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 (see r = –0.81, p < 0.01) but two other significant foci were found in right dorsal extrastriate cortex , and striate cortex (BA17, r = –0.68, p < 0.05). 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 (−90°, +90°, −90°, and so on), 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 (Forchheim, Germany) 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 [any consistent relationship obtains between standard metabolic rate and body mass. Moreover, those who acknowledge such a relationship hold divergent opinions about its range of application. Is it valid only for limited numbers of taxa, or is it universal? Since the 1960s there has been a measure of consensus: a consistent allometric scaling relationship does exist, at least among homoiotherms. Nevertheless, not all biologists agree, and scepticism is widespread, particularly about the alleged universality of Kleiber's law.The first issue is disagreement as to whether b, where B = standard metabolic rate, M = body mass, and a and b are constants, what is the value of b? Kleiber [Second, assuming that some version of Kleiber's law  applies to at least some taxa, there are disagreements about the gradient of the semi-log plot. That is, if B = aM Kleiber  and manyb, how is Kleiber's law to be interpreted mechanistically? What is its physical or biological basis? For those who claim that b = 0.67, this issue is simple: standard metabolic rate depends on the organism's surface to volume ratio. But for proponents of the majority view, that b = 0.75, the issue is not simple at all. Many interpretations have been proposed, and since several of these are of recent coinage and seem to be mutually incompatible, a critical comparative review seems timely.Third, assuming a consistent scaling relationship and an agreed value of b (B = standard metabolic rate, M = body mass, a and b are constants and b is taken to be approximately 0.75), was inferred by other investigators during the 1930s [Kleiber's initial paper  found suhe 1930s ,3. Relevhe 1930s -15) and b, where V is the variable in question, k is a constant and b = n/4; n = 3 for metabolic rate). Examples include lifespans, growth rates, densities of trees in forests, and numbers of species in ecosystems . Some cb assume some version of Kleiber's law: i.e. that a single allometric scaling relationship fits metabolic rates over a wide range of organisms. However, as noted in the introduction, there are dissenters. Everyone acknowledges considerable variation both within and among taxa, no matter whether b = 0.75, 0.67, or some other number. The question is whether these variations are deviations from a general law, or whether there is no such law. Conflicting opinions on this fundamental point recall the traditional philosophical difference between physicists and biologists: the former are inclined to see abstract mathematical generalities in any set of numerical data, the latter to see concrete particulars. All recent attempts to explain Kleiber's law by "universal" models have involved physicists and mathematicians; the sceptics are predominantly biologists.Most debates about the value of et al. [b = 0.75) was not statistically supported. Feldman [b ranges from around 0.67 to more than 0.9 over all major animal groups, the values perhaps reflecting complexity of organisation; single values such as 0.75 emerge only as averages over each group. Other investigators have been less sceptical; publications by Enquist and Niklas [Dodds et al.  re-exami Feldman  found no Feldman  found evd Niklas ,20 give a values than placental mammals and marsupials have lower ones, but the 0.75-power relationship is still inferred by many investigators . McNab rs  Kleiber's law is widely  applicable in biology, (ii) b values ranged from about 0.3 to 1.2 in these taxa (his Table 1 provides an excellent summary). Assuming that the delivery of nutrients to each organism entails diffusion through a boundary layer, Patterson showed how water movements and organism size might affect such delivery and hence determine metabolic rate. Using simple geometrical models of organisms (plates, cylinders and spheres), he derived b values ranging from 0.31 to 1.25, more or less consistent with the experimental values.Some of the doubts about the consensus are powerfully supported by studies on small aquatic organisms. Reviewing a large literature on metabolic rates in aquatic invertebrates and algae, Patterson  deployedviz. Sherwood number, Sh = hmW/D, where hm = mass transfer coefficient, W = characteristic dimension of organism and D = diffusivity; and Reynolds number (a function of organism size), Re = ρUW/μ, where ρ = density, U = water flow speed and μ = coefficient of viscosity. The graphs, which had the form Sh = c.Red, where d = 0.5 for ideal laminar flow and 0.8 for turbulent flow , revealed the relative importance of diffusion and mass transfer (convective movement) in the supply of materials. Patterson was able to derive an expression for hm, and was thus able to relate the supply of materials to body mass.Patterson plotted two dimensionless numbers against each other, ad hoc biological or other assumptions. Patterson's approach has implicit support in the literature: Coulson [The two main attractions of this model are (1) good agreement with a wide range of data and (2) derivation from basic physical principles without  Coulson  used cheThe Patterson model has not been given much attention by other investigators in the field and perhaps it deserves more consideration. Despite its inherent limitations  it is a potentially fruitful contribution to biophysics.b = 