HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (33) Citing Articles via Google Scholar Google Scholar Articles by Boron, W. F. Search for Related Content PubMed PubMed Citation Articles by Boron, W. F.

REPORT

Regulation of intracellular pH

Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, Connecticut 06520-8026

Address for reprint requests and other correspondence: W. F. Boron, Dept. of Cellular and Molecular Physiology, Yale Univ. School of Medicine, 333 Cedar St., New Haven, CT 06520 (E-mail: walter.boron{at}yale.edu)

Abstract

The approach that most animal cells employ to regulate intracellular pH (pH_i) is not too different conceptually from the way a sophisticated system might regulate the temperature of a house. Just as the heat capacity (C) of a house minimizes sudden temperature (T) shifts caused by acute cold and heat loads, the buffering power (ß) of a cell minimizes sudden pH_i shifts caused by acute acid and alkali loads. However, increasing C (or ß) only minimizes T (or pH_i) changes; it does not eliminate the changes, return T (or pH_i) to normal, or shift steady-state T (or pH_i). Whereas a house may have a furnace to raise T, a cell generally has more than one acid-extruding transporter (which exports acid and/or imports alkali) to raise pH_i. Whereas an air conditioner lowers T, a cell generally has more than one acid-loading transporter to lower pH_i. Just as a house might respond to graded decreases (or increases) in T by producing graded increases in heat (or cold) output, cells respond to graded decreases (or increases) in pH_i with graded increases (or decreases) in acid-extrusion (or acid-loading) rate. Steady-state T (or pH_i) can change only in response to a change in chronic cold (or acid) loading or chronic heat (or alkali) loading as produced, for example, by a change in environmental T (or pH) or a change in the kinetics of the furnace (or acid extrudes) or air conditioner (or acid loaders). Finally, just as a temperature-control system might benefit from environmental sensors that provide clues about cold and heat loading, at least some cells seem to have extracellular CO₂ or extracellular HCO₃^– sensors that modulate acid-base transport.

Key words: hydrogen ions; bicarbonate; exchanger; cotransporter

INTRACELLULAR pH (pH_i), which for our purposes we will regard as the pH of the aqueous cytosolic solution, is a parameter that is of interest to most biologists. The reason for this interest is that changes in pH_i affect the ionization state of all weak acids and weak bases–a bewildering array of cellular molecules that includes all peptides and proteins–and thus may potentially affect a wide array of biological processes. Not surprisingly, all animal cells that have been examined, aside from non-nucleated erythrocytes, vigorously regulate their pH_i (36). They do this by sensing changes in pH_i and then appropriately speeding up or slowing down various transporters that move acids and/or bases across the plasma membrane.

Many nonspecialists find it difficult to think about pH_i and pH_i regulation. After all, the pH scale is upside down (increases in pH_i correspond to decreases in [H⁺]), and protons have the disconcerting habit of binding to molecules (at any time, more than 99.99% of available protons are bound to "buffers"). Moreover, if a proton dissociates from a buffer and collides with a water molecule, this proton may become part of a perfectly respectable H₂O molecule, but simultaneously cause a completely different proton to pop off from another H₂O molecule some distance away—an example of the effects of a proton wire.

Sound complicated? It is and it isn’t. "It is" in the sense that any biological process is complex when one considers it at the molecular level. However, my PhD mentor, the venerable Albert Roos, now retired from Washington University School of Medicine and approaching his 90th birthday, used to tell me that if a scientist really understands his or her work, the scientist should be able to explain the essence of that work to the average person on the street. Thus my job in this review should be rather easy: I must only explain pH_i regulation to the physiologist on the street.

As it happens, the way cells approach the problem of pH_i regulation is not so different–conceptually, at least–from the way we might approach the problem of regulating the temperature in a house. Just as a furnace makes the temperature go up, transporters called "acid extruders" (which move H⁺ out of the cell and/or move bases such as HCO₃^– into the cell) make pH_i go up. Similarly, just as an air conditioner makes the temperature go down, transporters called "acid loaders" (which move H⁺ into the cell and/or move bases such as HCO₃^– out of the cell) make pH_i go down. In this review, we will develop a model of temperature regulation that we will subsequently apply to pH_i regulation. Every hypothetical example of a rise or fall in temperature corresponds to an analogous real-life instance of a rise or fall in pH_i.

The reader is directed to a website (http://www.the-aps.org/education/refresher/CellRefresherCourse.htm) that includes the PowerPoint slideshow that I presented during my oral presentation at Experimental Biology 2004, on April 17, 2004.

Temperature Regulation in a House

Heat capacity. Imagine that our house has an initial temperature (T) of 22°C. Imagine also that our house has a heat capacity (C) of 50 x 10⁶ cal/°C. This value is equivalent to that of 50,000 kg of H₂O (a 50-m³ pool of water).

We will now suddenly inject into our house 50 x 10⁶ calories of heat; this is roughly the amount that would be given off by a 1,000-W electrical heater over a period of 2.4 days. If we wait long enough for the injected heat to equilibrate with the mass of the house—and if no other processes add or withdraw heat—the temperature will increase by precisely 1°C:

(1)

In other words, the temperature of our house will rise from 22°C to 23°C.

If we introduce a 50-m³ indoor swimming pool into our house, the heat capacity will double to 100 x 10⁶ cal/°C. Thus a heat load of 50 x 10⁶ calories will cause the temperature to increase by only 0.5°C

(2)

Because the magnitude of the temperature change is inversely proportional to the heat capacity, raising the heat capacity of the house increases the ability to resist temperature fluctuations. In other words, heat capacity represents the ability to "buffer" loads of heat or cold, just as buffering power (ß) in a cell represents the ability to resist pH_i fluctuations in the face of loads of alkali or acid. However, raising the heat capacity (or ß) does not eliminate temperature (or pH_i) fluctuations, it only reduces their magnitude. Moreover, once we have imposed a heat/cold load (or alkali/acid load) and observed the resulting increase/decrease in temperature (or pH_i), subsequently raising the heat capacity (or ß) does not return the temperature (or pH_i) to its initial value, which would be 22°C in our temperature example.

The last statement in the previous paragraph is worthy of some clarification. Consider our example in Eq. 1, in which C is 50 x 10⁶ cal/°C. Imposing a heat load of 50 x 10⁶ calories will cause the temperature to rise from 22 to 23°C. If we now double the heat capacity by adding 50,000 kg of water with a temperature of 23°C, the temperature of the house (and that of the water that we just added) will remain at 23°C. If we had instead added 50,000 kg of water with a temperature of 22°C, the temperature of the house would fall by 0.5°C, and that of the water would rise by the same amount. However, this maneuver of adding 22°C water to the 23°C house simultaneously did two things: 1) it raised the heat capacity and 2) it injected a "cold" load. In analyzing the effects of various maneuvers—both in our temperature-regulation model and in pH_i regulation—we must constantly be vigilant to identify such dual effects.

Perturbations: Acute and Chronic Heat/Cold Loads

Acute heat loads. In a house, appliances can impose a heat load: hot plates, toasters, ovens, clothes dryers, irons, and water heaters, to name a few. On a hot day, opening an exterior door imposes a heat load on an air-conditioned house. These heat loads I will define as "acute," acute as opposed to "chronic," which we will discuss later. The most critical attributes of acute heat loads is that they exert their effects over a limited period of time that is—from the perspective of our observations—relatively brief. Thus we can easily describe the absolute magnitude of the heat load. For example, if one turns on a 1,000-W hair drier for 2 min, the imposed heat load is

(3)

Moreover, we can predict that this acute heat load will raise the temperature of our house (the one without the swimming pool) by 0.000, 57°C:

(4)

Later, we will see that acutely loading a cell with a known amount of alkali will produce a pH_i increase, the magnitude of which we can predict-using an equation analogous to Equation 4-if we know the buffering power.

Chronic heat loads. Unlike the heat sources discussed above, those that impose a chronic heat load exert their effects over an indefinite period of time that—from the perspective of our observations—is relatively long. For example, on a sunny summer day, the heat introduced from the environment by radiation, conduction, and convection may impose a heat load for many hours. Chronic heat loaders are best described in terms of the rate at which they impose a heat load, inasmuch as it is impossible to predict the total heat load.

In our temperature-regulation model, the distinction between acute and chronic heat loads is somewhat artificial. For example, if we inadvertently left an iron "on" indefinitely, the iron would become a chronic heat loader. In moderate climates, the heat load imposed by sun has a predictably limited duration (i.e., daylight hours); does the sun then become an acute heat loader? Nevertheless, when we turn to pH_i regulation, we will see that alkali loads to a cell usually segregate rather neatly into those with either a limited duration (and thus magnitude), such as exposing a cell to a permeant weak base, or an indefinite duration (and thus magnitude), such as stimulating a transporter that continually imports HCO₃^– into the cell.

Acute cold loads. Our house might also have a few mechanisms for imposing acute cold loads, such as frozen food left to thaw on the kitchen counter, or a refrigerator with an open door. On a cold winter day, we could impose a more substantial acute cold load by temporarily opening an exterior door. Each of these mechanisms exerts its effects over a limited and relatively short period of time, and thus withdraws a limited amount of energy from our house. The counterpart in pH_i regulation might be the acute acid load imposed by exposing a cell to a permeant weak acid.

Chronic cold loads. During the winter, the heat loss from our house to the environment represents a chronic cold load. By analogy to chronic heat loaders, chronic cold loaders are best described in terms of the rate at which they impose a cold load. The counterpart in pH_i regulation might be the chronic acid load imposed by a transporter that continually imports H⁺ or exports HCO₃^–.

Regulation: Furnaces and Air Conditioners

A "dumb" temperature-control system. Having mastered the concepts of heat capacity and the various kinds of heat and cold loads, we are in a position to discuss temperature regulation. Like any physiological regulatory system, our temperature-regulating system must have certain key components:

• A temperature sensor,
  
• A coordinating center, and
  
• Effectors for injecting heat into and/or withdrawing heat from our house.
  

In our first example (Fig. 1A), the system will work like that in a typical house. The furnace will have two speeds ("off" and "on"), and the coordinating center will turn on the furnace whenever the temperature falls below 20°C. The air conditioner also will have two speeds ("off" and "on"), and the coordinating center will turn on the air conditioner whenever the temperature rises above 24°C. If the furnace and air conditioner are sufficiently powerful, the temperature-control system will insure that the temperature does not fall far below 20°C, nor does not rise far above 24°C.

View larger version (22K): [in this window] [in a new window]   Fig. 1. The temperature (T) dependencies of hypothetical furnaces and air conditioners. A: "dumb" temperature controller. Here, the furnace produces heat at a fixed rate whenever the temperature falls below a threshold of 20°C. Similarly, the air conditioner withdraws heat at a fixed rate whenever the temperature rises above a threshold of 24°C, which must be above the threshold for the furnace. B: "smart" temperature controller. Here, the furnace is always on, but its rate of heat production increases linearly as the temperature falls. Conversely, the rate at which the air conditioner withdraws heat increases linearly as the temperature rises. If the furnace is the only chronic heat loader, and if the air conditioner is the only chronic cold loader, then the system is in a steady state at 22°C, when the rates of heat loading (Jheat) and cold loading (Jcold) are equal. C: "smart" temperature controller with lumped Jheat-vs.-T and Jcold-vs.-T parameters. Here, we combine the temperature dependence of the furnace with that of all nonregulatory chronic heat loaders to obtain Jheat. Similarly, we combine the temperature dependence of the air conditioner with that of all non-regulatory chronic cold loaders to obtain Jcold. cal, calorie.

