Methods xxx (2014) xxx–xxx

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What does calorimetry and thermodynamics of living cells tell us? Thomas Maskow ⇑, Sven Paufler UFZ, Helmholtz Centre for Environmental Research, Dept. Environmental Microbiology, Permoserstr. 15, D-04318 Leipzig, Germany

a r t i c l e

i n f o

Article history: Received 28 August 2014 Received in revised form 23 October 2014 Accepted 28 October 2014 Available online xxxx Keywords: Calorimetry Biothermodynamic Bioprocess control Enthalpy balances Oxycaloric equivalent Law of Hess

a b s t r a c t This article presents and compares several thermodynamic methods for the quantitative interpretation of data from calorimetric measurements. Heat generation and absorption are universal features of microbial growth and product formation as well as of cell cultures from animals, plants and insects. The heat production rate reflects metabolic changes in real time and is measurable on-line. The detection limit of commercially available calorimetric instruments can be low enough to measure the heat of 100,000 aerobically growing bacteria or of 100 myocardial cells. Heat can be monitored in reaction vessels ranging from a few nanoliters up to many cubic meters. Most important the heat flux measurement does not interfere with the biological process under investigation. The practical advantages of calorimetry include the waiver of labeling and reactants. It is further possible to assemble the thermal transducer in a protected way that reduces aging and thereby signal drifts. Calorimetry works with optically opaque solutions. All of these advantages make calorimetry an interesting method for many applications in medicine, environmental sciences, ecology, biochemistry and biotechnology, just to mention a few. However, in many cases the heat signal is merely used to monitor biological processes but only rarely to quantitatively interpret the data. Therefore, a significant proportion of the information potential of calorimetry remains unutilized. To fill this information gap and to motivate the reader using the full information potential of calorimetry, various methods for quantitative data interpretations are presented, evaluated and compared with each other. Possible errors of interpretation and limitations of quantitative data analysis are also discussed. Ó 2014 Published by Elsevier Inc.

1. Introduction Heat is an inevitable by-product of any microbial growth and product formation processes. Heat is directly related to growth stoichiometry and heat production rate to the metabolic fluxes via Law of Hess. Calorimetry measures the metabolic activity of a population of cells. By this, any metabolic change can be monitored on-line and in real time. These days, heat production rate can be measured with a specific limit of detection of 105 W L1 which corresponds to a microbial oxygen depletion of air saturated medium (approx. 6.7 mg L1 at 37 °C) within 44 days. In case of aerobic glucose combustion it relates to the tiny consumption rate of 0.03 lM h1 or 5 lg L1 h1 [1]. These data show the potential of calorimetry. A good overview about the dependency of the limit of detection on the considered reaction volume and on the applied instrument is given by Zogg et al. [2]. Calorimetry works in opaque media and doesn’t need any labeling agents. Long-lasting and ⇑ Corresponding author. Fax: +49 341 235 1351. E-mail addresses: [email protected] (T. Maskow), Sven.paufl[email protected] (S. Paufler).

elaborated optimization and design of primer, probes and hybridization conditions as known for molecular biology-based methods [3] are not required in calorimetry. Finally, thermal transducers may be, different to many other biochemical sensors, mounted in a protected location. This keeps the transducer stable for long time and reduces the signal drift. All of these advantages increase the scientific and technical demand for calorimetric measurement methods and data interpretation. As a result commercially available highly sensitive, high throughput calorimeters, able to measure up to 48 channels in parallel have been developed. Fields of application ranges from medical research [4], environmental microbiology [5–7] to the food industry [8]. Due to the development of highly sensitive calorimeters with a high throughput and their validation in different application fields the quantitative data interpretation becomes more and more important. First reports on enthalpy balances around systems of different scales which are also applicable to calorimeters, bioreactors or even ecosystems were published more than two decades ago [9,10]. The authors correlated metabolic fluxes and growth stoichiometry with heat flows via Law of Hess. However, advanced chemical analytics are indispensable for such a data analysis. Quite often comprehensive