0.75, which a consensus of physiologists has accepted since the 1960s. Many of these sceptics claim that the "true" value of b is 0.66 or 0.67 because the principal determinant of metabolic scaling is the surface-to-volume ratio of the organism; hence, assuming constant body density, the surface-to-mass ratio. The first study to suggest this explanation for the mass dependence of B is attributed to Rubner [b is approximately 0.67 for any single mammalian species and suggested that the interspecies value of 0.75 is a statistical artefact. Feldman and McMahon [a, termed the "specific mass coefficients"). Hayssen and Lacy [b = 0.65 for small mammals and b = 0.86 for large ones, again suggesting that b = 0.75 is a cross-species "average" with no biological significance; but it is questionable whether their data were measurements of standard metabolic rate in all cases. McNab [Several workers accept the reality of allometric scaling but question the value o Rubner , who stuo Rubner  reported McMahon  disagree McMahon , he arguand Lacy  found b s. McNab  reporteds. McNab  reasoned0.67 is a mass-independent measure of standard metabolism. Variations indicate the effects of factors other than body mass. Other workers broadly share Heusner's opinion (see e.g. [0.67, the interesting problem is not the index (b) in the Kleiber equation but the allegedly constant relationship between specific mass coefficient (a) and body size. This point was developed by Wieser [ontogeny of metabolism, which comprises several phases but follows the surface rule (M0.67) overall, from the phylogeny of metabolism, which concerns the mass coefficients (a). Following Heusner's argument, Wieser [nM0.66 and deduced that the specific mass coefficient an = aM0.09. Here, a is an interspecific mass coefficient . Another difficulty with this type of explanation lies in the calculation of body surface area; the Meeh coefficient, k, where surface area = kM0.67, is difficult to measure unequivocally but is generally taken as ~10  could be substantiated by his approach.According to Heusner, the ratio B/Msee e.g.  for revisee e.g.  for a gosee e.g.  found a see e.g.  reportedy Wieser , who dis, Wieser  wrote th [et al. , who queb = 0.67 remains a minority view. Those who accept it are faced with the twin difficulties of (i) establishing that their estimates of surface area are correct and (ii) explaining why, in Wieser's notation, an = aM0.09. Moreover, even if such arguments as Heusner's are valid for homoiotherms, it is hard to justify their extrapolation to poikilothermic animals, plants and unicellular organisms, all of which are held by consensus to fit Kleiber's law (but see the two preceding sections). Why should temperature fluxes across the body surface be the main determinants of metabolic rate in poikilotherms, particularly microorganisms? Even in mammals, maintenance of body temperature might not be the main contributor to energy turnover at rest (see later). Contrary to the view of Dodds et al. [b = 0.67 cannot be treated as a "null hypothesis".The claim that s et al. , therefoThroughout the remainder of this article, the consensus position will be assumed: Kleiber's law is valid for a wide range of organisms, and b = 0.75. This assumption is made tacitly and provisionally and does not imply dismissal of the foregoing sceptical arguments; but a field can only be reviewed coherently from the consensus point of view.cr, = k(E/ρ)1/3d2/3, where d = column diameter, E = Young's modulus and ρ = density. If E and ρ are constant then lcr3 = cd2, where c is a constant of proportionality. McMahon [all bone proportions change in the same way with animal size. The mass of a limb, wl, = αld2, where α is a constant. If wl is proportional to M, as it generally must be, then M = βld2, where β is another constant. Hence (given the above relationship between l and d) M is proportional to l4, implying that l is proportional to wl1/4; hence d is proportional to wl3/8, or M3/8. Empirical support for this relationship appeared in [A vertical column displaced by a sufficiently large lateral force buckles elastically. The critical length of column, l McMahon  applied eared in .2, W/t it is proportional to d2, and therefore to (M3/8)2 = M3/4. If this deduction applies to any skeletal muscle (as seems plausible), then it applies to the entire set of metabolic variables supplying the muscular system with nutrients and oxygen. Hence, B varies as M3/4. A broadly comparable but simpler argument was advanced by Nevill [2/3. Nevill's paper is seldom cited.McMahon  also appy Nevill ; large met al. [0.88, not M0.75, although there are disagreements about whether aerobic capacity determines the allometry of maximum metabolic rate [decreases rather than increases with increasing metabolic output, e.g. [One difficulty with McMahon's model is that little of the energy turnover under conditions of standard metabolic rate measurement entails muscle contraction. The model might still be valid if maximum metabolic rate followed the same allometric scaling law as B; this has been widely believed, and Taylor et al.  adduced et al. -48 indiclic rate . Weibel lic rate  presenteut, e.g. .) Anothe1.0. If maintenance metabolism were related to surface area  then a combination of the two effects, surface-to-mass ratio and work against gravity, might explain the observed M3/4 relationship. This model [all animals (and other taxa) in just the right proportions to generate a 0.75-power dependence on body mass. To take just one example, aquatic microbes are more affected by Brownian motion than by gravity, so why should they show the same balance between surface-to-mass ratio and gravitational effects as mice or elephants? Pace et al. [An increased gravitational field increases energy metabolism in animals ,53. Workis model  is diffie et al.  suggesteBefore more recent models purporting to explain Kleiber's law are discussed, some comments are needed on scaling of metabolism at the organ, tissue and cell levels. Belief that the Kleiber relationship can be explained in terms of the inherent properties of the cells dates from the 1930s  and pers-0.31, ventilation rate as M0.77 and oxygen consumption rate as M0.72 [1.0 so it is disproportionate in bigger animals [Standard metabolic rate (B) is usually measured as oxygen consumption rate, which correlates with nutrient utilization ,15 and ras M0.72 -60. All as M0.72 . The sig animals ,61-65.