Later, we will see that cells use acid-extruding transporters (analogous to furnaces) that raise pH_i, more so as pH_i falls. Moreover, cells have acid-loading transporters (analogous to air conditioners) that lower pH_i, more so as pH_i rises. In the steady state, these two sets of transporters continuously fight each other, consuming metabolic energy-the price that life is willing to pay for the sake of control.

Total heat and cold loading. Another way of approaching the analysis of the sophisticated temperature-control system would be to lump the heat output of the furnace with that of all other chronic heat loaders that are not part of our temperature-control system. The result is the total chronic heat loading (J_heat). Similarly, we also could lump the heat withdrawal of the air conditioner with that of all other chronic cold loaders, arriving at the total chronic cold loading (J_cold). If these "other," non-regulatory chronic heat and cold loaders have relatively low temperature dependencies, then the temperature dependency of J_heat and J_cold in Fig. 1C would have shapes similar to the "Furnace" and "Air conditioner" curves in Fig. 1B. However, at any temperature, the curves would have higher values in Fig. 1C. Nevertheless, the steady-state temperature would still be described by the intersection of the two curves.

Later, we will see that the total chronic acid extrusion in cells reflects transporters that extrude acid (analogous to furnaces) and—under special circumstances—passive chronic alkali loading (analogous to nonregulatory chronic heat loading). Similarly, the total chronic acid loading in cells reflects transporters that load the cell with acid (analogous to air conditioners) as well as passive acid loading (analogous to nonregulatory chronic cold loading).

We should not regard the curves in Fig. 1C as immutable. For example, during the winter, the nonregulatory component of J_cold (or chronic acid loading for a cell) would be higher at night (or when extracellular pH is low) than during the warmer daytime hours (or when extracellular pH is high). Moreover, the slope of the J_cold-vs.-T relationship (or the acid loading vs. pH_i relationship) would decrease if the air conditioner’s filter became clogged (or if the cell downregulated an acid loader). On the other hand, the slope would increase if a fall in outside temperature made the air conditioner more efficient (or if a change in ion gradients made an acid loader more efficient). The J_heat-vs.-T relationship would be subject to comparable changes. In other words, the curves in Fig. 1C describe the temperature dependencies of chronic heat and cold loading under a defined set of conditions that are subject to alteration.

Response to an acute cold load. Our sophisticated temperature-control system is now ready for a challenge. Imagine that it is a frigid winter’s evening, and that the temperature of our house is at a stable 22°C. Under these starting conditions, J_heat exactly matches J_cold, as represented by the intersection of the two curves in Fig. 2A. A blast of cold air through a temporarily open door now lowers the temperature from 22°C to 4°C (step 1 in Fig. 2A). Sensing this decrease in temperature, our control system increases the heat output of the furnace and simultaneously lowers the cold output of the air conditioner (step 2 in Fig. 2A). Similarly, cells will respond to a sudden decrease in pH_i by stimulating their acid extruders and inhibiting acid loaders. Returning to our temperature-regulation model, because J_heat is now far greater than J_cold, the temperature begins to rise. How fast? Consider a more general representation of Eq. 1:

View larger version (30K): [in this window] [in a new window]   Fig. 2. Response to acute cold and heat loads. A: effect of an acute cold load on Jheat and Jcold. After the sudden decrease in temperature (step 1), temperature at first increases rapidly (step 2) because Jheat greatly exceeds Jcold. As temperature gradually recovers, the rate of temperature increase slows (steps 3a and 3b) until the temperature eventually reaches its initial value at the intersection of the red and blue curves. B: time course of temperature in response to an acute cold load. At any instant in time, the rate of the temperature recovery is proportional to the difference (Jheat – Jcold), but inversely proportional to heat capacity. C: time course of temperature in response to an acute heat load. In this example, the blast of hot air introduces as much heat as was removed by the blast of cold air in the example in panels A and B.

We now transform this equation by dividing both sides by the time interval t:

(6)

In other words, the rate of temperature increase is proportional to the difference between the chronic rates of heat loading and cold loading, and the proportionality constant is the reciprocal of heat capacity. Thus, if the heat capacity were infinite, the temperature would never change, whereas if the heat capacity were very low, even small differences between J_heat and J_cold would lead to rapid temperature changes. We might regard Eq. 6 as the fundamental law of temperature regulation. As we will see in Eqs. 21 and 22, a comparable law exists for pH_i regulation in cells.

Returning now to step 2 in Fig. 2A, we can appreciate that the difference J_heat – J_cold, and thus the rate at which the temperature increases, is maximal immediately after the blast of cold air–when the temperature is lowest. As temperature increases, the rate of heat production by the furnace gradually decreases and the rate of heat withdrawal by the air conditioner gradually increases. Thus, the difference (J_heat – J_cold) gradually becomes smaller until, at the original temperature of 22°C, J_heat = J_cold and the system returns once again to its original steady state (i.e., T/t = 0). Similarly for cells that are recovering from an acute acid load, the difference between acid-loading and acid-extrusion rates is greatest when pH_i is lowest, and gradually falls to zero as pH_i rises toward its original steady-state value. During the course of a temperature recovery, the net amount of heat introduced by the temperature-control system (i.e., the increment in J_heat – J_cold integrated over time) is exactly equal to the net amount of cold introduced by the blast of cold air. We can make a comparable statement for cells, where the net amount of alkali extruded during a pH_i recovery exactly equals the amount of acid introduced by the insult—acute acid load.

The solid curve in Fig. 2B illustrates the hypothetical time course of the temperature recovery when the house has a normal heat capacity. If we wished to be more precise, we could express Eq. 6 as a differential equation, which we could solve to obtain an exponential expression that would describe the time course of T vs. time (t). Knowing the heat capacity of our house and the amount of heat withdrawn by the blast of cold air, we could compute the initial T. Knowing the actual value of J_heat and J_cold over the relevant range of temperatures, we could compute the entire time course of T vs. t from the instant of the big blast to the eventual reachievement of the steady state.

The dashed curve in Fig. 2B illustrates how the system would respond if we were to double the heat capacity of our house (or buffering power of a cell) by adding that 50-m³ swimming pool (or buffers to a cell). Notice that, for the same acute cold load (or acute acid load), the magnitude of the initial temperature (or pH_i) decrease is half of what it was before. Also notice that the time course of the exponential recovery of temperature (or pH_i) is stretched out by a factor of 2 (i.e., the time constant has doubled). Nevertheless, the total amount of heat withdrawn by the blast of cold air (or acid introduced by the acute acid load) equals the net amount of heat (or alkali) subsequently introduced by the temperature-control (or pH_i control) system, and both values are identical to their counterparts in the first example (solid curve in Fig. 2B), in which the heat capacity (or ß) was normal.

Response to an acute heat load. The solid curve in Fig. 2C illustrates the hypothetical response of our temperature-control (or pH_i control) system, assuming a normal heat capacity (or ß), to an acute heat (or alkali) load, such as we might observe upon opening an exterior door on a hot summer day (or injecting a cell with alkali). For the sake of symmetry, we assume that the magnitude of the acute heat load in this example is exactly the same as the magnitude of the acute cold in the previous one. Thus the blast of hot air causes the temperature to rise to 40°C. Our temperature-control (or pH_i control) system will respond by immediately reducing the rate of heat output by the furnace (or acid extrusion) and increasing the rate of heat withdrawal by the air conditioner (or acid loading), in a manner analogous to that shown in Fig. 2A. As a result, the temperature (or pH_i) falls with an exponential time course, with J_heat (or the chronic acid-extrusion rate) gradually rising and J_cold (or the chronic acid loading rate) gradually falling until these two parameters once again come into balance at the original temperature of 22°C (or pH_i).

As we saw in the blast-of-cold-air example, doubling the heat capacity reduces the initial T by a factor of 2 and increases the time constant of the temperature recovery by a factor of 2. However, the final temperature is the same.

It is worth emphasizing three points. First, a high heat capacity (or buffering power) can reduce the magnitude of a temperature (or pH_i) perturbation, but cannot eliminate the perturbation. Second, the magnitude of the heat capacity (or buffering power) has no effect whatsoever on the final steady-state temperature (or pH_i). Third, final steady-state temperature (or pH_i) depends solely on the relationship between J_heat (chronic acid extrusion) and J_cold (chronic acid loading).

Regarding this last point, as long as both J_heat-vs.-T and J_cold-vs.-T remain unchanged, the final steady-state temperature must also remain unchanged. As we will see in the next section, the steady-state temperature can change only as the result of a fundamental change in the kinetics of J_heat and/or J_cold.

The recovery of temperature in Fig. 2, B and C, is an indication of temperature regulation, the ability of the system-complete with its system of sensors, a coordinating center, and effectors-to return temperature to some steady-state value. To quantify temperature regulation, we focus on the rate at which the system can return temperature to normal following an acute cold or heat load. We might express this rate as an absolute rate of temperature recovery (dT/dt, expressed as°C/s) measured at some defined temperature, a series of dT/dt values measured over a range of temperatures, or as the time constant (measured in seconds) that characterizes the trajectory of the temperature recovery. We will see later that we can use analogous terms to quantitate pH_i regulation.

Changes in steady-state temperature. As we noted in our discussion of Fig. 1C, the J_heat and J_cold curves could change due to alterations in nonregulatory heat or cold loading, or to changes in the furnace and air conditioner themselves. In this section, we will examine three examples that illustrate how changes in J_heat and/or J_cold affect steady-state temperature as well as the time course of temperature as the system achieves a new steady state.

Response to inhibiting the furnace. We begin our first experiment in this series with normal J_heat-vs.-T and J_cold-vs.-T profiles, and thus a normal steady-state temperature of 22°C. What would happen if—in an instant—we were to reduce the output of our furnace by a constant fraction across a wide range of temperatures? In Fig. 3A, we represent such a fall in heat flow by decreasing the negative slope of the J_heat-vs.-T curve (dashed red curve in Figs. 3A and step 1 in Fig. 3A). At the instant of the perturbation, the temperature would still be 22°C. However, because we have rotated the J_heat-vs.-T curve downward, J_heat at 22°C is now substantially less than J_cold at 22°C. As a result, J_heat – J_cold is a relatively large negative number, so that according to Eq. 6, temperature begins to fall rather rapidly (step 2 in Fig. 3B). As the temperature falls, however, J_heat gradually rises and J_cold gradually falls until the two come into balance at a new, lower, steady-state temperature that, in this example, is 17°C (step 3a and 3b in Fig. 3A). The solid curve in Fig. 3C shows the time course of temperature for a normal heat capacity.

