverse opinions—for example, on whether President Bush is helping or hindering Africa's progress towards tackling HIV, and on whether health professionals should routinely screen women for domestic violence (tell us what you think by taking our poll at www.plosmedicine.org). And you'll find case-based learning materials on meningitis linked to an online video and an online quiz.A good general medical journal should also be a place where the global medical community can discuss together what matters to them. The magazine section of The revolutionary idea of anyone being able to read any article is possible.PLoS Medicine's professional editors, should provide any health professional with a quick introduction to an article. Patient summaries provide a starting point for patients to assess the relevance to them of a research paper.Interpretation of results is an essential part of a medical journal's job. Although we expect that many of our readers will be doctors, we hope readers will range from patients wanting to learn about the latest research on their illness, to teachers wanting to use an article in the classroom, to policymakers. Hence, we have several levels of comment on original research. Perspectives, written by an expert, are aimed at readers who are already familiar with the topic. Synopses, written by PLoS Medicine will not accept advertisements for pharmaceutical products or medical devices. Our open-access license allows free distribution of articles, so PLoS cannot benefit from exclusive reprint sales. And we consider as the lowest priority for publication papers that are simply aimed at increasing a drug's market share without obvious benefit to patients.We have decided not to be part of the cycle of dependency that has formed between journals and the pharmaceutical industry, an industry that focuses overwhelmingly on the most profitable drugs, thus sidelining many of the world's health problems. Medical journals have allowed their interests to become aligned with those of the pharmaceutical industry by printing advertisements for drugs, publishing trials designed by drug companies' marketing departments, and making profits on reprints used as marketing tools. We will aim to have the highest levels of transparency in our published papers. We require authors to tell us of any possible competing interests; we in turn will tell readers about them.PLoS Medicine is a journal for the global medical community; we invite you to join in.But, information flow should not be just one-way. Our editorial doors (or at least our E-mail boxes) are always open. We want your feedback on the journal: send us an E-mail or submit an eLetter about any article in the journal, take part in our polls, contribute ideas for the magazine section and submit original research.


A unique feature of the way the book is written, is that individual Chapters can be read out-of-sequence with no loss of continuity. The reader can skip around, omit certain sections, and still glean what he or she needs to know without feeling cheated. That's not easy to do, but this author does it well.I found the material to be written at a very comfortable cognitive level – aimed mainly at medical, upper-level-college, and graduate students – and sequenced in a logical manner, thus making it easy to follow and totally user-friendly. The author has a special talent for reducing complicated concepts to an easily understandable level. For example the Appendix dealing with Genetic Principles gives the best introductory synopsis of this topic that I have seen anywhere; and the "middle Chapters" that address "Mostly About" – Statistics (Chapter 3), Epidemiology (Chapter 4), Screening (Chapter 5), Clinical Trials (Chapter 6), Quality of Life (Chapter 7), and Epidemiology (Chapter 8), are gems!.What are especially nice are the many relevant, easy-to-understand and practical examples, that the author judiciously and effectively intersperses with the theoretical background material; and, the Chapter Summaries that conclude most of them. There is a very well-balanced give-and-take between theory and practice. I also liked the wonderful (sometimes witty) little parables and anecdotes that give the book a charming personality. In all, I would offer my compliments to the author for a "job well done!"


The neutral mutation rate is known to vary widely along human chromosomes, leading to mutational hot and cold regions. We provide evidence that categories of functionally related genes reside preferentially in mutationally hot or cold regions, the size of which we have measured. Genes in hot regions are biased toward extracellular communication , while those in cold regions are biased toward essential cellular processes . From a selective perspective, this organization of genes could minimize the mutational load on genes that need to be conserved and allow fast evolution for genes that must frequently adapt. We also analyze the effect of gene duplication and chromosomal recombination, which contribute significantly to these biases for certain categories of hot genes. Overall, our results show that genes are located nonrandomly with respect to hot and cold regions, offering the possibility that selection acts at the level of gene location in the human genome. Functionally related genes tend to be found in regions of the genome with high or low mutation rates, which implies that natural selection can also operate at the level of gene location Because of the abundant availability of mouse and human genome data , it has