-0.25, cardiac output as M0.81 (60) and circulation time as M0.25. The energy cost of supplying the body with 1 ml of oxygen is similar for all mammals [Stahl  describe mammals .0.63; for some organs the exponent is closer to 1.0; the sum of oxygen consumption rates over all tissues gives – approximately – the expected 0.75 index [2/3), which would otherwise be valid. More recently, however, Wang et al. [3/4.Standard metabolic rate has two main components: service functions, e.g. the operation of heart and lungs; and cellular maintenance functions, e.g. protein and nucleic acid turnover (e.g. ). Krebs 75 index . The dif75 index  has wide75 index  suggesteg et al.  repeated-0.18 [-0.1 [cf. [-0.1) and different fatty acid compositions [0.82 [0.82.M-0.18 = M0.64, consistent with the experimental tissue-slice data . Combining liver mass with mitochondrial number per hepatocyte gives a similar value [0.75 [Cells of any one histological type are size-invariant among mammals but allometric scaling is reported at the cellular level; e.g. the metabolic rate of isolated hepatocytes scales as M-0.18 . Numbers18 [-0.1 ,76. The 0.1 [cf. ), or it 0.1 [cf.  or allom0.1 [cf. . Also, lositions . The disns [0.82 ,80. Combar value . Cytochrue [0.75 -85. The in vitro, then presumably mitochondrial densities, inner membrane areas and cytochrome levels somehow become "normalized". This is a readily testable prediction [see [Isolated mammalian cells reportedly attain the same mitochondrial numbers and activities after several generations in culture, irrespective of the tissue of origin or the organism's body mass -88. If aion [see ], 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 [Coulson's flow model  was mentinternum -93. Obviet al. [However, flow theories advanced to explain Kleiber's law have not followed this line of argument. Banavar et al. ,95 and Det al. ,96 have D, where L is the physical length of the organism and D is its dimensionality. It proceeds with a theorem: the sum of flows through all parts of the network, F, is proportional to the (dimensionless) length multiplied by the metabolic rate. A quantity measuring the total flow of metabolites per unit mass of organism is then defined: r1 = F/M. r1 (which has units of inverse time) measures the dependence of the network's geometry on body mass, so it indicates the energy cost of metabolite delivery. Another parameter, r2, measures the metabolite demand by the tissues: r2 = the dimensionless length of the "service volume" (the amount of tissue that consumes one unit of metabolite per unit time). It is then deduced that B is proportional to (Mr1/r2)D/(D+1). Provided that r1 and r2 change proportionately – i.e. supply always matches demand – then for a three-dimensional organism, Kleiber's law follows. According to Banavar et al. [The most detailed account of this argument  begins wr et al. , deviatiterminal nodes (capillaries), not all nodes (as the model implies), are metabolite exchange sites. Also, the model seems to predict that r1/r2 will decrease as B rises from standard to maximal; but the best data suggest the opposite trend , except perhaps in terms of rather implausible variations among r1/r2 ratios. On the other hand, the model is simple and flexible and it reflects recent developments in the physics of networks. If it could be applied to flow at the cellular level, it might accord with the requirements discussed at the beginning of this section; though it is difficult to see how this can be achieved.This model has been criticized  because cussion: -48). Ban1/4, where t is the transport time and k is a constant. If the fluid transport rate  is proportional to B/M, Kleiber's law follows. However, Rau's model appears to assume that because metabolic rate is energy per unit time, it can be equated with the product of fluid volume flow rate and pressure . This assumption, which appears to be based exclusively on dimensional analysis, is fallacious.Rau  also advr is V = πn/2rn/Γ(n/2 + 1), and that A = dV/dr = nπn/2rn-1/Γ(n/2 + 1). Here, Γ(n) is the gamma-function such that Γ(n + 1) = nn, Γ(2) = 1 and Γ(3/2) = π1/2/2. Suppose two objects have "volumes" V1 and V2 and "areas" A1 and A2. From the foregoing, A1/A2 = (V1/V2)(n-1)/n; so if n = 4, a 3/4-power relationship between "volumes" (hence, masses?) emerges from a familiar mathematical principle. Might Kleiber's law therefore follow from a four-dimensional description of organisms? Speakman [et al. [Blum  observedSpeakman  pointed  [et al. ,101,102 et al. [This model addresses the supply of materials (particularly oxygen) through space-filling fractal networks of branching tubes. It assumes that as a result of natural selection, organisms maximize their use of resources. The initial account  assumed et al.  reasoneda, is a function of the element lengths at each level of the hierarchy, but one of these, the terminal one (l0), is invariant. Writing