View larger version (30K): [in this window] [in a new window]   Fig. 3. Response to a sudden decrease in the rate of heat production. A: dependence of Jheat and Jcold on temperature. Inhibition of the furnace is represented by a decrease in the slope of the Jheat-vs.-temperature relationship (dashed line). B: changes in Jheat and Jcold as temperature falls. Immediately after the decrease in Jheat (step 1), Jcold exceeds Jheat by a modest amount. Thus temperature initially falls at a moderate rate (step 2). As temperature continues to fall, the difference between Jcold and Jheat decreases (steps 3a and 3b), until the two values eventually come into balance at a temperature of 17°C. C: time course of temperature. At any instant in time, the rate of the temperature decline is proportional to the difference (Jcold – Jheat), but inversely proportional to heat capacity.

View larger version (44K): [in this window] [in a new window]   Fig. 15. Isolating the effects of Na-H exchange from those of other entities that may influence pHi. A: representative experiment on a single renal mesangial cell (the same data as presented in Fig. 14B). B: summary of data for total acid-base flux. In several experiments similar to that shown in A, the investigators determined the rate of pHi increase (i.e., dpHi/dt) at several discrete pHi values during the pHi recovery from the NH3/NH4+ pulse. At each pHi value, they multiplied the dpHi/dt by the total intracellular buffering power (ß) to obtain the total acid-base flux in micromoles of acid equivalents per second per liter of cytosol. At each pHi value, these data were averaged, to produce the plot shown here. C: summary of data for EIPA-insensitive acid loading. In several experiments similar to that shown in A, the investigators determined the rate of pHi decline during the exposure to EIPA at several discrete pHi values. At each pHi value, they multiplied the negative dpHi/dt by the total intracellular buffering power (ß) to obtain the total acid-base flux. At each pHi value, these data were averaged, to produce the plot shown here. D: computed pHi dependence of Na-H exchange, defined as the EIPA-sensitive acid-base flux. The data in this plot was obtained by subtracting the data in C from that in B. E: pHi dependence of Na-H exchange and EIPA-insensitive acid loading. This panel is analogous to Fig. 1B in the temperature-regulation model. The JNa-H-vs.-pHi plot replaces the plot of the heat output of the furnace in Fig. 1B. Similarly, the J"L"-vs-pHi plot replaces the plot of the cold output of the air conditioner in Fig. 1B. Note that J"L" is not the true chronic acid-loading rate, but the net chronic acid-loading rate after the subtraction of Na-H exchange. The solid portion of the J"L" plot is the line of best fit in C; the dashed line is an extrapolation of the J"L" plot in C. The solid portion of the JNa-H plot consists of the two line segments of best fit in D; the dashed line is an extrapolation of the JNa-H plot in D. Data in B–D modified from Ref. 13.

Response to stimulating nonregulatory cooling. Rather than inhibiting the furnace (or an acid extruder), as in the previous example, we can produce an identical decrease in steady-state temperature (or pH_i) either by stimulating the air conditioner (or an acid loader) or by lowering the outside temperature (or extracellular pH). In this next temperature-regulation example, we will do the latter by raising the non-regulatory component of J_cold by a constant fraction across a wide range of temperatures. We represent this effect in Fig. 4A the increased cooling by increasing the slope of the J_cold-vs.-T curve (dashed curve). At the instant of the perturbation, J_cold is now substantially greater than J_heat at 22°C. As in the previous example, J_heat – J_cold is now a relatively large negative number, so that temperature begins to fall rather rapidly. Eventually, the falling temperature brings J_heat and J_cold into balance at the same new, lower, steady-state temperature as in the previous example. The solid and dashed curves in Fig. 4B are exactly the same curves that described the time course of temperature when we inhibited the furnace in Fig. 3B.

View larger version (49K): [in this window] [in a new window]   Fig. 4. Response to a sudden increase in the rate of heat withdrawal. A: dependence of Jheat and Jcold on temperature. The increase in nonregulatory chronic cold loading is represented by an increase in the slope of the Jcold-vs.-temperature relationship (dashed line). B: time course of temperature. Note that the final temperature in this example (17°C) is the same as in Fig. 3, although the thermal insults were quite different. Thus, neither the change in steady-state temperature, nor even the time course of the temperature change, provide specific information about the underlying mechanism of the thermal insult.

Response to compound perturbations. Imagine that it is a gorgeous late summer’s day with an outside temperature of 28°C. All the windows of the house are open. While you are on a walk, a cold front moves in and lowers the outside temperature to 0°C. You rush home and close the windows, but the interior temperature has already fallen to 4°C. The temperature-regulating system senses the acute cold load (step 1a in Fig. 5A). Simultaneously, the system’s coordinating center recognizes that low outside temperature has rotated upward the J_cold-vs.-T curve (dashed curve and step 1b in Fig. 5A). Nevertheless, at the temperature of 4°C, J_heat greatly exceeds J_cold, so that Eq. 6 predicts that temperature should rise rapidly (step 2 in Fig. 5B). Of course, as the temperature rises, J_heat gradually falls and J_cold gradually rises (steps 3a and 3b in Fig. 5B) until the two eventually come into balance, not at the initial steady-state temperature of 22°C, but at the new steady-state temperature of 17°C, as dictated by the intersection of the J_heat-vs.-T curve and the newly rotated J_cold-vs.-T curve.

View larger version (27K): [in this window] [in a new window]   Fig. 5. Response to a compound perturbation: an acute cold load and a chronic increase in Jcold. A: dependence of Jheat and Jcold on temperature. In this example, we apply two thermal insults simultaneously, a blast of cold air (step 1a) and an increase in nonregulatory chronic cold loading (dashed line). Thus the compound perturbation is a combination of the insults in Fig. 2, A and B, and in Fig. 4. B: changes in Jheat and Jcold. Immediately after the increase in Jcold, Jcold greatly exceeds Jheat (step 2), so that temperature initially recovers rapidly. As temperature rises, the difference between Jheat and Jcold decreases (steps 3a and 3b), until the two values eventually come into balance at a temperature of 17°C. C: time course of temperature. At any instant in time, the rate of the temperature recovery is proportional to the difference (Jheat – Jcold). If the heat capacity had been doubled in this example, the magnitude of the initial decrease temperature would have been only half as great as shown in the figure. However, the final steady-state temperature would be the same.

Similar compound perturbations commonly occur in the world of pH_i regulation. As an example, consider respiratory acidosis, in which a rise in extracellular [CO₂] causes extracellular pH to fall. The rise in [CO₂] leads to an influx of CO₂ and thus an acute intracellular acid load. The fall in extracellular pH_o inhibits acid extruders and stimulates acid loaders and thus causes a chronic intracellular acid load.

One way to quantify the ability of our temperature-control system to maintain a stable steady-state interior temperature in the face of an altered exterior temperature would be to define a parameter called temperature stability or T_i/T_o—the change in steady-state interior temperature for an imposed change in exterior temperature. Later, we will discuss a similar definition in the domain of acid-base physiology.

An important lesson that we learned from Figs. 3–5 is that an isolated change in kinetics that affects the value of either J_heat or J_cold at the original steady-state temperature will always cause the steady-state temperature to change. We could draw a comparable conclusion concerning steady-state pH_i. Of course, it is possible for coordinated change in both J_heat and J_cold to leave the steady-state temperature unaltered. In this case, the intersection of the old J_heat-vs.-T and old J_cold-vs.-T curves would occur at the same temperature (but not the same energy flux) as the intersection of the new J_heat-vs.-T and new J_cold-vs.-T curves—as we shall see in the next section.

Environmental Sensors

The experiment in Fig. 5 revealed a flaw in our temperature-control system: environmental changes may lead to a change in steady-state temperature. We might compensate for the flaw by introducing one or more environmental sensors that warn the coordinating center of impending changes in the unregulated components of J_heat and/or J_cold.

Environmental temperature. The most obviously useful environmental sensor might be one that detects changes in exterior temperature (T_o). For example, we could program our temperature-regulating system to respond to a decrease in T_o by increasing the output of the furnace and/or decreasing the output of the air conditioner. Such a T_o-dependent shift in the temperature-sensitivity of a furnace could counteract the shift in steady-state temperature that we saw in Fig. 5C. However, we will underplay this type of sensor inasmuch as there is presently no evidence that the analogous pH_o sensors play a role in coordinating pH_i regulation. Sensors for pH_o clearly exist. For example, vascular smooth muscle cells dilate in response specifically to a fall in pH_o (2, 3). In sensory neurons the acid sensing ionic channel, which is primarily permeable to Na⁺, mediates a fast-rising and desensitizing inward current when pH_o falls below 6.9 (25, 46). Other examples of pH_o-sensitive channels are the tandem pore-domain acid-sensitive K⁺ channels, which are inhibited by decreases in both pH_o and pH_i below values of 7.3, and the human pancreatic tandem-pore K⁺ channel, which is stimulated at pH_o values >7.4 and inhibited by pH_o values <7.4 (22, 26, 30).

Snow sensors. Whereas a T_o sensor would provide direct information about a key parameter that directly influences unregulated J_heat and J_cold, a snow sensor would provide information about a parameter that is associated with increased passive heat loss. We might program the coordinating center to respond to the appearance of snow by increasing the output of the furnace over a range of temperatures, and by decreasing the output of the air conditioner, as shown in Fig. 6A. With these modifications, the system might appropriately detect the approach of a blizzard and prevent the house temperature from falling. As we will see later, an analogous sensor in cells might detect extracellular CO₂ and signal certain cells to stimulate acid-base transporters.

View larger version (31K): [in this window] [in a new window]   Fig. 6. Environmental sensors. A: a snow sensor. In this example, a detector sensing snow stimulates the furnace but inhibits the air conditioner. B: a sun sensor. In this example, a detector sensing an increase in sunlight inhibits the furnace but stimulates the air conditioner. C: hypothetical response of the overall Jheat-vs.-temperature and Jcold-vs.-temperature relationships to altered environmental cues. For example, although a blizzard might raise Jcold, the environmental sensors might trigger a compensatory increase in Jheat so that the steady-state temperature would remain unaffected.

Integrated responses to environmental sensors. Of course, we might wish to provide additional data concerning such relevant environmental parameters as wind speed. Even more important, we might wish to program the coordinating center to integrate appropriately information on all of the environmental parameters so that it would respond intelligently to a mixed message–such as those provided by bright sunlight when theexterior temperature is –30°C. In the ideal situation, our temperature-control system would respond to varying environmental cues by fine tuning the J_heat-vs.-T and J_cold-vs.-T relationships so that they always intersected at the target temperature of 22°C (Fig. 6C). As we will see later, when certain neurons are challenged by metabolic acidosis–a fall in extracellular pH caused by a decrease in the extracellular HCO₃^– concentration at a fixed level of CO₂–they are somehow able to maintain a near-stable pH_i.

