Hypoxia-inducible factor-1alpha (HIF1alpha) is a central regulator of the cellular response to hypoxia. Prolyl-hydroxylation of HIF1alpha by PHD enzymes is prerequisite for HIF1alpha degradation. Here, we demonstrate that the abundance of PHD1 and PHD3 are regulated via their targeting for proteasome-dependent degradation by the E3 ubiquitin ligases Siah1a/2, under hypoxia conditions. Siah2 null fibroblasts exhibit prolonged PHD3 half-life, resulting in lower levels of HIF1alpha expression during hypoxia. Significantly, hypoxia-induced HIF1alpha expression was completely inhibited in Siah1a/2 null cells, yet could be rescued upon inhibition of PHD3 by RNAi. Siah2 targeting of PHD3 for degradation increases upon exposure to even mild hypoxic conditions, which coincides with increased Siah2 transcription. Siah2 null mice subjected to hypoxia displayed an impaired hyperpneic respiratory response and reduced levels of hemoglobin. Thus, the control of PHD1/3 by Siah1a/2 constitutes another level of complexity in the regulation of HIF1alpha during hypoxia.

O2 consumption (VO2), CO2 production (VCO2), and minute ventilation (VE) have been measured during normoxia and hypoxia (10-20 min in 10% O2) in specimens of 27 species from 6 mammalian orders, ranging in body mass (M) from a few grams to several kilograms. In normoxia, both metabolism and VE scaled close to M3/4, VE/VO2 and VE/VCO2 therefore being independent of M. In hypoxia, VE/metabolism increased in all species (on average greater than 100%), mostly because of a drop in VO2. On average, VE was 23% above the normoxic value but in some species decreased below normoxia. VO2 dropped in all but one species, on average 35%. Body temperature decreased by variable amounts, usually more in the smallest species. The decrease in metabolism during hypoxia was positively correlated with the resting metabolic rate of the species in a manner very similar to what can be calculated from data of previously studied newborn mammals. Hence hypoxia may decrease metabolic rate by decreasing thermogenesis, with larger effects in smaller animals, whether newborns or adults, because of their higher thermogenic requirements. We conclude that 1) hypoxic hypometabolism is a general characteristic of the mammalian response to hypoxia and cannot be neglected in the interpretation of ventilatory and cardiovascular responses and 2) its magnitude is inversely related to the resting VO2 of the species and therefore could be less prominent or possibly absent in adults of larger species.

The physiological and biomechanical requirements of flight at high altitude have been the subject of much interest. Here, we uncover a steep relation between heart rate and wingbeat frequency (raised to the exponent 3.5) and estimated metabolic power and wingbeat frequency (exponent 7) of migratory bar-headed geese. Flight costs increase more rapidly than anticipated as air density declines, which overturns prevailing expectations that this species should maintain high-altitude flight when traversing the Himalayas. Instead, a " roller coaster" strategy, of tracking the underlying terrain and discarding large altitude gains only to recoup them later in the flight with occasional benefits from orographic lift, is shown to be energetically advantageous for flights over the Himalayas. M igrating birds must overrome many chal lenging environmental obstacles, such as arid deserts (1, 2) and featureless oceans (3 5), but few are capable of negotiating the formidably high mountains separat ing the Indian subcontinent from central Asia. Famously, one species that manages this feat is the bar beaded goa;e (Anser imicus), wbidl bi

Metabolic cold adaptation (MCA), the hypothesis that species from cold climates have relatively higher metabolic rates than those from warm climates, was first proposed nearly 100 years ago and remains one of the most controversial hypotheses in physiological ecology. In the present study, we test the MCA hypothesis in fishes at the level of whole animal, mitochondria and enzyme. In support of the MCA hypothesis, we find that when normalized to a common temperature, species with ranges that extend to high latitude (cooler climates) have high aerobic enzyme (citrate synthase) activity, high rates of mitochondrial respiration and high standard metabolic rates. Metabolic compensation for the global temperature gradient is not complete however, so when measured at their habitat temperature species from high latitude have lower absolute rates of metabolism than species from low latitudes. Evolutionary adaptation and thermal plasticity are therefore insufficient to completely overcome the acute thermodynamic effects of temperature, at least in fishes.

Birds that fly over mountain barriers must be capable of meeting the increased energetic cost of climbing in low-density air, even though less oxygen may be available to support their metabolism. This challenge is magnified by the reduction in maximum sustained climbing rates in large birds. Bar-headed geese (Anser indicus) make one of the highest and most iconic transmountain migrations in the world. We show that those populations of geese that winter at sea level in India are capable of passing over the Himalayas in 1 d, typically climbing between 4,000 and 6,000 m in 7-8 h. Surprisingly, these birds do not rely on the assistance of upslope tailwinds that usually occur during the day and can support minimum climb rates of 0.8-2.2 km·h −1 , even in the relative stillness of the night. They appear to strategically avoid higher speed winds during the afternoon, thus maximizing safety and control during flight. It would seem, therefore, that bar-headed geese are capable of sustained climbing flight over the passes of the Himalaya under their own aerobic power.exercise physiology | high altitude | satellite tracking | vertebrate migration | climbing flight M ountains and high plateaus present formidable obstacles to the migratory flights of a number of bird species. Large birds, such as cranes and geese, may find such barriers particularly challenging as the sustained climbing rates of birds scale negatively with increasing body mass (1). For example, brent geese (Branta bernicla) are unable to sustain climbing flights over the Greenland icecap (summit elevation 3,207 m, mean elevation >2,000 m) and make regular stops to recover, possibly from partly anaerobic flights (2). Nevertheless, populations of bar-headed geese (Anser indicus) that spend the winter at sea level in India and the summer in central Asia must perform the world's steepest migratory flight north over the highest mountain range on earth, the Himalaya (3). There, most passes are at altitudes greater than 5,000 m, where the air density and partial pressure of oxygen are only about half of those at sea level. As a consequence, the partial pressure of oxygen (PO 2 ) in the arterial blood may begin to limit maximum performance (4, 5), although negative effects on the rate of oxygen diffusion may be partially ameliorated by an increase in the gas diffusion coefficient (6). The thinner air at these higher altitudes will also reduce lift generation during flapping flight for any given air speed, thus increasing the energy costs of flying by around 30% (7,8).However, bar-headed geese have adapted in a variety of ways for living and flying at high altitudes (4, 5). Their skeletal and cardiac muscles are better supplied with oxygen, having greater capillary density, more homogenous capillary spacing, a higher proportion of mitochondria in a subsarcolemmal location, and a greater proportion of oxidative fibers than other waterfowl (9, 10). Bar-headed goose hemoglobin is also highly effective at oxygen loading (11), compared with many other bird species, largel...