Φ as a dimensionless function of the (dimensionless) ratio l1/l2 leads toA second, simpler account  developea (l0, l1, l2,...) = l12Φ(l0/l1, l2/l1...)Introducing a scaling factor, λ, leads toa (l0, l1, l2,...) = λ2l12 Φ(l0/λ l1, l2/l1...)2 because l0 is fixed. The dependence of Φ on λ is not known a priori, but it can be parameterized as Φ(l0/λ l1, l2/l1...) = λεΦ(l0/l1, l2/l1...), where ε is between 0 and 1. This power law reflects the fractal character of the network's hierarchical organization. Similar reasoning is applied to body volume, hence body mass, and the following expression for the exchange surface area is derived:-which is not proportional to λa = kMr, r = (2 + ε)/(3 + ε + ζ),a, then ε must tend to 1 and ζ to 0. This gives r = 0.75. If a limits the supply of oxygen and nutrients, and hence determines standard metabolic rate, then B is proportional to a and Kleiber's law follows.where k is a constant and ζ (0 < ζ < 1) is an arbitrary exponent of length, just as ε is an arbitrary exponent of area. If natural selection has acted to maximize the scaling of a, are predicted to be closer to 3 than 2; empirically, the microscopic convolutions of surfaces such as the mammalian intestinal mucosa are well known. The mass of the smallest possible mammal is deduced and shown to be close to the mass of the shrew. Other approaches to exchange networks, assuming minimum energy expenditure and scale-invariance, have led to similar models [et al. [The model has several attractions: it derives from well-established physical principles, invokes natural selection and is mathematically impeccable. It implies that cells and organelles transport materials internally along space-filling fractal networks rather than by "diffusion", which seems correct ,86,103. r models . The mod [et al.  showed t [et al.  could reexplicandum itself unconvincing. Finally, the model is said to explain the quarter-power scalings of a wide range of biological variables other than metabolic rate, including population densities of trees [This is an impressive range of successes. However, West and his co-workers make claims that are less compelling. The observation that cytochrome oxidase catalytic rates fit the same allometric curve as whole-organism metabolic rates is claimed as corroboration. However, cytochrome oxidase is not an organism, or a cell: it does not have a metabolic rate. It is also debatable whether mitochondria can be said to have "metabolic rates".  If West scussion -49 and fdemand is generally more significant, i.e. for the service and cellular maintenance functions mentioned previously. Therefore, it is not clear why the geometry and physics of the supply system should predict the allometric scaling of standard rather than maximal metabolic rate. (2) During maximal energy output by an organism, the supply of material is likely to be limiting. For example, in mammals, muscle contraction is responsible for most of the energy turnover at maximum output and it is generally believed that the rate is limited by oxygen supply (if anaerobic capacity is ignored). However, under standard metabolic rate conditions, energy analysis ; this reb = 0.75. However, there are often wide differences among empirical b values, as discussed earlier; these were addressed in, for example, [(3) The mathematical derivations given in  are ideaexample,  and 31]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 [(4) West ochachka  show areet al. model, despite its impressive economy, elegance, consistency and range, cannot be accepted unreservedly in its present form. The very generality, or "universality", of this model has made it suspect for some biologists [These difficulties show that the West ologists . The impologists .This group elaborated a multi-cause rather than a single-cause account of allometric scaling. Their "allometric cascade" model holds that each step in the physiological and biochemical pathways involved in ATP biosynthesis and utilization has its own scaling behaviour and makes its own contribution (defined by a control coefficient between 0 and 1) to the whole-organism metabolic rate. Thus, many linked steps rather than a single overarching principle account for Kleiber's law.b values that are far from 0.75 . It is ve [e.g. ].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 [Tissue-specific gene expression has traditionally been viewed as a predictor of tissue-specific function: for example, genes specifically expressed in the eye are likely to be involved in vision. But microarray analysis in model organisms such as yeast and in yeast -5. Consein yeast -10. As ain yeast . Applicain yeast . Subsequin yeast .C. elegans. Its general utility in mammals has not yet been established with respect to the proportion of either genes or functional categories to which it can be effectively applied. Nor has it been formally examined how the use of quantitative transcriptional co-expression for inference of gene function compares to the more traditional approach of inferring functions on the basis of tissue-specific transcription. The extent and precision of hypotheses regarding gene functions that can be drawn from expression analysis in mammals is an important and timely question, given the current absence of knowledge of the physiological functions of at least half of all mammalian genes. Given that distinct and coordinate expression of a group of functionally related genes implies an underlying pathway-specific transcriptional regulatory mechanism, identification of such instances would also represent a step towards delineating mammalian transcriptional networks.To date, this approach has only been extensively applied