Overview of pH_i Homeostasis

The foregoing discussion, in which we treated the problem of pH_i regulation as if it were a problem in temperature regulation, is very likely to be true a century from now. The discussion that follows, in which we examine pH_i regulation per se, necessarily will be less complete and more tentative. It will be less complete because cells use many different transporters as the analogs of furnaces and air conditions, and because different cell types make use of different subsets of these transporters. Our pH_i discussion will be more tentative than our temperature discussion because I am not aware of a single cell type for which, as of this writing, we know at the molecular level just which transporters make which contribution to pH_i homeostasis. Moreover, we are only beginning to understand the regulation of individual transporters and the interactions among them. Thus much of what one might write today about the details of pH_i regulation will be outdated a year or a decade from now. However, our discussion of temperature regulation has laid the groundwork for understanding the fundamental concepts of pH_i regulation, and these concepts will likely withstand the test of time.

Thus far we mentioned the word "pH"—and associated acid-base terms—only parenthetically. Nevertheless, if you understand the concepts that we have developed in our discussion of temperature regulation, then you understand the principles necessary for being a midlevel expert in the field of pH_i regulation. You merely must change the names of the terms we have been using: replace temperature with pH, heat capacity with buffering power, cold loading with acid loading, furnace with acid extruder, and so on.

pH is defined as the negative logarithm (base 10) of the H⁺ activity (a parameter that measures the effect that the ion produces, which is usually less than its actual concentration). If we make the simplifying assumption that the H⁺ concentration is the same as activity

(7)

The pH scale is a scale of ratios. Because the log₁₀ of 10 is 1 and the log₁₀ of 2 is 0.3, tenfold changes in [H⁺] correspond to pH changes of 1 and twofold changes in [H⁺] correspond to pH changes of 0.3.

Buffering (Heat Capacity)

Buffers. According to Brønsted’s definition, an acid is any substance that is capable of donating a proton. In the general sense

(8)

where n is the valence. In this example, HB⁽ⁿ⁺¹⁾ is a weak acid, and B⁽ⁿ⁾ is its conjugate weak base.¹ Viewed differently, B⁽ⁿ⁾ is a weak base, and HB⁽ⁿ⁺¹⁾ is its conjugate weak acid. Other common examples of weak acids include ammonium (NH₄⁺), carbonic acid (H₂CO₃), and monobasic inorganic phosphate (H₂PO₄^–):

(9)

The corresponding conjugate weak bases are NH₃, HCO₃^–, and dibasic inorganic phosphate HPO₄⁼. For each of these buffers, the equilibrium is described by an equation of the form:

(10)

where K is the equilibrium constant. In its logarithmic form, the relationship becomes:

(11)

where pK is –log₁₀K. Thus when pH = pK (i.e., when [H⁺] = K), [B⁽ⁿ⁾] = [HB⁽ⁿ⁺¹⁾].

We could write a similar equation for the CO₂/HCO₃^– buffer system:

(12)

The concentration of dissolved CO₂ is given by Henry’s law,

(13)

where s is the solubility of CO₂ (in mM/mmHg) and PCO₂ is the partial pressure of CO₂ in the gas phase with which the aqueous solution is in equilibrium. Combining Eq. 12 and Eq. 13 yields the Henderson-Hasselbalch equation:

(14)

Buffering power. Together, a weak acid and its conjugate weak base constitute a buffer pair. That is, the members of the buffer pair are capable of reversibly releasing or binding a proton, and thereby acting as a buffer. Buffering power (ß) is defined as the amount of strong base (e.g., NaOH)—or the negative of the amount of strong acid (e.g., HCl)—that one would have to add to a liter of solution to raise the pH of the solution by 1 pH unit:

(15)

The amount of strong acid or strong base is generally given in millimoles. Strictly speaking, pH is unitless, so that it is correct to give buffering power in units of mM. However, authors often explicitly include pH: mM/pH unit. This designation has the advantage of making it easier to keep track of units in complex calculations.

Buffering in a closed system. As it happens, buffering power varies with pH. How might we measure the pH dependence of buffering power? In this example, we will work with a closed-system buffer, that is, one in which neither member of the buffer pair can escape from (or be added to) the beaker either by equilibrating with the atmosphere or by some biochemical or transport reaction. Inorganic phosphate or HEPES would be such a buffer. For simplicity, let us assume that the buffer has a pK of 7. We will start with a beaker of buffer solution at a pH of, say, 4. We will now add a small amount of NaOH (i.e., Strong Base in Eq. 15). The added OH^– will have two fates: (1) Some will be neutralized (i.e., buffered) as HB⁽ⁿ⁺¹⁾ dissociates into B⁽ⁿ⁾ and H⁺, and the H⁺ then combines with the OH^– to form H₂O; this sequence of reactions will result in no pH change (2). The rest of the added OH^– will equilibrate with H⁺ and H₂O and thereby raise the pH of the solution by a small amount (i.e., pH in Eq. 15), raising the pH to slightly more than 4. We then use Eq. 15 to compute the mean ß over the pH range 4 – (4 + pH). We plot this computed ß in Fig. 7A at the mean pH of 4 + pH/2, and repeat the whole procedure many times as the computed ß rises to a maximum at a pH of 7 and then falls to near-zero values as we approach a pH of 10.

View larger version (31K): [in this window] [in a new window]   Fig. 7. The pH dependence of buffering power in a closed system. A: single buffer. in a closed system. Equation 17 was used to compute buffering power, assuming a pK of 7 and a total buffer concentration of 1 mM. B: multiple buffers in a closed system. The nine lower curves were each computed using Eq. 17. In each case, the total buffer concentration was 1 mM; pK values ranged from 5.0 to 9.0 in steps of 0.5. The center curve is the same as in A. The upper curve is the sum of the nine lower curves. Reproduced by permission of Elsevier (5).

(16)

If we would combine Eq. 10 and Eq. 16, and then take the partial derivative of B⁽ⁿ⁾ with respect to pH (holding the amount of total buffer constant), we would see that

(17)

Here, [TB] is [HB⁽ⁿ⁺¹⁾] + [B⁽ⁿ⁾], the total buffer concentration. If we would plot ß_closed as a function of pH, we would obtain the bell-shaped curve in Fig. 7A. The maximal ß_closed is 58% of [TB].

Buffering powers are additive, so that if we had a solution containing a mixture of many closed-system buffers, we could simply add their individual buffering powers to obtain the total buffering power of the solution. In the example of Fig. 7B, the hypothetical solution consists of nine buffers, each at a concentration of 1 mM, with their pK values evenly spaced at intervals of 0.5. Note that in the center of this pH range, the total buffering power is remarkable constant. Indeed, the total closed-system buffering power of cells is remarkably constant over a wide pH range, tending to fall gradually as pH_i rises. Thus the relative constancy of ß_closed in a cell is not terribly different from the absolute constancy of heat capacity in our house.

Buffering in an open system. Although the foregoing analysis works quite well for most buffers, it fails badly when [TB] is not constant. The most common example in which [TB] is not constant is the CO₂/HCO₃^– buffer pair, where the volatile CO₂ freely exchanges with its environment. In this case, [CO₂] is constant, but [TB] can vary over a very wide range. Open-system buffering by CO₂/HCO₃^– is extremely important in pH_i regulation because we can generally regard the extracellular solution as an infinite sink for CO₂, which can freely diffuse across most cell membranes. Thus we can generally regard [CO₂]_i as fixed.

Imagine an experiment in which we begin with a beaker that contains one liter of an aqueous solution that—in terms of CO₂/HCO₃^– and pH—mimics mammalian blood plasma (Fig. 8). However, the beaker contains no other buffers. The atmosphere has a PCO₂ of 40 mmHg, the dissolved [CO₂] is 1.2 mM (as determined by Henry’s law), the pH of the solution is 7.4, and [HCO₃^–] is 24 mM. We now add 10 mmol of HCl to the beaker (two panels at top right). Nearly all of the added 10 mmol of H⁺ are neutralized as they combine with nearly 10 mmol of HCO₃^– to form nearly 10 mmol of H₂CO₃, which in turn forms nearly 10 mmol of CO₂. The excess CO₂ evolves into the atmosphere so that the final [CO₂] is the same as the initial [CO₂]. Because the buffering reaction is not limited by the buildup of the ultimate reaction product (i.e., CO₂), buffering is quite effective, and pH falls by only 0.23 (i.e., from 7.40 to 7.17). If we had instead performed this same experiment in a closed bottle, the newly formed CO₂ would not have been able to escape the aqueous solution, the reaction product would have limited the extent of the overall buffer reaction, and pH would have fallen by a substantially greater amount.

View larger version (46K): [in this window] [in a new window]   Fig. 8. Buffering in an open system. The beaker of the left (step 1), which contains 1 liter of solution having a pH of 7.4, represents the starting conditions. The two beakers at the top represent the effect of adding 10 mM of HCl. As HCO3– neutralizes the added H+ (step 2A), [HCO3–] falls and [CO2] rises. The latter leads to the net diffusion of CO2 from the beaker. After nearly all of the added 10 millimoles of H+ have been buffered by HCO3–, the system is once again in equilibrium (step 3A), with a pH of 7.17 and a [HCO3–] of 14 mM. The two beakers at the bottom represent the effect of adding 10 millimoles of NaOH. As CO2 and H2O form HCO3– and H+, the newly formed H+ can neutralize the added OH– (step 2B), forming H2O. As a result [HCO3–] rises and [CO2] falls. The latter leads to the net diffusion of CO2 from the atmosphere into the beaker. After nearly all of the added 10 mM of OH– have been buffered by CO2, the system is once again in equilibrium, with a pH of 7.55 and a [HCO3–] of 34 mM. Reproduced by permission of Elsevier (5).

If we were to repeat the experiments in Fig. 8, but add much smaller amounts of HCl or NaOH, we could compute the pH dependence of the CO₂/HCO₃^– buffer system over a wide pH range. The steadily rising curve in Fig. 9 shows the result of such series of hypothetical experiments. If we would combine Eq. 10 and Eq. 16, and then take the partial derivative B⁽ⁿ⁾ with respect to pH, holding [CO₂] = [HB⁽ⁿ⁺¹⁾] constant, we would see that

(18)

View larger version (49K): [in this window] [in a new window]   Fig. 9. The pH dependence of buffering power in an open system. Equation 18 was used to compute the open-system buffering power, assuming a pK of 6.1 and a [CO2] of 1.2 mM. Equation 17 was used to the compute closed-system buffering power, assuming that the system was closed at a pH of 7.4, when [HCO3–] was 24 mM and [CO2] was 1.2 mM. Reproduced by permission of Elsevier (5).

The flattened, bell-shaped curve in Fig. 9 shows what the buffering power of the CO₂/HCO₃^– system would be if we put the starting solution from Fig. 8 into a closed glass bottle. The ß_closed for CO₂/HCO₃^– is maximal at the overall pK of the CO₂/HCO₃^– equilibrium, 6.1.