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T. Maskow, S. Paufler / Methods xxx (2014) xxx–xxx

metabolic information are not available for interpretation of calorimetric experiments. For this, the applicability of more simple growth models assuming a constant heat production rate per cell or per converted electron were successfully tested just recently [11]. Finally, the recent tendency to simplify and to standardize calorimetric measurements leads to potential misinterpretations of calorimetric experiments [12,13]. This review relates different types of calorimetric data interpretation to the level of available information. It explains weaknesses and advantages of the evaluation methods. Also, causes of various misinterpretations of calorimetric measurements are going to be discussed. Finally, the compromise between modern trends in respect to miniaturization and high-throughput measurements and the requirement for more analytical information for a correct calorimetric data interpretation will be discussed. 2. Extracting quantitative information from the calorimetric signal

organisms is designated in the following as biomass. The elemental composition of X1 = 1.70; X2 = 0.42 and X3 = 0.25 is typical for bacterial biomass [14].

rS CHS1 OS2 þ rN NH3 þ rO2 O2 ! r X CHX1 OX2 NX3 þ r CO2 CO2 þ r H2 O H 2 O with Y i=X ¼

follows Y S=X CHS1 OS2 þ Y N=X NH3 þ Y O2 =X O2 ! CHX1 OX2 NX3 þ Y CO2 =X CO2 þ Y H2 O=X H2 O

n n X X ðY i=X Df Hi Þ ¼  ðY i=X DC Hi Þ i¼1

Every open system, independent of its size and the ability to monitor the heat production, can be considered as a type of calorimeter. A ‘‘system’’ can be a part of an ecosystem, a bioreactor, an animal or a plant but it could also be a simple medical or food sample containing active cells. Clearly defining the systems boundaries is essential for designing the experiments and later for data evaluation. Fig. 1 shows a simplified chart of such an open system exchanging energy and matter with the environment. The general balance of such a system has the structure of:

Accumulation in the system ¼ Input  Output  Chemical; biological conversions ð1Þ Every conserved quantity (e.g., elements, electrons, individual chemical and biological species as well as enthalpy) can be balanced. The usage of enthalpy balances for evaluation of calorimetric results makes most sense if information about the metabolic fluxes ri (Eq. (2)) or about the growth stoichiometric coefficients Yi/X (Eq. (3)) is available or aspired. The difference between heat and enthalpy is discussed in a later section of the article. However, the main barrier to application of fully balanced systems is the requirement of an exhaustive chemical analysis of the calorimetrically monitored process. The following example shows the simplest case of aerobic biomass formation (CHX1OX2NX3) from any organic carbon source (CHS1OS2). The cell dry mass of biological

Fig. 1. Calorimeter as an open system exchanging energy and matter with the environment. The calorimeter can incorporate parts of ecosystems, bioreactors or simple samples with living matter. The system can be in steady state or in a transient. It has to be adapted to the specific experimental conditions. Convective heat flows due to exchange of matter is not considered. The figure is derived from [9].

ð3Þ

The five unknown stoichiometric/yield coefficients Yi/X are not independent of each other. They have to fulfill four elemental balances for C, H, O and N. This means the yield coefficients can be calculated if one more conditional relation is known. This additional relation can be the enthalpy balance which is also called the Law of Hess (Eq. (4)).

DR H X ¼

2.1. Data interpretations using the first law of thermodynamics

ð2Þ

ri rX

ð4Þ

i¼1

The energy balance/Law of Hess correlates the measured growth reaction enthalpy DRHX with the energy contributions of each chemical species i involved in the growth reaction. DRHX is the enthalpy released (negative sign) or consumed (positive sign) during the formation of one mole biomass. The energy contribution of each species can be described as enthalpies of formation DfHi or of combustion DCHi (see Eq. (4)). However, the correct standard and reference state of the enthalpy of all species have to be selected and to be corrected for temperature. Von Stockar and co-workers discussed consequences of wrong usage of reference states and neglecting temperature corrections [10]. Note, the usage of combustion enthalpies simplify the enthalpy calculation (in this specific case) because the combustion enthalpies of CO2 and H2O are zero (see Eq. (5))

DR HX ¼ ðDC HX  Y S=X DC HS  Y N=X DC HN Þ

ð5Þ

The calorimetrically determined heat production rate Q_ contains additional kinetic information[15,16]. Eq. (6) shows it at the example of the growth rate rX.