to relatively simple model organisms such as yeast and PWP1, which is widely expressed. PWP1 is co-expressed with many RNA-processing genes in mouse, and we show that its yeast homolog is required for rRNA biogenesis. The data and the associated analyses in this paper will be invaluable for directing experimental characterization of gene functions in mammals, as well as for dissecting the mammalian transcriptional regulatory hierarchy.Here, in order to demarcate the general utility of using gene expression patterns to infer mammalian gene functions, and to use this information to begin characterizing genes discovered by sequencing the mouse genome , we usedIn order to generate an extensive survey of mammalian gene expression, we analyzed mRNA abundance in 55 mouse tissues using custom-designed microarrays of 60-mer oligonucleotides  correspoAll hybridizations were performed in duplicate. Data processing and normalization are described in detail in the Materials and methods. The data were processed so that each measurement reflects the abundance of each transcript in each tissue relative to the median expression across all 55 tissues; although the microarray spot intensities were used to determine which genes were detected as expressed (see below), the figures herein show the normalized, arcsinh-transformed and median-subtracted data, which for convenience we refer to as ratios. All of the data, together with tables detailing correspondence to genes in other cDNA and EST databases, annotations and other features of the encoded proteins, probe sequences, and other files used in our analyses below, are available as Additional data files and without restriction on our website .Four lines of evidence support the quality of our data and its consistency with existing knowledge of mammalian physiology and gene expression. First, we detected the expected patterns of expression for genes previously shown to be expressed specifically in each of the 55 tissues surveyed Figure . This vaet al. [et al. [Second, there is a clear correspondence, albeit not absolute, between our data and two other mouse microarray data sets ,17, whicet al. , who alsThird, our array data are consistent with RT-PCR analysis. We tested for expected tissue-specific expression of 107 genes (a mixture of characterized and uncharacterized) in 18 selected tissues. In this analysis a single primer pair was tested for each gene.  Among the 55 primer pairs that could result in amplification, 53 (96%) gave a correct-product size in the tissue(s) expected on the basis of our array data, and 47 (85%) produced amplification most strongly or exclusively in the expected tissue(s) . We considered the XM genes to be 'expressed' only if their intensity exceeded the 99th percentile  of intensities from the negative controls Figure . 21,622 To examine the relationships among tissues and gene functions, we asked whether genes carrying specific Gene Ontology 'Biological Process' (GO-BP) categories, which reflect the physiological function of a gene, were preferentially expressed in each of the tissue samples, using a statistical test . A selection of the WMW scores are shown in Figure An alternative way to ask whether gene regulation corresponds to gene function is to examine the correlations among the transcript levels of genes, independent of the tissue-source information. An initial confirmation that patterns of transcript abundance correspond to gene functions comes from simply examining the behavior of all genes within distinct functional categories. For example, Figure We also asked more generally whether groups of co-expressed transcripts were associated with specific GO-BP categories. Figure It stands to reason that a gene expressed in a specific tissue is likely to be functioning in that tissue. Therefore, we next asked how accurately mammalian gene functions can be predicted on the basis of gene expression profiles. There are many anecdotal examples in which the tissue-specific or cell-type-specific expression of a gene has been used to aid in discovering its function, and this approach has been advocated in previous analyses of mouse tissue expression data (see for example, ). Our daAn alternative strategy is to generate functional predictions on the basis of transcriptional co-expression ,24, whicIn this analysis, we used support vector machines (SVMs) . An SVM Importantly, the discriminant values output by an SVM can be processed to obtain an estimate of the probability that the prediction for each gene in each category is correct (that is, an estimate of precision), on the basis of how well previously annotated genes in the given category can be distinguished from previously annotated genes that are not in the category. This is accomplished by a three-fold cross-validation strategy, in which the analysis is run three times, each time with a different one-third of the annotations masked so that the SVM algorithm does not know whether or not they are in the category when it is assigning discriminant values. Any given discriminant value is then converted to a precision value by simply asking what proportion of the masked genes with discriminant values above the given discriminant value really are in the category in question. The proportion of known genes in the category that are identified by the SVM as being in the category is also obtained at each discriminant value, and is referred to as recall. For all subsequent analyses we used precision and recall as our primary measures of success.We trained separate SVMs for each of the 992 GO-BP categories. This revealed that genes in hundreds of categories could be recognized with precision greater