SK if synonymous substitutions are neutral—affects gene organization, this would constitute a type of selection complementary to traditional selection on point mutations  Consortium . If the re known , this paBecause mutation rates are regional, mutation rates in genes categories could be influenced by events altering the organization of genes in the genome, such as gene relocation or gene duplication. We therefore analyzed mechanisms by which functional categories of genes may have become concentrated in hot or cold regions. A clustering analysis reveals that the hotness of some categories is enhanced by local gene duplications in hot regions. However, there are strong functional similarities among the hot categories—both clustered and unclustered—as well as among the cold categories. These functional similarities imply that the instances of duplicated categories are not random; i.e., selection may have affected which genes have duplicated and persisted.Mus musculus and Homo sapiens have been measured by several groups on a genome-wide scale . This identification is not without complexities, however, since there are processes that can in principle selectively affect the 4-fold sites. For example, some have argued that exogenous factors such as isochore structure influence the silent sites  measures of SK using mutation models in the Phylogenetic Analysis by Maximum Likelihood (PAML) package  as a defining length scale. This analysis was similar to that of We tested the effect of gene duplications on category rates through a clustering analysis see . If gene10 values. Of the 21 statistically significant hot categories, ten categories had statistically significant clustering (−log10clusterp > 3). For example, the “olfactory receptor activity” category has 223 genes, with a randomly expected number of clustered genes equal to 30.6. The actual number of clustered genes was found to be 190, which has a p-value of less than 10−16. In the set of 37 cold gene GO categories, none had statistically significant clustering. The clustering significance is plotted versus the substitution score z for all the GO categories with at least five members in 10clusterp > 3), but virtually no cold ones.We found that some of the hot gene categories were indeed clustered, but that none of the cold gene categories were. The results of the clustering for the hot and cold categories are displayed in Tables As an example of clustering in the hot gene categories, we considered the olfactory receptors. It is well-established that olfactory receptors occur in clusters throughout the human genome , and we We next attempted to determine whether the high olfactory rates are due to a regional bias. The substitution rates of all genes are plotted in Similar clustering and regional hotness were observed for other hot gene categories. We plot the substitution rates of a cluster of homophilic cell adhesion genes on Chromosome 5 in Several explanations have been proposed that could account for the regional biases in mutation rate , includiTo shed light on the structural properties of the hot and cold mutational regions, we measured the length scale over which substitution rates are correlated. Previously, correlations have been observed in blocks of particular physical (5 Mb)  or genetr(t) is the substitution rate of a gene t basepairs downstream of a gene with substitution rate r(0), and <…> indicates an average over the available data r(t)> = 0 threshold at approximately 11 Mb, and the mouse curve first crosses it at approximately 9 Mb. This suggests that there are multiple phenomena that control the mutation rate of regions, both long (10 Mb) and short (1 Mb) length scale.We observed two notable behaviors: (1) a strong correlation that decays over a region of approximately 1 Mb, and (2) a longer range correlation which plateaus over a region of approximately 10 Mb. At larger distances, correlations are weaker. For example, the human curve first dips below the <We also measured the characteristic short-range correlation length using an exponential fit. The correlation length τ was determined by fitting the data to the functional formA∞ is the correlation at long distances and (A0 + A∞) is the correlation at zero distance. Because of the observed plateauing behavior of the data, we performed our curve fit over the region t ∈ . For the human data, we obtained A0 = 0.83, τ = 1.21 × 106, A∞ = 0.39. For the mouse data, we found values of a similar magnitude , suggesting that short-range mutational processes may be alike in mouse and human. The long-range correlation A∞ was at least an order of magnitude larger than would be expected by chance at all distances up to 10 Mb . Differential duplication rates could force a category to have a mutation rate bias, due to the block structure of the mutation rate and the fact that duplications occur locally.Recently, there has been evidence for selective factors influencing gene location in yeast . This suThe observed categories of hot and cold genes suggest gene locations have been selectively influenced by regional mutation rates. This is because if mutation rates were unrelated to gene function, then the lists of hot and cold categories would be expected to be random; i.e., the lists shown in The hot categories were found to be biased toward receptor activities or roles in extracellular communication. Intriguingly, arguments based on protein-level effects appear applicable to the silent-site hotness of these categories. Cellular receptors and those involved in