Q_ ¼ r X DR HX

ð6Þ

R The growth reaction heat (DR Q ¼ Q_ dt) is equal to the growth reaction enthalpy DRH for open systems with a constant pressure (dp = 0). In case of calorimetric measurements in the often used R closed ampoules, a contribution of work ( V dp) has to be added. In case of a typical ampoule of 4 mL with 2 mL cellular suspension and 2 mL air space a pressure increase of 1 atm (101,325 Pa) provides only 0.2 J, which can often be neglected in comparison to the growth heat. Calorimetry measure the average metabolic activity of a population of cells. An in-depth data interpretation can only be done if enough analytical information is available. The calorimetric systems must be large enough for extensive sampling or to include online sensors. This is the case for experiments in a reaction calorimeter, in bioreactors combined with a flow through calorimeter [17–20], or by performing a calorimetric experiment in parallel to an equivalent experiment in a bioreactor. The latter two are error-prone due to metabolic processes in the flow through line or wall growth [21,22]. Differences between the physical environment in the calorimeter and in the bioreactor may distort the calorimetric result. An experiment must be carefully designed to minimize distortions of the calorimetric signal due to (i) the addition of substances with deviating temperatures, (ii) the heat of neutralization by keeping the pH constant, (iii) heat of evaporation due to aeration, (iv) heat effects of gas adsorption and release, and

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Fig. 2. Heat production rate (top) and relative energy distribution between the substrate (glucose) and the products (ethanol, biomass, intermediates and heat) during the time course of growth of Saccharomyces cerevisiae (bottom). The energy input (i.e., in form of glucose) was set to 100%. The dotted lines indicate sudden changes in the heat production rate due to the consumption of glucose or ethanol, respectively. The data were taken from [17].

(v) heat input by stirring of media with a potentially changing viscosity have also to be taken into account. This can be done as for instance described by von Stockar et al. and others (see for example [10,23–26]). All these are unwanted energetic flows resulting in increased noise, baseline offset and signal drift. They have a negative impact on the methods overall limit of detection and limit of quantification that are closely related to the systems signal to noise ratio.

3

concept [27]. The potential of reaction calorimetry in this context was demonstrated at the example of Saccharomyces cerevisiae shifting from the respiratory to the respire-fermentative metabolism [28] and at the example of the cleavage of aromatic ring structures in ortho- or meta position [29]. Other applications are the usage to quantify the energetic costs of metabolic adaptations. This was demonstrated at the examples of adaptation to a shortage of nutrients [30] or to stress exerted by high salt concentrations [31–34]. For instance it was found that the adaptation to salt stress using ectoine as protection molecule does not burden the metabolism (for details see [33]). Also the more complex haloadaptation process of the non-conventional yeast Debaryomyces hansenii was revealed using enthalpy balances [35]. How valuable the calorimetry in combination with enthalpy balances is, shows the following very early and simple example (Fig. 2). The energy distribution, during the growth of yeast, is quantified in this work. Important changes in the assimilation pathway from the usage of glucose to usage of ethanol are immediately indicated by two main maxima in the heat production rate. In order to solve the describing equation system for a calorimeter at least as many measured entities have to be available as the number of degrees of freedom of the system. However, sometimes even more analytical information are available than the degrees of freedom of the system. In such cases, it is said that the system is over-determined or contains redundant information. The redundancy can be used for detection of gross and systematic errors and data reconciliation. The bases for these applications were founded more as two decades ago [36–39]. Taking the simple aerobic growth (see Eq. (3)) as an example, the five independent growth yields are accompanied by four conservation equations. Therefore, the degree of freedom is one. With the measurement of the reaction heat is the system completely described and with the additional measurement of CO2 evolution, or oxygen consumption over-determined. Even first application of pure calorimetric monitoring of bioreactors was reported very early [40] and 14 years later demonstrated at industrial scale [41]. The application of redundancy analysis to calorimetrically monitored bioreactors is discussed by van der Heijden et al. [37]. In contrast to systems with redundant information is the case of the biodegradation of trace pollutants (in the lg/L range). Here it is often difficult or even impossible to follow the degradation kinetics of pollutants or the formation of biomass or intermediates by chemical analysis due to the extreme small changes in concentration. The following example measured by isothermal titration calorimetry will illustrate how calorimetry can, in combination with enthalpy balances, yield new insights. The first step during the biodegradation of the herbicide 2,4-Dichlorphenoxybutyrate (2,4-DB)