Total buffering power in a cell. The buffering power that is most relevant for a cell is that of the cytosol–the compartment in direct contact with the plasma membrane and most other membrane–enclosed cellular compartments. The total intracellular buffering power is the sum of the individual buffering powers of all cytosolic buffers, regardless of whether they behave as closed- or open-system buffers. These cytosolic buffers generally can be divided into two classes. The intrinsic buffers are the closed system buffers, the aggregate buffering power of which one would measure experimentally (36). The extrinsic buffers are the open-system buffers, like the CO₂/HCO₃^– buffer pair, that one has the option of adding to the system. In principle, these extrinsic buffers could be any buffer pair in which one of the members could readily cross the cell membrane. Examples might include butyrate/butyric acid (the neutral weak acid is often permeant) and NH₃/NH₄⁺ (the neutral NH₃ is generally permeant). One computes the buffering power of these open-system buffers using an expression analogous to Eq. 18. In mammalian cells (exposed to a PCO₂ of 40 mmHg), the open-system CO₂/HCO₃^– buffering power accounts for one-half to two-thirds of the total buffering power at the resting pH_i. Of course, the total intracellular buffering power, as well as the contribution of CO₂/HCO₃^–, increase at higher pH_i values.

Perturbations: Acute and Chronic Acid and Alkali (Heat/Cold) Loads

Acute acid and alkali loads: microinjection. In the laboratory, one can acutely acid load a cell by injecting or iontophoresing H⁺ into the cytosol, as was pioneered by Roger Thomas (42). Figure 10A illustrates a hypothetical experiment in which we microinject HCl into a cell. For the sake of simplicity, we will assume that the cell is bathed in a solution that does not contain any CO₂/HCO₃^–. Thus only closed-system non-HCO₃^– buffers will neutralize the injected H⁺. The minute amount of injected H⁺ that does not react with B⁽ⁿ⁾ causes intracellular pH to fall.

View larger version (43K): [in this window] [in a new window]   Fig. 10. Acute acid and alkali loads produced by microinjection. A: microinjection of HCl. B(n) and HB(n+1) represent non-HCO3– buffers. If CO2 and HCO3– were also present, then HCO3– would compete with B(n) for the injected H+ according to the reactions in Fig. 8/step 2A. In this case, the total buffering power would be greater, and the intracellular pH (pHi) decrease would be smaller. The bottom panel shows that, in the absence of acid-base transporters, pHi would be stable after its initial decline. B: microinjection of KOH. If, in addition to the non-HCO3– buffer, CO2 and HCO3– were also present, then CO2 would compete with HB(n+1) for the injected OH– according to the reactions in Fig. 8/step 2B.

Acute acid and alkali loads: permeant weak acids and bases. Although the hypothetical experiments in Fig. 10 nicely illustrate the concept of imposing an acute acid or alkali load, microinjections are only possible in a laboratory setting and require not only a skilled experimenter but a large cell. As illustrated in Fig. 11A, a far easier way to acid load a cell-and one to which cells are regularly subjected in vivo-is to increase the concentration of CO₂ in the extracellular fluid (8). CO₂ crosses the plasma membranes of most cells very easily. Once in the cytosol, the CO₂ can combine with H₂O to form H₂CO₃, most of which (but not all) then very slowly dissociates to form HCO₃^– and H⁺. Most cells contain abundant carbonic anhydrase, an enzyme that in effect catalyzes the conversion of CO₂ + H₂O to H₂CO₃ and thus accelerates the acidification of the cell. Almost all the H⁺ formed in this reaction sequence is buffered by non-HCO₃^– buffers. Note that the CO₂/HCO₃^– buffer pair cannot buffer the H⁺ produced by the influx of CO₂. In this experiment, we are assuming that the cell has no pH_i regulatory mechanism. Thus the influx of CO₂ causes pH_i to fall and then level off once [CO₂]_i has risen to the level of [CO₂]_o and the net influx of CO₂ has stopped. When we remove the CO₂, pH_i returns to its initial value. Although we used CO₂ as the permeant weak acid in this experiment, we could have used any permeant weak acid (e.g., acetic acid, butyric acid) to acid load the cell. The extent of the pH_i decline in such an experiment depends on the intracellular buffering power (greater values of ß reduce the magnitude of the pH_i decrease), the initial extracellular concentration of the weak acid, and the relationship between pH_i and the pK of the buffer (the higher the pH_i, and the lower the pK, the greater the net acidification).

View larger version (44K): [in this window] [in a new window]   Fig. 11. Acute acid and alkali loads produced by exposing the cell to a permeant weak acid or weak base. A: exposure to a permeant weak acid. In this case, CO2 crosses the plasma membrane, ultimately producing H+. Non-HCO3– buffers neutralize almost all of the newly produced H+, thereby minimizing the pHi decrease. The bottom panel shows that, in the absence of acid-base transporters, pHi would be stable after its initial decline, and would return to precisely its original value after the removal of CO2/HCO3–. B: exposure to a permeant weak base. In this case, NH3 crosses the plasma membrane, consuming H+. Non-HCO3– buffers provide almost all of the H+ needed for the formation of NH4+, thereby minimizing the pHi increase. If CO2/HCO3– were present, CO2 would also provide H+, according to the reactions in Fig. 8/step 2B.

Once added to a cell, CO₂ and NH₃ behave as open-system buffers, and augment the total buffering power of the cell. Although the CO₂/HCO₃^– system cannot buffer the protons produced as CO₂ enters the cell in Fig. 11A, the CO₂/HCO₃^– system could buffer the NH₃ that enters the cell in Fig. 11B. The same is true in reverse for the NH₃/NH₄⁺ buffer system.

Chronic acid and alkali loads: passive fluxes across the plasma membrane. The equilibrium (or Nernst) potential for H⁺ (E_H) is the hypothetical membrane potential at which H⁺ would be in equilibrium across the membrane. According to the laws of electrochemistry

(19)

where [H⁺]_o is the extracellular H⁺ concentration, [H⁺]_i is the intracellular [H⁺], and R, T, and F have their usual meanings. Figure 12 illustrates how we might compute E_H when the relevant values are reasonably physiological: pH_i is 7.2, pH_o is 7.4, and the transmembrane voltage (V_m) is –60 mV. Under these conditions, E_H is –12 mV. In other words, if V_m were –12 mV, no energy would be required or released to move H⁺ across the membrane in either direction. Because V_m is in fact –60 mV in this example, the voltage inside the cell is far too negative for H⁺ to be in equilibrium. As a result, protons tend to enter the cell passively down their electrochemical gradient. This influx of H⁺ is in principle never ending. As long as the gradient for H⁺ remains inward, and as long as the cell membrane remains permeable to H⁺, protons will enter the cell passively, imposing a chronic intracellular acid load.

View larger version (59K): [in this window] [in a new window]   Fig. 12. Chronic acid and alkali loads produced by passive fluxes of charged weak acids and bases across the plasma membrane. In this example, we assume that the cytosolic pH is 7.2 and that the transmembrane voltage is –60 mV.

(20)

Because V_m is more negative than ENH₄, the positively charged NH₄⁺-like H⁺-will tend to passively enter the cell. Conversely, because V_m is more negative than E_OH and EHCO₃, the negatively charged OH^– and HCO₃^– will tend to passively exit the cell. All of these movements tend to impose a chronic intracellular acid load. Harkening back to our temperature-regulation model, these passive fluxes are akin to heat escaping from our house during the winter. The lower the pH_o (the lower outside temperature), the greater the tendency for acid to accumulate inside the cell (the faster the house loses heat).

Regulation: acid extruders and loaders (furnaces and air conditioners). Just as the house in our temperature-regulation analogy had a furnace and an air conditioner to regulate temperature, cells have a host of acid-base transporters that perform analogous functions. The so-called acid extruders—which are analogous to furnaces—use energy to extrude H⁺ from the cell or to accumulate a weak base, such as HCO₃^–. Just as a furnace raises temperature, an acid extruder raises pH_i. We quantify the rate of acid extrusion (J_E), analogous to J_heat, as the rate at which acid equivalents exit the cell per volume of cell water, or per surface area of cell membrane. Because the membrane surface area is usually difficult to measure, most investigators report J_E in units such as µM/s. Experimentally, one obtains J_E as the product of the rate of pH_i recovery (dpH_i/dt) at a particular pH_i value by the total intracellular buffering power (ß) at that same pH_i. This calculation would be analogous to obtaining J_heat as the product of dT/dt (the rate of temperature change) and C (the heat capacity).

The so-called acid loaders—which are analogous to air conditioners—mediate the passive exit of weak bases—generally HCO₃^– or CO₃⁼. Just as an air conditioner lowers temperature, an acid loader lowers pH_i. The rate of acid loading (J_L) is analogous to J_cold. The analogy between air conditioners and acid loaders breaks down somewhat because, whereas air conditioners require the input of energy, acid loaders at least in theory work without the direct input of energy. It is perhaps also worth noting that certain acid-base transporters can—at least under some experimental conditions—switch their direction of net transport if the gradient driving transport reverses. This situation would be somewhat analogous to using a heat pump to, alternately, heat and cool our house.

Acid extruders. The acid extruders are listed on the left side of Fig. 13. They all require the input of energy either to extrude H⁺ from the cell, or to take up HCO₃^–. The vacuolar-type H⁺ pump uses the energy of ATP hydrolysis to extrude H⁺ from the cell, whereas the Na/H exchanger uses the energy of the Na⁺ gradient to extrude the H⁺. The Na/H exchanger was first studied at the cellular level by Murer and colleagues (27), and the first Na/H exchanger was cloned by Pouysségur and colleagues (39). For an overview of the Na/H exchangers (the SLC9 family of transporters), consult the review by Orlowski and Grinstein (28). Several Na⁺-driven HCO₃^– (or CO₃⁼) transporters also are acid extruders. The Na⁺-driven Cl-HCO₃^– exchanger was first discovered by Thomas in snail neurons (41, 43), and by Russell, De Weer, Roos, and Boron in their work on squid axons (7, 8, 11, 37) and barnacle muscle fibers (4, 9, 10, 38). This was the first transporter ever to be studied as a pH_i regulator. The cDNA encoding a related transporter in Drosophila was expression cloned by Romero and colleagues (35), and the gene for the human Na⁺-driven Cl-HCO₃ exchanger was cloned by Grichtchenko et al. (20).

View larger version (52K): [in this window] [in a new window]   Fig. 13. Acid extruders and acid loaders. Steady-state pHi depends on the balance between chronic acid extruders and chronic acid loaders. Three of the chronic acid loaders—the passive entry of H+ and the passive exit of OH– and of HCO3–—are also represented in Fig. 12.

Not shown in the figure is the third known type of Na⁺-coupled HCO₃^– transporter, the electroneutral Na/HCO₃^– cotransporter, the first evidence for which was provided by Aalkjær et al. (1) The cDNA encoding this transporter was cloned by Pushkin et al. (29) and also by Choi et al. (17). The latter group actually showed that the cDNA (NBCn1) encodes an electroneutral Na/HCO₃^– cotransporter that is independent of Cl^–.