Monitoring the physiological status and behaviour of free-swimming fishes remains a challenging task, although great promise stems from techniques such as biologging and biotelemetry. Here, implanted data loggers were used to simultaneously measure heart rate (f (H)), visceral temperature, and a derivation of acceleration in two groups of wild adult sockeye salmon (Oncorhynchus nerka) held at two different water speeds (slow and fast). Calibration experiments performed with individual fish in a swim tunnel respirometer generated strong relationships between acceleration, f (H), tail beat frequency and energy expenditure over a wide range of swimming velocities. The regression equations were then used to estimate the overall energy expenditure of the groups of fish held at different water speeds. As expected, fish held at faster water speeds exhibited greater f (H) and acceleration, and correspondingly a higher estimated energy expenditure than fish held at slower water speeds. These estimates were consistent with gross somatic energy density of fish at death, as determined using proximate analyses of a dorsal tissue sample. Heart rate alone and in combination with acceleration, rather than acceleration alone, provided the most accurate proxies for energy expenditure in these studies. Even so, acceleration provided useful information on the behaviour of fish and may itself prove to be a valuable proxy for energy expenditure under different environmental conditions, using a different derivation of the acceleration data, and/or with further calibration experiments. These results strengthen the possibility that biologging or biotelemetry of f (H) and acceleration may be usefully applied to migrating sockeye salmon to monitor physiology and behaviour, and to estimate energy use in the natural environment.

Measurement of acceleration can be a proxy for energy expenditure during movement. The variable overall dynamic body acceleration (ODBA), used in recent studies, combines the dynamic elements of acceleration recorded in all three dimensions to measure acceleration and hence energy expenditure due to body movement. However, the simplicity of ODBA affords it limitations. Furthermore, while accelerometry data loggers enable measures to be stored, recording at high frequencies represents a limit to deployment periods as a result of logger memory and/or battery exhaustion. Using bantam chickens walking at different speeds in a respirometer while wearing an accelerometer logger, we investigated the best proxies for rate of oxygen consumption (Vo(2)) from a range of different models using acceleration. We also investigated the effects of sampling acceleration at different frequencies. The best predictor of Vo(2) was a multiple regression including individual measures of dynamic acceleration in each of the three dimensions. However, R(2) was relatively high for ODBA as well and also for certain measures of dynamic acceleration in single dimensions. The aforementioned are single variables, therefore easily derived onboard a data logger and from which a simple calibration equation can be derived. For calibrations of Vo(2) against ODBA, R(2) was consistent as sampling number decreased down to 600 samples of each acceleration channel per ODBA data point, beyond which R(2) tended to be considerably lower. In conclusion, data storage can be maximized when using acceleration as a proxy for Vo(2) by consideration of reductions in (1) number of axes measured and (2) sampling frequency.

Several errors were published in the original online version of J. Exp. Biol. 212,[471][472][473][474][475][476][477][478][479][480][481][482]. These errors occurred in both the PDF and fulltext versions of the online article but have now been corrected. The print version is correct.The captions of Figs 9 and 10 were truncated, and the caption published under Fig. 11 referred to Fig. 10.Figs 9-11, together with the correct captions, are printed below.We sincerely apologise to all authors and readers of this article for any inconvenience this has caused. Erratum Fig. 9. Rate of oxygen consumption as a function of heart rate in eight bantam chickens. Data were recorded while the chickens walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Also plotted are two best-fit regression lines (solid line) and 95% confidence intervals (black dashed lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if sVO 2 was estimated from one measurement of heart rate, during one additional behaviour by one additional chicken. 95% prediction intervals were calculated as if sVO 2 was estimated from 10,000 measurements of heart rate, during four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model. . Also plotted are best-fit regression lines (solid line) and 95% confidence intervals (black broken lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if sVO 2 was estimated from one measurement of heart rate, during one behaviour by one additional chicken. 95% prediction intervals were calculated as if sVO 2 was estimated from 10,000 measurements of heart rate, during four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model. the standard error of the estimate (s.e.e.) and (C) the coefficient of variation (CV=100*s.e.e./Estimate) were calculated for this range of activity using four predictive approaches. Each of the four approaches used either partial dynamic body acceleration in the x and z axis (PDBAxz) or heart rate (fH) to predict VO 2 . The approaches used were (1) one-model using PDBAxz (black solid lines), (2) two-model using PDBAxz (grey solid lines), (3) one-model using fH (black broken lines), (4) two-model using fH (grey broken lines). See text for further details of the four predictive approaches.