extracellular communication are the proteins that most directly interact with the environment and are therefore the most likely to have experienced a dynamically changing set of selection pressures. This variability of selection pressures would have made it favorable for them to be in hot regions, in order that new mutations be possible to deal with new stimuli. Examples of hot categories with known protein-level diversification pressures include the olfactory receptors , immune Arguments normally applied to protein-level selection were found to be appropriate for cold mutation rate categories as well. Cold categories were often related to transcription or other regulatory processes. Regulatory proteins should be tuned to interact with many different nucleic acid or protein targets, in contrast with receptor proteins, which typically interact with only a particular ligand. Mutations to regulatory proteins would therefore be expected to be more deleterious, and hence it would be beneficial for them to have low mutation rates. Strong conservation pressures in the cold categories could also be related to their roles as housekeeping genes  or as esSK and AK are under similar pressures. This is consistent with what would be expected if gene locations have evolved to make use of the block structure of the mutation rate, since relocation to a hot (cold) spot would increase propensities for both high (low) AK and SK. More quantitatively, we observed that SK category biases were similar to category biases caused by selection on amino acid changing point substitutions—i.e., selection observable through the ratio A/KSK. We performed a GO z-score analysis on A/KSK . There were eight hot categories common to both the 4-fold and A/KSK classifications  and 17 common cold categories . The strong commonalities between the two types of classification suggest that the selective forces that influenced amino acid changing point mutations also influenced gene locations. The hot and cold categories derived from A/KSK are available as The applicability of protein-level arguments to synonymous mutation rates suggests that AK and SK are correlated beyond levels expected by neutral evolutionary theory  cannot be attributed to duplication events. One alternate phenomenon that could cause cold category biases is gene relocation to cold regions. The concept of relocation brings up a number of questions. First, if cold genes have relocated, this leaves one wondering in what sort of environment cold genes originated. One speculative possibility is that these genes developed in regions of high recombination (the hot regions), which would have allowed for fast duplication and functional diversification, and later dispersed to cooler regions as their functions became fixed. Second, it is unclear whether gene relocations occur frequently enough to account for the observed rate biases. This issue is complicated by the fact that genes have arisen at different times. Many of the cold gene categories occur in diverse sets of tissues and have important regulatory effects, suggesting they should be relatively old. This old age may have allowed them enough time to redistribute through the genome.We verified the correlations of substitution rates along the genome and showed that these correlations lead to an excess of hot and cold genes, confirming studies by Some data issues suggest topics for further exploration. First, the resolution of our analysis is dictated by the structure of the GO taxonomy, which currently has 16,000 categories but is evolving. Our category inferences should become more specific as GO gene assignments improve. Second, multispecies data will be invaluable in revealing the mutations that have occurred in each lineage. One promising early result from human–chimpanzee comparisons, based on a set of 96 orthologs derived from HOVERGEN release 44 , is thatwww.ensembl.org), then used BLAST (−10 (using the higher score as a worst-case bound) or better. We chose this method of ortholog determination to get a one-to-one relationship between proteins. We found 14,790 ortholog pairs, a coverage rate of approximately 50% in mouse and 40% in human. The observed E-values between orthologs have a median value of 0.0 (<1e − 180). The aligned peptide orthologs were then used in conjunction with ENSEMBL cDNA data to determine aligned orthologous cDNA. For the chimpanzee–human comparison, human genes from ENSEMBL were compared to chimpanzee genes from HOVERGEN. A mutual best-hit criterion was used to determine the set of 96 orthologs.We downloaded a list of the available 37,347 human and 27,504 mouse peptides from the ENSEMBL sequence database  = N) to get one universal rate distribution, which in the limit of many datapoints should approach the Normal distribution (2π)−½ exp (x2/2). We defined the normalized substitution rate to beUnder this model, at each N the distribution of substitution rates can be described by a binomial distribution with a standard deviation of σ, consistent strand orientation (a block is either in the same strand orientation in both species or completely reversed), and consistent chromosome (no chromosome changes in either species in a block), yielding a dataset of 11,087 neighbor pairs. Under this condition, the Pearson correlation was 0.26, corresponding to a highly significant p-value of 10−189.We also tested whether neighboring genes have similar substitution rates. The orthologs were ordered by their location along the human genome, after which we calculated the Pearson