than 50% Figure . TypicalWe next used these trained SVMs Figure  to prediIn order to provide a set of 'highest priority' predictions, we singled out those with the highest estimated precision. Among the unannotated genes (that is, those carrying no annotation in GO-BP), 1,092 (representing 117 superGO categories) were associated with precision values of 50% or greater; thus, on the basis of the analysis above, each of these genes is more than 50% likely to be involved in the given biological process. Figure While some predictions correspond to expression in a single tissue (for example, the 56 genes predicted in 'vision' were predominantly expressed in the eye), such cases were unusual. Rather, most of the predictions were based on expression in multiple functionally related tissues  or more complex patterns . Many predictions were found to be in categories related to the cell cycle and RNA processing. These genes tended to be expressed constitutively, but were most highly expressed in embryonic tissues, presumably because of rapid cell growth during development. However, many other predictions relate to neural functions, the immune response, muscle contraction, small-molecule metabolism, and other aspects of adult physiology. All of the individual predictions are provided in a table in the Additional data files, together with the expected precision and other information regarding the gene and the encoded protein, and these can be sorted by gene or by functional category.2+-ATPase in cardiac sarcoplasmic reticulum [ESP1) gene was predicted by SVM to function in 'mitotic cell cycle', 'cytokinesis', and 'microtubule based process', consistent with the function of its yeast counterpart [Among the 1,092 unannotated genes, 488 (45%) have no overt sequence features suggesting physiological or biochemical function . Examination of the remaining 55% provided evidence that many of the predictions are likely to be correct. First, a handful of genes that were not annotated in our version of GO have in fact been characterized in the literature. For example, SVMs correctly predicted that phospholamban, the regulator of the Caeticulum  is involnterpart .A more comprehensive and objective analysis was enabled by the fact that, in an independent sequence-based analysis we conducted , known protein domain structures were encoded by 461 (42%) of these 1,092 unannotated genes  . These pet al., the authors of reference [et al. [et al. [et al. [Although there was a significant correlation among the three different mouse tissue-specific data sets compared in Figure eference . Their neference  include  [et al. , and tha [et al. , with ea [et al.  for predP < 10-76; two-sided pairwise t test), indicating that co-expression patterns can be used (by SVMs) to predict gene functions significantly better than tissue-specificity alone.We used two different approaches to ask how well tissue specificity can predict the functional classes of genes, in comparison to co-expression. First, from our data we compiled three sets of lists: genes that are expressed in each of the 55 individual samples; genes that are expressed highest in each of the individual 55 samples and in groups of functionally related samples ; and also genes that are expressed uniquely in individual samples. All of these lists  are compiled in the Additional data files. For each of the 992 GO-BP categories, we assessed the precision and recall for each of these lists (that is, whether these lists can distinguish genes in the category from those not in the category), and then identified the best precision value and its associated recall value for that category. Figure ad hoc procedures for sorting the genes in different ways, or by more automated procedures for generating large numbers of lists. However, an alternative analysis suggests that this is unlikely: when we re-ran SVMs with the matrix of 1s and 0s indicating which gene is expressed (or not) in each tissue, rather than the matrix of quantitative expression values, the resulting predictions were inferior . These Third, while previous analyses using microarray expression data to predict gene functions -10 have PWP1 is an example of a gene that is widely expressed but has a pattern of expression that was predictive of its function , all ofWe have created an extensive mouse expression data set and asked whether quantitative gene expression patterns correspond to functional gene categories. Our major finding is that most tissues express many functional categories, consistent with the fact that they contain many different cell types performing many different functions, but that many different functional pathways are coordinately expressed in a quantitative manner across tissues such that many categories display one or more distinctive patterns. For example, embryonic heads contain many cell types, and consequently express genes in a variety of categories including 'CNS/brain development', 'M phase', 'skeletal development', and 'microtubule-based process', yet an SVM can distinguish genes in these categories because they are differentially regulated across all 55 tissues in a way that is characteristic for each functional category Figures . The simMouse tissues were isolated from the following strains: ICR ; CD1 , tongue surface, snout, large intestine, thyroid, aorta, brown fat, lymph node, olfactory bulb, adrenal gland, prostate, digits, trachea, trigeminal nucleus); C3H ; C57BL/6 ; Black Swiss ; and R1 (ES cells). With the exception of embryonic tissues and ES cells, tissues were harvested from 3–6 month-old mice. Following recommended University of Toronto protocols, mice were euthanized by barbiturate