Acid loaders. The acid loaders include three nonregulatory mechanisms discussed in connection with Figs. 12–the passive entry of H⁺, and the passive exit of OH^– and of HCO₃^–. Figure 13 also shows two HCO₃^– transporters that can act as acid loaders. The Cl-HCO₃^– exchanger was the first acid-base transporter ever to be studied in any context. One isoform mediates Cl-HCO₃^– exchanger in erythrocytes. Vaughan-Jones was the first to demonstrate the role of a Cl-HCO₃^– exchanger as an acid loader in the regulation of pH_i (44, 45). The cDNA encoding the erythrocyte transporter (AE1) was cloned by Kopito et al. (24).

We have already mentioned the electrogenic Na/HCO₃^– cotransporter in its role as an acid extruder. In renal proximal tubules, the transporter operates with a Na⁺:HCO₃^– stoichiometry of 1:3. Given the membrane voltage of proximal-tubule cells, as well as [HCO₃^–] and [Na⁺] on either side of the membrane, thermodynamics dictates that the net direction of transport is outward. In the renal proximal tubule, NBCe1 plays a key role in the reabsorption of HCO₃^–. When NBCe1 is expressed in Xenopus oocytes, it exhibits a stoichiometry of 1:2. It is not yet clear why NBCe1 has a 1:3 stoichiometry in the proximal tubule. It has been suggested that a novel protein in the proximal tubule complexes with NBCe1 and thereby confers the unusual 1:3 stoichiometry (21). The Cl-HCO₃ exchangers related to AE1 and all the Na⁺-coupled HCO₃^– transporters are members of the SLC4 family of transporters. For an overview, consult the review by Romero et al. (33).

Isolating an acid extruder from other elements. In our example of temperature regulation, our house had only one furnace and one air conditioner. Even in such a simple case, it might be difficult to separate the effects of the furnace from the air conditioner because both operate over a rather large, common temperature range. The analysis would be even more challenging if the house had more than one furnace, for example. The situation in cells is indeed more challenging because the typical cell has several acid extruders and several acid loaders. The most straightforward approach for separating out the effects of any one of these would be to acutely load the cell with acid or alkali and then monitor the subsequent recovery of pH_i with and without a specific inhibitor of the transporter in question, an approach first used by Boyarsky et al. (13) to determine the pH_i dependence of the Na/H exchanger in renal mesangial cells.

Consider the model cell in Fig. 14A, which has two acid extruders (an H⁺ pump and Na/H exchanger) and two acid loaders (an H⁺ leakage pathway and the metabolic production of H⁺). This cell might very well have several HCO₃^– transporters, but we will minimize their contribution by conducting the experiment in the nominal absence of CO₂/HCO₃^–. The approach (Fig. 14B) will be to impose an acute intracellular acid load, using the ammonium prepulse technique (8). Although a detailed description of this approach is outside the scope of this review, the essence is that exposing the cell to a solution containing NH₃/NH₄⁺ causes 1) a rapid pH_i increase due to the influx of the highly permeant NH₃, followed by 2) a slower acidification due to the uptake of the weak acid NH₄⁺, as well as other acid-loading processes. When the NH₃/NH₄⁺ is removed, the rapid efflux of NH₃ causes pH_i to plummet to a value well below the initial pH_i, the undershoot reflecting the previous accumulation of NH₄⁺ and other acids during the NH₃/NH₄⁺ exposure.

View larger version (27K): [in this window] [in a new window]   Fig. 14. Recovery of a cell from an acute acid load. A: acid extruders and loaders in a model cell. B: representative experiment on a single renal mesangial cell. Throughout the experiment, pHo was 7.40 and temperature was 37°C. During the indicated period, the cell was exposed to a solution containing 20 mM NH3/NH4+. During the indicated period, the cell was exposed to 50 µM ethylisopropyleamide (EIPA) to block Na-H exchange. pHi was computed from the fluorescence of BCECF. Data in B modified from Ref. 13.

Figure 15 illustrates how we can use the data in experiments similar to that of Fig. 14B to determine the pH_i dependence of the Na-H exchanger. As described in Fig. 15, the data in Fig. 15B represents the pH_i dependence of the total acid-base flux due to Na-H exchange, including any positive or negative contribution from other processes. The data in Fig. 15C represents net acid loading by everything that is not Na-H exchange. Finally, Fig. 15D represents the pH_i dependence of Na/H exchanger. Although it had previously been thought that the Na/H exchanger turned off at the resting pH_i, these data demonstrate that–like the furnace in Figs. 1B–the transporter is still active at values above the normal resting pH_i.

The fundamental law of pH_i regulation. Just as we introduced the fundamental law of temperature regulation (Eq. 6), we can introduce the fundamental law of pH_i regulation

(21)

which, in differential format, becomes

(22)

In other words, at any instant in time, the rate of pH_i change is proportional to the difference between the acid-extrusion and acid-loading rates, and inversely proportional to the total intracellular buffering power. If J_E equals J_L, pH_i is stable.

Changes in steady-state pH_i (temperature). In our discussion of temperature regulation, we noted that the only way to change steady-state temperature is to alter the fundamental kinetics of J_heat and/or J_cold. The analogous rule is true as well for pH_i: the only way to alter steady-state pH_i is to alter the fundamental kinetics of J_E and J_L.

Effect of the ras oncogene. A particularly striking example of a shift in steady-state pH_i is seen when comparing wild-type 3T3 fibroblasts with counterparts transformed with the c-H-ras oncogene (23). Whether the cells were incubated in a CO₂/HCO₃^–-free or a CO₂/HCO₃^–-containing medium, the ras-transformed cells had a steady-state pH_i that was about a half pH unit more alkaline than the untransformed parental cells. Figure 16 summarizes an analysis in which Kaplan and Boron used an ammonium prepulse to acid load the two populations of cells that they incubated in a CO₂/HCO₃^– medium. Because the cells were in a HCO₃^–-containing medium, two acid-extrusion mechanisms were functioning: a Na-H exchanger and a Na⁺-coupled HCO₃^– transporter. This situation is analogous to having a house with two furnaces.

View larger version (39K): [in this window] [in a new window]   Fig. 16. The pHi dependence of acid-extrusion rates in NIH-3T3 fibroblasts. The solid symbols represent the parental 3T3 cells, whereas the red symbols represent cells transformed with the c-H-ras oncogene. The flux due to Na-H exchange was taken as the flux sensitive to 50 µM EIPA, whereas the flux due to HCO3– uptake was taken as the flux sensitive to 50 µM DIDS. Data modified from Ref. 23.

Effect of metabolic acidosis. It has long been known that when cells are subjected to respiratory acidosis (a fall in pH_o caused by an increase in PCO₂) or metabolic acidosis (a fall in pH_o caused by a decrease in [HCO₃^–]_o at a fixed PCO₂), pH_i falls as well (36). Conversely, it has long been known that when cells are subjected to respiratory alkalosis (a rise in pH_o caused by a decrease in PCO₂) or metabolic alkalosis (a rise in pH_o caused by an increase in [HCO₃^–]_o at a fixed PCO₂), pH_i rises as well. By analogy to our temperature model, we may regard the ratio pH_i/pH_o as an index of pH_i stability:

(23)

pH_i/pH_o is generally 30%. However, in many pH-sensitive cells, such as the glomus cells of the peripheral chemoreceptor (14), or certain neurons in pH-sensitive brain regions (31, 32), pH_i/pH_o may be twice as large.

Figure 17 summarizes data from experiments conducted by Bouyer et al. (12) on cultured hippocampal neurons, the firing rate of which exhibits little sensitivity to changes in pH_o (47). Quite surprisingly, subjecting the neurons to metabolic acidosis elicited one of two very different responses. A minority of the neurons (30%, black symbols) exhibited the expected response, a relatively large pH_i decrease that corresponds to a pH_i/pH_o of 0.60. In these cells, the large fall in steady-state pH_i presumably occurred because the decreases in pH_o and [HCO₃^–]_o caused a decrease in acid extrusion and/or an increase in acid loading. In other words, a fundamental change in the kinetics of J_E and/or J_L led to a shift in steady-state pH_i.

View larger version (43K): [in this window] [in a new window]   Fig. 17. Effect of metabolic acidosis on cultured hippocampal neurons from rat. The black points represent data from 4 neurons that had a large pHi response to metabolic acidosis, whereas the red points represent data from 5–10 neurons that had a small pHi response to metabolic acidosis. Data modified from Ref. 12.

CO₂ and HCO₃^– (environmental) sensors. A wide variety of signal-transduction systems impinge on the pH_i-regulating machinery of various cell types. The ras oncogene (see Fig. 16) is just one example. These signal-transduction systems allow acid-base transporters to respond to various hormones, growth factors and even to signals generated within the cell.

Ongoing work in our laboratory suggests that at least certain cells may have sensors that allow them to respond to changes in extracellular [CO₂] or extracellular [HCO₃^–] akin to the snow and sun sensors that we discussed in our temperature-regulation model. The key tool for beginning to elucidate these sensors is a technique that our laboratory developed for generating out-of-equilibrium (OOE) CO₂/HCO₃^– solutions (48). The OOE approach exploits the slow equilibrium CO₂ + H₂O H₂CO₃. As shown in Fig. 18, the approach is to mix two dissimilar solutions, and deliver the mixture to the cells so rapidly that the CO₂, HCO₃^–, and H⁺ in the mixture does not have time to come into equilibrium.

View larger version (57K): [in this window] [in a new window]   Fig. 18. Generating out-of-equilibrium CO2/HCO3– solutions. A: generating a "pure" CO2 solution. B: generating a "pure" HCO3– solution. In both A and B, a dual syringe pump causes the contents of two syringes to mix at the "T". Downstream from the "T", nylon mesh inside the tubing insures thorough mixing before the solutions reach the cells. After the solutions pass the cells, they are sucked away. Because the solutions are flowing very rapidly (7.0 ml/min), the solution newly created at the "T" does not have time to come into equilibrium before reaching the cells (time from mixing to cells <200 ms). Thus, one is able to control [CO2], [HCO3–], and pH independently. The values in the two boxes indicate the idealized values of pH, [CO2], and [HCO3–] at the time each of the two out-of-equilibrium solutions reaches the cells. We estimate that, by the time the "pure" CO2 solution reaches the cells, the reaction CO2 + H2O HCO3– + H+ has had time to increase [HCO3–] to 1% of the standard value of 22 mM (which, with 5% CO2, would yield a pH of 7.40). Similarly, we estimate that, by the time the "pure" HCO3– solution reaches the cells, the reaction HCO3– + H+ CO2 + H2O has had time to increase [CO2] to 0.1% of the standard value of 5% (which, with 22 mM HCO3–, would yield a pH of 7.40). Modified from Ref. 49.

In principle, this stimulation of HCO₃^– reabsorption by basolateral CO₂ could be a side effect of the fall in pH_i that occurs as one raises [CO₂]_BL. However, we were quite surprised to find that isolated changes in pH_BL have no effect on JHCO₃, even though these isolated changes in pH_BL caused marked changes in pH_i. Thus, it appears that, at least as far as JHCO₃ is concerned, the proximal-tubule cell is impervious to acute changes in pH, whether they be changes in basolateral pH or cytosolic pH. The sensitivity to changes in [CO₂]_BL must therefore reflect the ability of the cell to sense CO₂ per se. Inasmuch as other lines of evidence suggest that the tubule is insensitive to CO₂ in the lumen (15, 16), the most likely explanation is that proximal-tubule cells have some sort of mechanism for sensing CO₂ per se at or near the basolateral membrane.