correlation of a gene's substitution rate z-score) based on the substitution rates of all members of that category. Of the genes in our ortholog set, 9,966 had GO classifications available. The z-score was defined to beFor each GO category, we calculated a normalized substitution rate  was used to avoid the problem of double counting of genes when several are close together. To test the statistical significance of the number of clustered genes in a category, we used bootstrapping. For each GO category, we performed 5,000 random trials of selecting GON random genes from the entire set of orthologs, where GON is the number of genes in the GO category. In each trial, we counted the number of clustered genes in this randomly selected group. The average number of clustered genes was used to approximate the random number of clustered genes by a Poisson distribution. These Poisson statistics were then used to calculate the significance of the number of clustered genes for the GO category. A Poisson distribution is appropriate as long as clustering of neighbors is a rare event, i.e., as long as GON<<allgenesN, which was generally the case.To measure clustering, for each gene in a GO category we tested whether it had another category member downstream of it within the short-range correlation length of τ = 1010clusterp > 3 is the cutoff for significance.The random distributions were visually inspected and found to agree with the shape of the Poisson curve. To generate the data for p in these repeat sequences was 0.33, which was very close to the value for 4-fold sites, 0.34. The start position of each repeat sequence was used to define its location in the genome. In order to determine the locations of repeat sequences (based on the June 2002 UCSC genome map) along the physical map used for the gene sequences (based on the ENSEMBL May 2003 map), gene locations according to the two maps were compared. Repeat sequence locations were then corrected using the location differences of nearby genes. For clarity, the ancestral repeat values shown in Aligned repeat sequences between mouse and human were obtained from the dataset of r(0)r(t) for t values even as large as several megabases. We binned these data into 100 uniformly spaced groups covering t ∈ and then averaged over each of these bins to determine the correlation function <r(0)r(t)>. The data were plentiful enough for the averaged values shown in t since the amount of data decreases with t, a fact manifested in the increasing fluctuations at larger t in r(0)r(t)> at t = 15 Mb in the human data of t = 75,000 it was based on 22,860 measurements. For genes with alternative splicings, only one of the genes was used, in order to avoid spurious effects caused by reuse of DNA. Orthologous block boundaries were defined by genes at which the chromosome changes in either species. Monotonicity and consistent strand orientation were ignored in order to obtain blocks with large values of t. Most of the r(0)r(t) data comes from blocks at least several megabases long. Approximately 5% is in blocks of size less than 106 basepairs, 55% is in blocks of size between 106 and 107 basepairs, and the remaining 40% is in larger blocks.We considered all pairs of genes on continuous orthologous blocks, starting from the first neighbor up to the 35th gene downstream. This allowed us to get hundreds of measurements of r(i)r(j)> should be of the order ∼O.Click here for additional data file.Dataset S2Gene categories with significant negative selection on amino acid changing point mutations.(21 KB XLS).Click here for additional data file.http://www.geneontology.org/) ID numbers for the categories discussed in this paper are as follows: brain development (GO:0007420), calcium-dependent cell adhesion molecule activity (GO:0008014), calcium-dependent protein serine/threonine phosphatase activity (GO:0004723), calcium ion binding activity (GO:0005509), carbohydrate metabolism (GO:0005975), cell adhesion (GO:0007155), cell growth and/or maintenance (GO:0008151), chymotrypsin activity (GO:0004263), CTD phosphatase activity (GO:0008420), cytoplasm (GO:0005737), development (GO:0007275), DNA binding activity (GO:0003677), dynein ATPase activity (GO:0008567), dynein complex (GO:0030286), enzyme activity (GO:0003824), G-protein coupled receptor protein signaling pathway (GO:0007186), GTP binding activity (GO:0005525), heterogeneous nuclear ribonucleoprotein (GO:0008436), homophilic cell adhesion (GO:0007156), immune response (GO:0006955), integral to membrane (GO:0016021), internalization receptor activity (GO:0015029), intracellular protein transport (GO:0006886), magnesium-dependent protein serine/threonine phosphatase activity (GO:0004724), membrane (GO:0016020), metabolism (GO:0008152), microtubule-based movement (GO:0007018), microtubule motor activity (GO:0003777), mRNA binding activity (GO:0003729), myosin phosphatase activity (GO:0017018), neurogenesis (GO:0007399), nucleus (GO:0005634), olfactory receptor activity (GO:0004984), oncogenesis (GO:0007048), protein amino acid dephosphorylation (GO:0006470), protein phosphatase type 2A activity (GO:0000158), protein phosphatase type 2B activity (GO:0030357), protein phosphatase type 2C activity (GO:0015071), protein serine/threonine kinase activity (GO:0004674), protein transporter activity (GO:0008565), proteolysis and peptidolysis (GO:0006508), receptor activity (GO:0004872), regulation of transcription, DNA-dependent (GO:0006355), regulation of transcription from Pol II promoter (GO:0006357), regulation of translational initiation (GO:0006446), ribonucleoprotein complex (GO:0030529), RNA binding activity (GO:0003723), RNA polymerase II transcription factor activity (GO:0003702), RNA splicing (GO:0008380), transcription coactivator activity (GO:0003713), transcription factor activity (GO:0003700), transcriptional activator activity (GO:0016563), translation regulator activity (GO:0045182), trypsin activity (GO:0004295), ubiquitin conjugating enzyme activity (GO:0004840), ubiquitin cycle (GO:0006512), and ubiquitin-dependent protein catabolism (GO:0006511).The Gene Ontology (