injection and tissues were dissected as quickly as possible (within 10 minutes), snap-frozen in liquid nitrogen, and preserved at -80°C until use. RNA was extracted using homogenization and Trizol reagent (Invitrogen, Carlsbad, USA) following the instructions from the manufacturer, and mRNA was purified as described previously [m-balanced probes were generated using PrimerX [A FASTA file of 42,192 known and predicted mRNAs (XM sequences) was obtained from Deanna Church at NCBI on July 9, 2002 and is posted as Additional data file 1. Interspersed repeats and low complexity DNA sequences were masked with Repeat-Masker [ PrimerX  with def PrimerX . The spe PrimerX .Among the probes on the array, 40,822 were unique; those that were not unique can be attributed primarily to gene duplications, predominantly pseudogenes of GAPDH, ribosomal proteins, and retrovirus-like sequences. To minimize the impact of redundancy on statistical analyses, we collapsed the data from 1,928 duplicated probes and XM sequences that were in these sequence families (including 100 probes duplicated between the two array designs) into 525 groups that shared identical probe sequence and/or were both annotated and regulated in the same way. We also mapped all of the XM sequences to the current version of the mouse genome (Build 32) and to three cDNA databases  and identified 1,991 XM sequences in which XM sequences adjacent on the chromosome also mapped to the same cDNA; these were collapsed into 904 groups. The Additional data files include a table mapping the 41,699 probes against the 39,309 presumed distinct transcripts.18VN (0.25 μg per reaction) to synthesize cDNA. The reaction contained a 1:1 mixture of 5- thymidine 5'-triphosphate (Sigma, St. Louis, USA) and thymidine triphosphate (TTP) in place of TTP alone. The cDNA products were bound to QIAquick PCR Purification columns (Qiagen, Hilden, Germany) following the manufacturer's instructions, washed three times with 80% ethanol, and eluted with water. Purified cDNA was reacted with N-hydroxy succinimide esters of Cy3 or Cy5 (Amersham Pharmacia Biotech, Piscataway, USA) following the manufacturer's instructions. Hydroxylamine-quenched Cy-labeled cDNAs were separated from free dye molecules using QIAquick columns. Mixed labeled cDNAs were added to hybridization buffer containing 1 M NaCl, 0.5% sodium sarcosine, 50 mM methyl ethane sulfonate (MES), pH 6.5, 33% formamide and 40 μg herring sperm DNA (Invitrogen, Carlsbad, USA). Hybridizations were carried out in a final volume of 0.5 ml injecting into an Agilent hybridization chamber at 42°C on a rotating platform in a hybridization oven  for 16–24 h. Slides were then washed (rocking for approximately 30 seconds in 6 × SSPE, 0.005% sarcosine, then rocking for approximately 30 seconds in 0.06 × SSPE) and scanned with a 4000A microarray scanner (Axon Instruments, Union City, USA). Hybridizations were performed in duplicate with fluor reversal: that is, each mRNA sample was examined in duplicate, once in the Cy3 channel and once in the Cy5 channel, on separate arrays. Each array was hybridized with two samples simultaneously, each from an individual tissue. Essentially identical results were obtained from single-channel data from the same mRNA sample analyzed on different arrays, which were distinct from individual channels on the same arrays analyzed with a different mRNA. The organization of the hybridizations, and the data for individual channels, are given in the Additional data files.The mRNA (1–2 μg) was reverse-transcribed with random nonamer primers (1 μg per reaction) and TTIFF images were quantitated with GenePix (Axon Instruments). Individual channels were spatially detrended  by high-pass filtering (see ) using 1Mouse GO-BP annotations were downloaded from the Gene Ontology website  and the P value [P values were assigned to these measures using a permutation scheme on the gene labels. The statistical significance of the P values was evaluated using the Benjamini-Hochberg (BH) linear step-up procedure [P values of its co-regulated group. A gene-specific permutation scheme associated P values with these scores and the FDR was also controlled at 1%.Each gene was associated with a co-regulated group consisting of the 50 annotated genes with the highest Pearson correlation coefficient relative to it. Annotation enrichment of this group in each GO-BP category was scored using the hypergeometric P value . The minrocedure  to ensurStarting with an initial hierarchical clustering (agglomerative, average linkage, based on Pearson correlation coefficient), rows were divided into groups by removing a small number of links at the highest levels of the tree and grouping together all rows contained within the same disconnected subtree. Each row group was then associated with the column that contained the maximum expression value averaged over all the profiles in the group. The row groups were then sorted in increasing order of their associated column numbers.We used the SVM software package Gist  version We identified the closest corresponding mouse mRNAs in FANTOM II   and [56al. data ). The Su [et al.  gene exp [et al.   and sequences are listed in the Additional data files. RT-PCR assays were performed using the OneStep RT-PCR Kit (Qiagen). Reactions were performed in 25 μl volumes containing 0.5 ng polyA+ mRNA, 7.5 units porcine RNAguard (Amersham) and 300 pM each of the forward and reverse primers. After 30 rounds of amplification, the reaction products were separated on 2% agarose gels stained with ethidium bromide. Inverted black-and-white images of the gels were recorded using a Syngene gel documentation system and GeneSnap