In response to an increase in [CO₂]_BL, the tubule cell presumably increases the transport rate of one or more of the transporters involved in the reabsorption of HCO₃^–: a Na-H exchanger and a vacuolar-type H⁺ pump at the apical membrane (these extrude acid into the lumen, thereby titrating HCO₃^– to CO₂ and H₂O), and an electrogenic Na/HCO₃ cotransporter at the basolateral membrane (the pathway for HCO₃^– to enter the blood). Further work indicates that the ability of the tubule to respond to changes in [CO₂]_BL is blocked by nanomolar levels of certain agents that block a restricted group of tyrosine kinases (50, 51), and is also blocked by procedures that interfere with the ability of luminal angiotensin II to activate its receptor (52). Of course, it would be interesting to know the identity of the CO₂-sensor molecule, as well as to know the precise signal-transduction cascade used to stimulate the acid-base transporters.

Experiments on cultured hippocampal neurons. As noted earlier, the data represented by the red symbols in Fig. 17 indicate that most hippocampal neurons have the ability to resist the expected effects of metabolic acidosis on pH_i. However, all hippocampal neurons exhibit a large pH_i decrease in response to respiratory acidosis, and all neurons exhibit a large pH_i increase in response to both metabolic and respiratory alkalosis. A line of reasoning too tedious for the present forum suggests the neurons that have unusually small responses to metabolic acidosis do so because they are able to detect the decrease in [HCO₃^–]_o per se. Indeed, preliminary work shows that neurons resistant to metabolic acidosis have an unusual response to an OOE solution that has a normal [CO₂]_o, a normal pH_o but a low [HCO₃^–]_o: in these neurons, an isolated decrease in [HCO₃^–]_o causes pH_i to rise by 0.15.

The above result is as unexpected as observing that lowering [Ca²⁺]_o causes [Ca²⁺]_i to rise, and suggests that these neurons have a mechanism for directly sensing changes in [HCO₃^–]_o per se. These neurons come from a part of the brain well known for exhibiting long-term potentiation, a primitive form of memory. The unusual neurons evidently do not care about pH_i changes triggered by changes in [CO₂]_o. Nor do they care about increases in pH_i caused by increases in [HCO₃^–]_o. However, they seem to be carefully defending pH_i against the decrease that would otherwise result from lowering [HCO₃^–]_o. It would be very interesting to know the identity of the HCO₃^– sensor, and the signal transduction pathways it uses. Nevertheless, based on the logic that we developed in our temperature-regulation model, we can conclude that these unusual neurons must respond to decreases in [HCO₃^–]_o by stimulating acid extrusion and/or inhibiting acid loading.

In conclusion, as the reader may glean from the second half of this article, the field of pH_i regulation is quite complex, and is becoming ever more complex as we better understand the molecular mechanisms by which the acid-base transport proteins function, and learn more about how protein-protein interactions and a wide range of signal-transduction systems influence these transporters. It is indeed sometimes a challenge to integrate the effects of molecular changes in such a way that one can accurately predict the effects on pH_i regulation. At times such as these, it is useful to remember that pH_i regulation is not as complicated as it is sometimes cracked up to be. Just keep track of those furnaces and air conditioners, and you will do just fine.

GRANTS

This work was supported by National Institutes of Health Grants HD-32573, NS-18400, DK-17433, and DK-30344.

Acknowledgments

I thank Duncan Wong for helping with the figures and for other computer-related assistance. I am also grateful to Patrice Bouyer and Michelle Messier for comments on an earlier version of the manuscript.

Footnotes

¹ A weak acid or base is one that is not necessarily fully dissociated. For example, HCl is a strong acid because it is virtually fully dissociated into H⁺ and Cl^–.

Received for publication September 13, 2004. Accepted for publication September 17, 2004.

REFERENCES

1. Aalkjær C and Hughes A. Chloride and bicarbonate transport in rat resistance arteries. J Physiol 436: 57–73, 1991.[Abstract/Free Full Text]
2. Apkon M and Boron WF. Extracellular and intracellular alkalinization and the constriction of rat cerebral arterioles. J Physiol 484: 743–753, 1995.[Abstract/Free Full Text]
3. Apkon M, Weed RA, and Boron WF. Motor responses of cultured rat cerebral vascular smooth muscle cells to intra- and extracellular pH changes. Am J Physiol Heart Circ Physiol 273: H434–H445, 1997.[Abstract/Free Full Text]
4. Boron WF. Intracellular pH transients in giant barnacle muscle fibers. Am J Physiol Cell Physiol 233: C61–C73, 1977.[Free Full Text]
5. Boron WF. Acid-base physiology. In: Medical Physiology. A Cellular and Molecular Approach, edited by Boron WF and Boulpaep EL. Philadelphia, PA: Saunders, p. 633–653, 2003.
6. Boron WF and Boulpaep EL. Intracellular pH regulation in the renal proximal tubule of the salamander: basolateral HCO₃^– transport. J Gen Physiol 81: 53–94, 1983.[Abstract/Free Full Text]
7. Boron WF and De Weer P. Active proton transport stimulated by CO₂/HCO₃^– blocked by cyanide. Nature 259: 240–241, 1976.[CrossRef][Medline]
8. Boron WF and De Weer P. Intracellular pH transients in squid giant axons caused by CO₂, NH₃ and metabolic inhibitors. J Gen Physiol 67: 91–112, 1976.[Abstract/Free Full Text]
9. Boron WF, McCormick WC, and Roos A. pH regulation in barnacle muscle fibers: dependence on intracellular and extracellular pH. Am J Physiol Cell Physiol 237: C185–C193, 1979.[Abstract/Free Full Text]
10. Boron WF, McCormick WC, and Roos A. pH regulation in barnacle muscle fibers: dependence on extracellular sodium and bicarbonate. Am J Physiol Cell Physiol 240: C80–C89, 1981.[Abstract/Free Full Text]
11. Boron WF and Russell JM. Stoichiometry and ion dependencies of the intracellular-pH-regulating mechanism in squid giant axons. J Gen Physiol 81: 373–399, 1983.[Abstract/Free Full Text]
12. Bouyer P, Bradley SR, Zhao J, Wang W, Richerson GB, and Boron WF. Effect of extracellular acid-base disturbances on the intracellular pH of neurones cultured from rat medullary raphe or hippocampus. J Physiol 559: 85–101, 2004.[Abstract/Free Full Text]
13. Boyarsky G, Ganz MB, Cragoe EJ Jr, and Boron WF. Intracellular pH dependence of Na-H exchange and acid loading in quiescent and arginine vasopressin-activated mesangial cells. Proc Natl Acad Sci USA 87: 5921–5924, 1990.[Abstract/Free Full Text]
14. Buckler KJ, Vaughan-Jones RD, Peers C, Lagadic-Gossmann D, and Nye PCG. Effects of extracellular pH, PCO₂ and HCO₃^– on intracellular pH in isolated type-1 cells of the neonatal rat carotid body. J Physiol 444: 703–721, 1991.[Abstract/Free Full Text]
15. Chen LK and Boron WF. Acid extrusion in S3 segment of rabbit proximal tubule. I. Effect of bilateral CO₂/HCO₃^–. Am J Physiol Renal Fluid Electrolyte Physiol 268: F179–F192, 1995.[Abstract/Free Full Text]
16. Chen LK and Boron WF. Acid extrusion in S3 segment of rabbit proximal tubule: II. Effect of basolateral CO₂/HCO₃^–. Am J Physiol Renal Fluid Electrolyte Physiol 268: F193–F203, 1995.[Abstract/Free Full Text]
17. Choi I, Aalkjær C, Boulpaep EL, and Boron WF. An electroneutral sodium/bicarbonate cotransporter NBCn1 and associated sodium channel. Nature 405: 571–575, 2000.[CrossRef][Medline]
18. Deitmer JW and Schlue WR. An inwardly directed electrogenic sodium-bicarbonate cotransport in leech glial cells. J Physiol 411: 179–194, 1989.[Abstract/Free Full Text]
19. Gillies RJ, Martínez-Zaguilán R, Peterson EP, and Perona R. Role of intracellular pH in mammalian cell proliferation. Cell Physiol Biochem 2: 159–179, 1992.
20. Grichtchenko II, Choi I, Zhong X, Bray-Ward P, Russell JM, and Boron WF. Cloning, characterization, and chromosomal mapping of a human electroneutral Na⁺-driven Cl-HCO₃ exchanger. J Biol Chem 276: 8358–8363, 2001.[Abstract/Free Full Text]
21. Gross E, Hawkins K, Abuladze N, Pushkin A, Cotton CU, Hopfer U, and Kurtz I. The stoichiometry of the electrogenic sodium bicarbonate cotransporter NBC1 is cell-type dependent. J Physiol 531: 597–603, 2001.[Abstract/Free Full Text]
22. Han J, Kang D, and Kim D. Functional properties of four splice variants of a human pancreatic tandem-pore K⁺ channel, TALK-1. Am J Physiol Cell Physiol 285: C529–C538, 2003.[Abstract/Free Full Text]
23. Kaplan D and Boron WF. Long-term expression of c-H-ras stimulates Na-H and Na⁺-dependent Cl-HCO₃ exchange in NIH-3T3 fibroblasts. J Biol Chem 269: 4116–4124, 1994.[Abstract/Free Full Text]
24. Kopito RR and Lodish HF. Primary structure and transmembrane orientation of the murine anion exchange protein. Nature 316: 234–238, 1985.[CrossRef][Medline]
25. Krishtal OA and Pidoplichko VI. A receptor for protons in the membrane of sensory neurons may participate in nociception. Neuroscience 6: 2599–2601, 1981.[CrossRef][Web of Science][Medline]
26. Lesage F. Pharmacology of neuronal background potassium channels. Neuropharmacology 44: 1–7, 2003.[CrossRef][Web of Science][Medline]
27. Murer H, Hopfer U, and Kinne R. Sodium/proton antiport in brush-border-membrane vesicles isolated from rat small intestine and kidney. Biochem J 154: 597–604, 1976.[Web of Science][Medline]
28. Orlowski J and Grinstein S. Diversity of the mammalian sodium/proton exchanger SLC9 gene family. Pflügers Arch 447: 549–565, 2004.[CrossRef][Web of Science][Medline]
29. Pushkin A, Abuladze N, Lee I, Newman D, Hwang J, and Kurtz I. Cloning, tissue distribution, genomic organization, and functional characterization of NBC3, a new member of the sodium bicarbonate cotransporter family. J Biol Chem 274: 16569–16575, 1999.[Abstract/Free Full Text]
30. Rajan S, Wischmeyer E, Xin LG, Preisig-Müller R, Daut J, Karschin A, and Derst C. TASK-3, a novel tandem pore domain acid-sensitive K⁺ channel. An extracellular histidine as pH sensor. J Biol Chem 275: 16650–16657, 2000.[Abstract/Free Full Text]
31. Ritucci NA, Chambers-Kersh L, Dean JB, and Putnam RW. Intracellular pH regulation in neurons from chemosensitive and nonchemosensitive areas of the medulla. Am J Physiol Regul Integr Comp Physiol 275: R1152–R1163, 1998.[Abstract/Free Full Text]
32. Ritucci NA, Dean JB, and Putnam RW. Intracellular pH response to hypercapnia in neurons from chemosensitive areas of the medulla. Am J Physiol Regul Integr Comp Physiol 273: R433–R441, 1997.[Abstract/Free Full Text]
33. Romero MF, Fulton CM, and Boron WF. The SLC4 family of HCO₃^– transporters. Pflügers Arch 447: 495–509, 2004.[CrossRef][Web of Science][Medline]
34. Romero MF, Hediger MA, Boulpaep EL, and Boron WF. Expression cloning and characterization of a renal electrogenic Na⁺/HCO₃^– cotransporter. Nature 387: 409–413, 1997.[CrossRef][Medline]
35. Romero MF, Henry D, Nelson S, Harte PJ, Dillon AK, and Sciortino CM. Cloning and characterization of a Na⁺-driven anion exchanger (NDAE1). A new bicarbonate transporter. J Biol Chem 275: 24552–24559, 2000.[Abstract/Free Full Text]
36. Roos A and Boron WF. Intracellular pH. Physiol Rev 61: 296–434, 1981.[Free Full Text]
37. Russell JM and Boron WF. Role of chloride transport in regulation of intracellular pH. Nature 264: 73–74, 1976.[CrossRef][Medline]
38. Russell JM, Boron WF, and Brodwick MS. Intracellular pH and Na fluxes in barnacle muscle with evidence for reversal of the ionic mechanism of intracellular pH regulation. J Gen Physiol 82: 47–78, 1983.[Abstract/Free Full Text]
39. Sardet C, Franchi A, and Pouysségur J. Molecular cloning, primary structure, and expression of the human growth factor-activatable Na⁺/H⁺ antiporter. Cell 56: 271–280, 1989.[CrossRef][Web of Science][Medline]
40. Stubbs M, McSheehy PM, Griffiths JR, and Bashford CL. Causes and consequences of tumour acidity and implications for treatment. Mol Med Today 6: 15–19, 2000.[CrossRef][Web of Science][Medline]
41. Thomas RC. Ionic mechanism of the H⁺ pump in a snail neurone. Nature 262: 54–55, 1976.[CrossRef][Medline]
42. Thomas RC. The effect of carbon dioxide on the intracellular pH and buffering power of snail neurones. J Physiol 255: 715–735, 1976.[Abstract/Free Full Text]
43. Thomas RC. The role of bicarbonate, chloride and sodium ions in the regulation of intracellular pH in snail neurones. J Physiol 273: 317–338, 1977.[Abstract/Free Full Text]
44. Vaughan-Jones RD. Regulation of chloride in quiescent sheep-heart Purkinje fibres studied using intracellular chloride and pH-sensitive micro-electrodes. J Physiol 295: 111–137, 1979.[Abstract/Free Full Text]
45. Vaughan-Jones RD. Chloride-bicarbonate exchange in the sheep cardiac Purkinje fibre. In: Intracellular pH: Its Measurement, Regulation and Utilization in Cellular Functions, edited by Nuccitelli R. New York: Liss, 1982, p. 239–252.
46. Waldmann R, Champigny G, Bassilana F, Heurteaux C, and Lazdunski M. A proton-gated cation channel involved in acid-sensing. Nature 386: 173–177, 1997.[CrossRef][Medline]
47. Wang W and Richerson GB. Chemosensitivity of non-respiratory rat CNS neurons in tissue culture. Brain Res 860: 119–129, 2000.[CrossRef][Web of Science][Medline]
48. Zhao J, Hogan EM, Bevensee MO, and Boron WF. Out-of-equilibrium CO₂/HCO₃^– solutions and their use in characterizing a new K/HCO₃ cotransporter. Nature 374: 636–639, 1995.[CrossRef][Medline]
49. Zhao J, Zhou Y, and Boron WF. Effect of isolated removal of either basolateral HCO₃^– or basolateral CO₂ on HCO₃^– reabsorption by rabbit S2 proximal tubule. Am J Physiol Renal Physiol 285: F359–F369, 2003.[Abstract/Free Full Text]
50. Zhou Y, Bouyer P, and Boron WF. CO₂-evoked increased in HCO₃ reabsorption in the rabbit S₂ proximal tubule: Blockade by genistein (Abstract). FASEB J 17: A1221, 2003.
51. Zhou Y, Bouyer P, and Boron WF. Role of a receptor tyrosine kinase in the CO₂-induced stimulation of HCO₃ reabsorption by rabbit S2 proximal tubules (Abstract). FASEB J 18: A1016, 2004.
52. Zhou Y, Bouyer P, and Boron WF. Role of endogenously secreted angiotensin II in the CO₂-induced stimulation of HCO₃ reabsorption by rabbit S2 proximal tubules (Abstract). FASEB J 18: A1016, 2004.
53. Zhou Y, Zhao J, Bouyer P, and Boron WF. Regulation of bicarbonate reabsorption by S2 proximal tubule: evidence for a CO₂-sensor (Abstract). FASEB J 15: A142, 2001.