The Scar/WAVE family of proteins mediates signals to actin assembly by direct activation of the Arp2/3 complex. These proteins have been characterised as major regulators of lamellipodia formation downstream of Rac activation and as members of large protein complexes.We have investigated the interactions of the three human Scar/WAVE isoforms with several previously described binding partners for Scar/WAVE 1 or 2. We find that all three Scar/WAVE isoforms behave similarly and are likely to participate in the same kinds of protein complexes that regulate actin assembly.Differences between Scar/WAVE proteins are therefore likely to be at the level of tissue distribution or subtle differences in the affinity for specific binding partners. Homolrotein]) -11. N-WAin (SHD) .in vitro, but have been postulated to be regulated by the small GTPase Rac in vivo )Scar/WAVE3 is the most tissue-specific of the three mammalian Scar/WAVE isoforms, being found in haematopoietic cells and brain tissue, but not yet being characterised in cells ,56. It hProtein sequence conservation between the three Scar/WAVE proteins in the N-terminal Scar Homology Domain (SHD) indicates a potential for association of binding partners with all three members of the family. Scar/WAVE1 and 2 have been shown to associate with the key adapter protein Abi1 through the SHD, so it is possible that Abi1 would also associate with Scar/WAVE3 ,30. To tOur data and previously published studies indicate that Abi1 exhibits the potential to bind to all three mammalian Scar/WAVE isoforms. To test whether the association of Scar/WAVE1, 2, and 3 with Abi1 is mediated by the Scar Homology Domain, Myc-Scar/WAVE SHD of each of the three family members were transiently expressed in Cos cells with HA-Abi1 or HA-HSPC300 and immunoprecipitated using a 9E10 anti-myc monoclonal antibody. HA-Abi1 precipitated with each of the three SHDs, but not in the absence of antibody or Myc-SHD Fig. . HA-HSPCConservation of Scar Homology Domain binding partners raises the question of whether other binding partners of Scar/WAVE are also capable of interacting with the entire Scar/WAVE family. IRSp53 has been implicated in regulation of actin dynamics through binding to Scar/WAVE proteins ,50,54. GAs an activator of the Arp2/3 complex, Scar/WAVE3 would be expected to localize to the same areas of the cell as polymerized actin, subunits of the Arp2/3 complex, and members of the Scar/WAVE complex. C2C12 cells were used for immunocytochemistry with anti-Scar3 polyclonal antibody Fig. , phalloiIf the SHD of Scar/WAVE protein mediates the interaction with a regulatory complex it can be expected that the SHD will co-localise with binding partners in cells. Ectopic expression of full length Scar/WAVE isoforms in cells reveals a peri-nuclear and cytoplasmic pool, causing an abundance of polymerised actin in the cytoplasm, but with enrichment of Scar/WAVE protein in lamellipodia ,58. A siWhen expressed with Myc-Scar 1 SHD Fig. , HA-HSPCHA-Abi1 Fig.  stronglyWe show that Abi1 and HSPC300 interact with three human Scar/WAVE isoforms, via the Scar Homology Domain. Conservation of Abi1 binding between all three Scar/WAVEs is indicative of the association of the "Scar/WAVE complex" with all three isoforms, as Abi1 seems to be the link between Scar/WAVE and NckAP1 and SRA-1/PIR121 in previous reports . AssociaLittle is known about the role of Scar/WAVE3 in cells. Expression of the mammalian Scar/WAVE3 gene is largely limited to the brain and lung with relatively little expression in other adult tissues ,32,56. Tbrk1 gene product, the Maize homologue of HSPC300, causes aberrations in cellular morphology and filamentous actin distribution of leaf epidermal cells [The role of interaction of HSPC300 with the Scar/WAVE3 complex is unknown. Loss of function of the al cells ,25. Thisal cells .Association of all three Scar/WAVE isoforms with an Abi-NckAP1-SRA1 complex may show that there is some functional redundancy between Scar/WAVE family members but data from mice deficient in a Scar/WAVE isoform indicates that Scar/WAVE1 and 2 do not have completely overlapping functions ,38,57,61We conclude that Scar/WAVE3 is likely to participate in similar signalling complexes to Scar/WAVE1 and 2 and that the differences between these Scar/WAVE proteins is likely to be at the level of tissue expression, differences in affinity for certain binding partners and possibly interaction with yet undiscovered binding partners.All chemicals were purchased from Sigma-Aldrich, UK unless otherwise specified. Antibodies were from the following sources: 9E10 anti-Myc monoclonal (Cancer Research UK), 12CA5 anti-HA monoclonal (Cancer Reaearch UK), anti-Myc polyclonal 1:500 anti-HA polyclonal (Santa Cruz), 1:500 anti-Scar/WAVE3 (Upstate), 1:100 anti-Abi1, or 1:100 anti-Scar/WAVE1 , anti-AbHSPC300 and Abi1 expression vectors were created using the Gateway Cloning system (Invitrogen). Open reading frames for