software (Synopics, Frederick, USA). In total, 107 primer pairs were tested. Of the 57 XM genes tested that corresponded to a known cDNA, 42 were among those that were amplified (74%). Of the 25 tested that corresponded to an EST but not to a known cDNA, 12 were amplified (48%). However, of the 25 tested that did not correspond to a cDNA or EST, only one was amplified (4%).Primer pairs were designed to have a matching TSix different gene-trap resources were searched to identify genes associated with gene trap ES cell lines. For BayGenomics , Centre TetO7-PWP1 and isogenic wild-type control strains were created and analyzed as previously described for other essential yeast genes [The st genes . Brieflyst genes . TAP purst genes  using 1.There are 40 Additional data files comprising all the raw data; they are also available on our website . A web tXM (predicted mRNA sequences) from NCBIClick here for additional data fileXP (encoded protein sequences) from NCBIClick here for additional data fileArray Design 1 (spot map)Click here for additional data fileArray Design 2 (spot map)Click here for additional data fileArray Master file - 41,699 probes; the Master file contains probe sequences, columns listing the closest sequences in RIKEN, ENSEMBL, Refseq, and Unigene; GenBank description, EST overlap, GO-BP annotations, Domain (most significant)Click here for additional data fileArray information after removal of redundant probes: probe combinationsClick here for additional data fileArray information after removal of redundant probes: master file - 39,309 presumed distinct transcriptsClick here for additional data fileHybridization recordsClick here for additional data fileFigure 1 dataClick here for additional data fileFigure 2b dataClick here for additional data fileFigure 4a dataClick here for additional data fileFigure 4b dataClick here for additional data fileFigure 4c dataClick here for additional data fileFigure 4d dataClick here for additional data fileFigure 5 dataClick here for additional data fileFigure 7 dataClick here for additional data fileSupplementary figures S1-3Click here for additional data fileData: 41,699 probes - single channel, arcsinh intensitiesClick here for additional data fileData: 41,699 probes - replicates combined, arcsinh intensitiesClick here for additional data fileData: 41,699 probes - median-subtracted, zeroedClick here for additional data fileData: 39,309 presumed distinct transcripts - median-subtracted, zeroedClick here for additional data fileData: 21,622 presumed distinct transcripts - median-subtracted and zeroed, expressed above 99% of negative-control spotsClick here for additional data fileData: binary matrix of expression above 99% of negative-control spotsClick here for additional data fileAnnotations: GO annotations among 39,309 presumed distinct transcripts (12,543 annotated genes, 47,900 annotations)Click here for additional data fileAnnotations: GO annotations among 21,622 presumed distinct transcripts (9,499 annotated genes, 37,876 annotations)Click here for additional data fileAnnotations: superGO annotationsClick here for additional data fileAnnotations: map between GO and superGO annotationsClick here for additional data fileSVM Predictions: GO-BP SVM Predictions, 15% precisionClick here for additional data fileSVM Predictions: GO-BP SVM Predictions, 50% precisionClick here for additional data fileSVM Predictions: superGO SVM Predictions, 15% precisionClick here for additional data fileSVM Predictions: superGO SVM Predictions, 50% precisionClick here for additional data fileRT-PCR primer sequencesClick here for additional data fileXM genes with gene trap linesClick here for additional data fileXM motifsClick here for additional data file779 known and putative DNA-binding transcription factors among XM genesClick here for additional data fileTable of accession numbers of genes common to Zhang, Su, and Bono dataClick here for additional data fileTech report on spatial detrendingClick here for additional data fileMap locations of XM genes (BLASTed against Build 32) - retained only if top hit of both XM sequence and array probe overlap (30,387 presumed distinct transcripts)Click here for additional data fileSVM functional predictions for 7,147 unannotated mapped transcripts , also see [19]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 (feel free to make a thousand copies, translate articles into other languages, put articles into books—just give the author proper credit).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 . 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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 (surface receptors, cell adhesion, immune response, etc.), while those in cold regions are biased toward essential cellular processes (gene regulation, RNA processing, protein modification, etc.). 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 ∈ [0, 10000000]. 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 (A0 = 1.08, τ = 0.73 × 106, A∞ = 0.32), 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 (“nucleus,” “regulation of transcription,” “transcription factor activity,” “RNA binding activity,” “development,” “ribonucleoprotein complex,” “protein transporter activity,” “protein serine/threonine kinase activity,” “ubiquitin conjugating enzyme activity,” “GTP binding activity,” “ubiquitin-dependent protein catabolism,” “translation regulator activity,” “intracellular protein transport,” “neurogenesis,” “ubiquitin cycle,” “cytoplasm,” “regulation of transcription from Pol II promoter”). 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 ∈ [0, 15000000]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 (