This article has been cited by other articles:

				T. I. Lam, P. M. Wise, and M. E. O'Donnell Cerebral microvascular endothelial cell Na/H exchange: evidence for the presence of NHE1 and NHE2 isoforms and regulation by arginine vasopressin Am J Physiol Cell Physiol, August 1, 2009; 297(2): C278 - C289. [Abstract] [Full Text] [PDF]

				P. Swietach, S. Patiar, C. T. Supuran, A. L. Harris, and R. D. Vaughan-Jones The Role of Carbonic Anhydrase 9 in Regulating Extracellular and Intracellular pH in Three-dimensional Tumor Cell Growths J. Biol. Chem., July 24, 2009; 284(30): 20299 - 20310. [Abstract] [Full Text] [PDF]

				L. Schneider, C.-M. Stock, P. Dieterich, B. H. Jensen, L. B. Pedersen, P. Satir, A. Schwab, S. T. Christensen, and S. F. Pedersen The Na+/H+ exchanger NHE1 is required for directional migration stimulated via PDGFR-{alpha} in the primary cilium J. Cell Biol., April 6, 2009; 185(1): 163 - 176. [Abstract] [Full Text] [PDF]

				N. Svichar, A. Waheed, W. S. Sly, J. C. Hennings, C. A. Hubner, and M. Chesler Carbonic Anhydrases CA4 and CA14 Both Enhance AE3-Mediated Cl--HCOFormula Exchange in Hippocampal Neurons J. Neurosci., March 11, 2009; 29(10): 3252 - 3258. [Abstract] [Full Text] [PDF]

				P F Oliveira, M Sousa, A Barros, T Moura, and A Rebelo da Costa Intracellular pH regulation in human Sertoli cells: role of membrane transporters Reproduction, February 1, 2009; 137(2): 353 - 359. [Abstract] [Full Text] [PDF]

				X. Ma, O. Shor, S. Diminshtein, L. Yu, Y. J. Im, I. Perera, A. Lomax, W. F. Boss, and N. Moran Phosphatidylinositol (4,5)Bisphosphate Inhibits K+-Efflux Channel Activity in NT1 Tobacco Cultured Cells Plant Physiology, February 1, 2009; 149(2): 1127 - 1140. [Abstract] [Full Text] [PDF]

				K. H. Ahmed and B. Pelster Ionic determinants of pH of acidic compartments under hypertonic conditions in trout hepatocytes J. Exp. Biol., October 15, 2008; 211(20): 3306 - 3314. [Abstract] [Full Text] [PDF]

				P. Swietach, S. Wigfield, P. Cobden, C. T. Supuran, A. L. Harris, and R. D. Vaughan-Jones Tumor-associated Carbonic Anhydrase 9 Spatially Coordinates Intracellular pH in Three-dimensional Multicellular Growths J. Biol. Chem., July 18, 2008; 283(29): 20473 - 20483. [Abstract] [Full Text] [PDF]

				R. J. Lee, J. M. Harlow, M. P. Limberis, J. M. Wilson, and J. K. Foskett HCO3- Secretion by Murine Nasal Submucosal Gland Serous Acinar Cells during Ca2+-stimulated Fluid Secretion J. Gen. Physiol., July 1, 2008; 132(1): 161 - 183. [Abstract] [Full Text] [PDF]

				B. Schewe, E. Schmalzlin, and B. Walz Intracellular pH homeostasis and serotonin-induced pH changes in Calliphora salivary glands: the contribution of V-ATPase and carbonic anhydrase J. Exp. Biol., March 1, 2008; 211(5): 805 - 815. [Abstract] [Full Text] [PDF]

				I. J. Lynch, A. Rudin, S.-L. Xia, L. R. Stow, G. E. Shull, I. D. Weiner, B. D. Cain, and C. S. Wingo Impaired acid secretion in cortical collecting duct intercalated cells from H-K-ATPase-deficient mice: role of HK{alpha} isoforms Am J Physiol Renal Physiol, March 1, 2008; 294(3): F621 - F627. [Abstract] [Full Text] [PDF]

				L.-M. Chen, I. Choi, G. G. Haddad, and W. F. Boron Chronic continuous hypoxia decreases the expression of SLC4A7 (NBCn1) and SLC4A10 (NCBE) in mouse brain Am J Physiol Regulatory Integrative Comp Physiol, December 1, 2007; 293(6): R2412 - R2420. [Abstract] [Full Text] [PDF]

				A. Kanaan, R. M. Douglas, S. L. Alper, W. F. Boron, and G. G. Haddad Effect of chronic elevated carbon dioxide on the expression of acid-base transporters in the neonatal and adult mouse Am J Physiol Regulatory Integrative Comp Physiol, September 1, 2007; 293(3): R1294 - R1302. [Abstract] [Full Text] [PDF]

				C. Hille and B. Walz A vacuolar-type H+-ATPase and a Na+/H+ exchanger contribute to intracellular pH regulation in cockroach salivary ducts J. Exp. Biol., April 15, 2007; 210(8): 1463 - 1471. [Abstract] [Full Text] [PDF]

				S. Balakrishnan, M. Gopalakrishnan, M. Alagesan, and E. S. Prakash What is the ultimate goal in acid-base regulation? Advan Physiol Educ, March 1, 2007; 31(1): 51 - 54. [Abstract] [Full Text] [PDF]

				J. Srivastava, D. L. Barber, and M. P. Jacobson Intracellular pH Sensors: Design Principles and Functional Significance Physiology, February 1, 2007; 22(1): 30 - 39. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (33) Citing Articles via Google Scholar Google Scholar Articles by Boron, W. F. Search for Related Content PubMed PubMed Citation Articles by Boron, W. F.