HSPC300, and Abi1, derived from I.M.A.G.E. clones 4519512 (HSPC300) and 4158413 (Abi1) , were amplified by PCR and ligated into the pENTR-Topo entry vector (Invitrogen). N-terminally HA- and Myc- tagged HSPC300, and Abi1, were created in pRK5 DEST-Myc or pRK5 DEST-HA Gateway destination vectors by recombination from entry vectors. pRK5 DEST-Myc or pRK5 DEST-HA were created by ligation of the Gateway Vector Conversion System (Invitrogen) reading frame A cassette into pRK5-myc or pRK5-HA cut with SmaI. Scar/WAVE1, Scar/WAVE2, and Scar/WAVE3 full length and deletion constructs were created by PCR and cloned into pRK5-myc or pGEX4T-2. Scar/WAVE-1 constructs were derived from KIAA00429; Scar/WACos 7 and C2C12 cells were grown in DMEM plus 10% Foetal Calf Serum and antibiotics. Transfections were performed using Genejuice transfection reagent (Novagen) following the manufacturers instructions. Cell lysis was performed from confluent cells in a 1% Triton X-100 lysis buffer . Lysates were cleared by centrifugation and equalised to 1 mg/ml total protein concentration.Mouse brains were homogenised on ice in 2 μl of 1% Triton X-100 lysis buffer per mg of tissue using a fine gauge needle. Homogenates were centrifuged once for 10 minutes at 13,000 rpm in a microcentrifuge, then for 30 minutes at 100,000 g in a Beckmann TLA-100.2 rotor. Supernatants were equalised to 1 mg/ml total protein concentration with 1% Triton X-100 lysis buffer.Lysates or brain extracts  were incubated with 30 μl bed volume of pre-washed protein G beads (Cancer Research UK) for 1 hour, designated beads minus antibody controls. Beads were precipitated and supernatants removed. 5 μg of antibody monoclonal 9E10 anti-myc, 12CA5 anti-HA, Rabbit anti-Scar/WAVE3, or Mouse anti-Abi1 or an irrelevant control antibody (rabbit or mouse anti-myc) was added to the supernatant and incubated for 1 hour, before the addition of 30 μl bed volume of pre-washed protein G beads, designated beads plus antibody, for 1 hour. Beads were precipitated, and the supernatant removed. Beads were washed 3 times in 30-fold bed volume of lysis buffer. Beads were resuspended in an equal volume of 2× SDS-PAGE loading buffer . 10 μl oE.coli induced for 3 hours with 0.2 mM IPTG, or 15 hours at 25°C for GST-IRSp53. Cells were spun down and sonicated in 1 ml of 1% Triton PBS  per 100 ml bacterial culture. GST fusion proteins were batch purified on 100 μl Glutathione-s-agarose (Sigma) per 100 ml culture and washed 5 times with 10-fold bed volume of sonication buffer. Beads were resuspended in an equal volume of 1% Triton lysis buffer. SDS-PAGE analysis and coomasie staining were used to qualify GST fusion proteins. 500 μg total protein of transfected Cos7 cell lysate was incubated with GST-fusion protein or GST alone for 1 hour. Beads were precipitated, lysates removed and the beads were washed 3 times in 1% Triton lysis buffer. Beads were resuspended in and equal volume of 2× SDS-PAGE loading buffer. Samples were analysed by SDS-PAGE and western blotting.GST-fusion proteins were expressed in BL21 Proteins were separated by SDS-PAGE and transferred onto nitrocellulose membrane by western blotting. Blots were saturated with 5% dried milk in PBS 0.2% Tween-20, probed with 1:500 9E10 anti-Myc monoclonal, 1:500 12CA5 anti-HA monoclonal, 1:500 anti-Myc polyclonal 1:500 anti-HA polyclonal, 1:500 anti-Scar/WAVE3, 1:100 anti-Abi1, or 1:100 anti-Scar/WAVE1 primary antibody in 2.5% BSA 0.2% Tween-20 PBS followed by 1:10'000 dilution of HRP-conjugated secondary antibody diluted in 0.2% Tween-20 PBS. Bound secondary antibody was visualised using SuperSignal (Pierce) according to the manufacturers instructions.Cells cultured on glass coverslips were fixed in 4% paraformaldehyde and permeabilized with 0.1% Triton PBS. Cells were stained with 1:200 Rb anti-HA polyclonal, 1:200 12CA5 anti-HA monoclonal, 1:200 9E10 anti-myc monoclonal, 1:100 anti-Scar\WAVE3, 1:200 monoclonal anti-Arp3, or 1:25 anti-Abi1 primary antibodies diluted in 1% BSA PBS. Secondary antibodies used were Goat anti-mouse or rabbit, FITC, TRITC, Alexa-488 or Alexa-546 Conjugates at 1:200 dilution in 1% BSA PBS. Filamentous actin was visualised using Alexa-546 conjugated phalloidin (Molecular Probes) diluted 1:500 in 1% BSA PBS. Slides were viewed using a BioRad MRC100 confocal laser scanning microscope. For endogenous Scar/WAVE3 labelling, it was important to test which cell lines expressed Scar/WAVE3. Among the following cell lines: NIH 3T3, N1E 115 neuroblastoma, N19 neuroblastoma, PC6 neuronal precursor, J774.A1 bone marrow macrophage, RAW 264.7 alveolar macrophage and C2C12 myoblast, we found that only C2C12 expressed endogenous Scar/WAVE3 by western blotting of whole cell lysates . We thus used C2C12 for localisation of endogenous Scar/WAVE3 protein.GST glutathione-S-transferase, WH2 WASP-homology 2, A acidic sequence motif, SHD Scar homology domain, NckAP1 Nck-associated protein 1, HSPC300 haematopoeitc stem/progenitor cell protein, Abi 2 abelson tyrosine kinase interactor 2C.F.S. performed most of the experiments and contributed intellectually to the design and interpretations. C.F.S. also drafted the manuscript. T.H.M. performed the experiments shown in Figure A) Schematic representation of the domain architecture of Scar/WAVE family proteins and the deletion constructs used in these experiments.  Numbers indicate start or end point of Scar/WAVE1 deletion mutants in amino acid residue position.  Start/End points for Scar/WAVE2 deletion mutants are FL = 498, delta A = 456, SPW = 455, SP = 435, SB = 245, SHD = 170, BPWA = 171, PWA = 246.  Start/End points for Scar/WAVE3 deletion mutants are FL = 499, delta A = 488, SPW = 457, SP = 439, SB = 238, SHD = 171, BPWA = 172, PWA = 239.  FL; Full length.  SHD; Scar Homology Domain.  B; Basic Rich Region.  PRR; Proline rich region.  W; Wiskott Homology 2 domain.  C; Central or connecting region.  A; Acidic Rich Region.  (B) Co-immunoprecipitation of HA-Abi1 with Myc-Scar/WAVE deletion constructs.  (C) Co-immunoprecipitation of HA-Abi1 or HA-HSPC300 respectively with Myc-Scar/WAVE deletion constructs.  Protein G beads were used to immunoprecipitate protein from transiently transfected Cos 7 fibroblasts before (-Ab) and after addition (+Ab) of an anti-Myc (9E10) monoclonal antibody.  Bead bound fractions and supernatants (Sup) were analysed by SDS-PAGE followed by immunoblotting.  Blots were probed with an anti-HA polyclonal antibody for the presence of HA-Abi1 or HA-HSPC300 in immunoprecipitated complexes.Characterization of HSPC300 and Abi1 binding domain of Scar/WAVE1.  (Click here for file


The analysis of gene expression using DNA microarrays provides genome wide profiles of the genes controlled by the presence or absence of a specific transcription factor. However, the question arises of whether a change in the level of transcription of a specific gene is caused by the transcription factor acting directly at the promoter of the gene or through regulation of other transcription factors working at the promoter.a1-α2 complex, which represses haploid-specific genes in the yeast Saccharomyces cerevisiae. Our analysis identified many known or suspected haploid-specific genes that are direct targets of the a1-α2 complex, as well as a number of previously uncharacterized targets. We were also able to identify a number of haploid-specific genes which do not appear to be direct targets of the a1-α2 complex, as well as a1-α2 target sites that do not repress transcription of nearby genes. Our method has a much lower false positive rate when compared to some of the conventional bioinformatic approaches.To address this problem we have devised a computational method that combines microarray expression and site preference data. We have tested this approach by identifying functional targets of the These findings show advantages of combining these two forms of data to investigate the mechanism of co-regulation of specific sets of genes. A bioinformatic approach to identifying cis-regulatory elements controlling transcription has become feasible with the availability of complete genome sequences and large scale expression data using high-throughput methods such as microarrays ,2 and SASaccharomyces cerevisiae 2 - A(Xa(g) - Xα(g))2.Score(g for the three cell types for the variables Xt(g) with t indicating the type. We have since found that using the complete expression data for the polyploids is a better strategy. In the final results, shown in the paper, Xa(g) is the average of the log expression for a, aa and aaa. Likewise, Xα (g) is the average from α, αα and ααα. Xa/α(g) comes from averaging log expression over aaα, aαα and aaαα. The polyploid averaged quantities tend to be less noisy , namely, fraction of genes with score higher than g.An explanation of the purpose served by different terms in the overall score is described below. The first term scores well when expression in diploids is lower than the average expression in haploids. The second term penalizes the gene if the expressions in different types of haploids are very different. a1-α2 repressor complex. In order to select for genes with an upstream region with a strong a1-α2 repressor, we used the binding site mutational data available  ≈ eβE(S)/z assuming near saturation of binding. The symbol z represents the fugacity and β is inverse of kBT, kB being the boltzman constant. The binding (free) energy is given by E(S) = ΣibεibSib, within the single base model )•exp(ΔG(S)/RT) in the biochemistry literature -ATP, and then annealed with excess cold complementary oligonucleotide. The HO(10) and HO(8) a1-α2 sites within Upstream Regulatory Sequence 1 (URS1) of the HO promoter were used as strong and weak binding sites respectively. The EMSA was performed as described previously [μM a1 and five-fold titrations of α2 starting at 82 nM in protein dilution buffer .Oligonucleotides containing the predicted eviously , using aa1-α2 binding sites were synthesized with 5' overhangs to allow cloning into the XhoI site of pTBA23 (2μ URA3 Ampr), a reporter plasmid containing a CYC1-lacZ fusion [β-galactosidase activity was measured on three independent transformants, as described previously [Oligonucleotides containing Z fusion . Reporteeviously .SAGE – Serial Analysis of Gene ExpressionHD – HomeodomainORF – Open Reading FrameChIP – Chromatin immunoprecipitationNHEJ – Non-Homologous End JoiningTC – Total ChromatinIP – Immunoprecipitated sampleEMSA – Electrophoretic Mobility Shift AssayPCR – Polymerase Chain ReactionPMSF – Phenyl Methyl Sulfonyl FluorideEDTA – Ethylenediaminetetraacetic AcidVHN carried out the ChIP experiments, and wrote parts of the first draft of the manuscript. RAO did the bioinformatics analysis, and wrote programs facilitating primer design. ARB did EMSA and beta-gal assays. JRM constructed the strains and contributed to the design of the ChIP experiment. AKV and AMS supervised and coordinated the computational and the experimental research as well as prepared the manuscript. All authors contributed to the manuscript and approved the final version.


In order to confirm a previous finding of linkage to alcoholism on chromosome 1 we have carried out a genetic linkage study.DNA from eighteen families, densely affected by alcoholism, was used to genotype a set of polymorphic microsatellite markers at loci approximately 10 centimorgans apart spanning the short arm and part of the long arm of chromosome 1. Linkage analyses were performed using the classical lod score and a model-free method. Three different definitions of affection status were defined, these were 1. Heavy Drinking (HD) where affected subjects drank more than the Royal College of Psychiatrists recommended weekly amount. 2. The Research Diagnostic Criteria for alcoholism (RDCA) 3. Alcohol Dependence Syndrome (ADS) as defined by Edwards and Gross (1976) and now incorporated into ICD10 and DSMIV.Linkage analyses with the markers D1S1588, D1S2134, D1S1675 covering the cytogenetic region 1p22.1-11.2 all gave positive two point and multipoint lods with a maximum lod of 1.8 at D1S1588 (1p22.1) for the RDCA definition of alcoholism. Another lod of 1.8 was found with D1S1653 in the region 1q21.3-24.2 using the HD affection model.These results both support the presence of linkage in the 1p22.1-11.2 region which was previously implicated by the USA Collaborative Study of the Genetics of Alcoholism (COGA) study and also suggest the presence of another susceptibility locus at 1q21.3-24.2. Epidemiological studies report that alcoholism as defined by the Research Diagnostic Criteria (RDC) affects almost 10–15% of the general population in the USA and 1–5% in Europe . The oveSelection criteria for the families eliminated all but 9% of the families contacted from all sources. Press contacts were found to be the most productive source of suitable families willing to participate in the study, accounting for over 50% of subjects recruited. There appeared to be no bias in the affection status of subjects recruited from the different sources . The affAssuming dominant transmission, two-point heterogeneity LODS (HLOD) for linkage to the HD affections status gave a score of 1.8 near the marker D1S1653. The three-point multipoint analysis produced an HLOD of 1.8 with the markers D1S1653 and D1S1677. Analyses with the same markers assuming recessive transmission produced an HLOD of 1.1 with a two-point analysis and an HLOD of 1.5 with a three-point analysis. The model-free (MFLINK) analysis produced an MLOD of 1.5 for both the two-point and three-point analyses. Three-point analyses of D1S1677 and D1S1679 produced an HLOD of 1.7 assuming recessive transmission and a MALOD of 1.3 for the model-free three point analysis. The marker D1S1675, on the short arm of chromosome 1, produced an HLOD of 1.2 with both a two-point analysis and a three-point analysis in combination with D1S3723 assuming dominant transmission, Model-free analyses of D1S1588 produced a MALOD of 1.5 and 1.2 using two-point and three-point analyses, respectively. Figure Assuming recessive transmission the marker D1S1679 was linked to RDC alcoholism with a two-point HLOD of 1.0 and a three-point HLOD of 1.6 with the markers D1S1677 and D1S1679. The MLOD two point MFLINK score with D1S1677 was 0.9 and this increased to 1.4 with a three-point model-free analysis.For the RDCA level of affection status the highest MALOD score of 1.8 was found near the telomere of chromosome 1p near the marker D1S1588 under the model free analysis. Figure The marker D1S1591 gave the highest maximum admixture lod (MALOD) of 1.00 with the model-free method of analysis for the ADS category. The results of MFLINK multipoint analysis for this and the other affection status models are shown in table The COGA study first reported evidence of a locus linked to alcoholism on chromosome 1p with the markers D1S532 and D1S1588 linked to Alcohol Dependence . Curtis Dick et al  who analIn the UK linkage study the two regions on chromosome 1 are implicated with two different affection status categories. We could speculate that the two different loci on chromosome 1 could be mediating an effect on alcoholism through two loci that have different types of effect on susceptibility. The most interesting and probably important finding from the COGA linkage study is that the stratification of families that have members with both alcoholism and depression maximizes the lod to 5.12 on chromosome 1p near the markers D1S1648 and D1S1588 . WithoutPrior to commencing the study, ethical permission for this research project was obtained from the University College London Medical School Clinical Investigations Panel which has been updated in 2003 with multicentre research ethics committee (MREC) approval. Information stored on computer was registered under the 1984 Data Protection Act.Caucasian families multiply affected by alcoholism, and suitable for linkage studies, were ascertained with the following selection criteria.(a) Presence of two or preferably more cases of alcoholism as defined by the RDC.(b) Large families, preferably with two or more generations willing and able to participate.(c) Evidence of unilineal inheritance of alcoholism in the parental generation, which meant that families were excluded where both parents were affected.(d) Willingness of as many family members as possible, especially affected individuals, to participate in the research project. The families were identified from hospital records, clinicians, advertisements in the media and other contacts . The famFifty nanograms of total genomic DNA was extracted from venous blood samples and amplified by Polymerase Chain Reaction (PCR) with oligonucleotide primers using standard methodology . Short T. Positions of the markers are shown in table Ten markers on 1p21-35 were genotyped: D1S1591, D1S2134, D1S1665, D1S1728, D1S532, D1S1588, D1S551, D1S1631, D1S3723 and D1S1675. Four markers on 1q were genotyped: D1S1595, D1S1653, D1S1677 and D1S1679. Marker order and intermarker distances were as compiled in the Ensembl database Genotypes were read blind to diagnostic information. Tests for Mendelian inheritance of marker data were performed and inconsistent genotypes were repeated or omitted.The linkage analysis was performed using the classical lod score method and using likelihood-based model-free analysis carried out with the MFLINK program . For lodIG carried out the genotyping and produced the manuscriptCCHC collected the multiplex family sample and participated in the design of the studyWK participated in genotypingAD participated in genotypingAM participated in genotypingDC performed the linkage analysisHMDG conceived the study and participated in its design and coordinationAll authors read and approved the final manuscript