ð7Þ

In fundamental research fully balanced calorimetric experiments can be applied to detect shifts in metabolic pathways, because a given pathway is always connected to a certain growth stoichiometry. The growth stoichiometry in turn can be calculated in advance by knowledge of the respective pathway using the YATP

is an enzymatically catalyzed cleavage of the ether bond (Eq. (7)). The question arises what happens with the cleavage products. For clarifying that question a tiny amount of 2,4-DB was added to a bacterial suspension and the degradation monitored calorimetrically resulting in a reaction heat of 1149 ± 16 kJ/mol

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2,4-DB. This measured reaction heat was compared with calculated reaction heats from enthalpy balances assuming different fates of 2,4-DB (Fig. 3). The comparison suggested that the metabolic end products are dichlorophenol (DCP) and biomass [42]. The accumulation of DCP during biodegradation of 2,4-DB has already been reported [43], thus supporting this interpretation. Taking this as an example the potential fate of biodegradation of other pollutants can also be revealed. 2.2. Data interpretations using a constant energy per converted electron In the most common form of calorimetry (i.e., microcalorimetry) but also with the newly emerging miniaturized or chip-calorimeter it is difficult to fully characterize the biological process. Furthermore, for many applications of calorimetry in medicine, pharmacy, food industry, and environmental sciences is the real time detection of metabolic activity sufficient. If the calorimetric signal has to be interpreted without additional information, it is a good idea to assume a constant heat production per converted electron. If this assumption is correct, then the heat signal reflects mainly the catabolic part of the metabolism because most electrons are converted via electron transport phosphorylation. The concept of a constant heat per converted electron goes back to Thornton [44] who first described a linear relation of the combustion enthalpy of multiple organic compounds DCHi versus degree of reductance ci (Eq. (8)). Such a relation (in the following called Thornton-Rule) was confirmed by many other authors [9,45–48].

DC Hi ¼ ci DC He þ di

with ci ¼ 4 þ S1  S2

ð8Þ

DCHe, di describe the heat related with the conversion of one mole electrons and the deviation between the linear relation and the actual combustion enthalpy of the chemical compound i respectively. ci in Eq. (8) is related to one carbon mole (C-mol) of the compound i having the composition of CHS1OS2NS3. For instance one mole of ethanol (C2H6O) corresponds to two carbon mole of ethanol (CH3O0.5). From this linear relation and depending on the applied data base a heat per mole converted electrons DCHe between (107  120) kJ e-mol1 can be derived [14,44]. Therefore, another exciting question can be answered. The heat Q released per mol oxygen respired r O2 is ((107  120) kJ/emol  4 e-mol/mol-O2 = (428  480) kJ/mol-O2). That value is

Fig. 3. Comparison of the measured reaction enthalpy of the biodegradation of the herbicide 2,4-DB by Rhodococcus erythropolis K2–3 with enthalpy balance calculations assuming different fate of 2,4-DB. Complete oxidation (A); Incomplete oxidation (B: DCP is remaining and succinate is combusted; C: DCP is remaining and succinate is used to produce biomass; D: DCP and succinate is remaining). The data were taken from [42].

called oxycaloric equivalent and usually attains values between 430 and 480 kJ/mol-O2 with an average value of (455 ± 15) kJ/mol-O2 [49]. Since the calorimetry in this simple picture reflects the catabolic side of metabolism, it is interesting to compare the calorimetry with methods, which show the other sides of the metabolism. For instance, the comparison with microscopy (measure of biovolume), the counting of colony forming units (measure of fertility), fluorescence microscopy (live/dead distinction), ATP content (energy charge of cells) has the potential to deliver mechanistic information about the metabolic target of biocides or antibiotics [7,50,51]. This can be applied for instance to characterize toxic properties of chemicals, the action of antibiotics or of biological agents. Even more interesting for calorimetric data interpretation are deviations in Q_ =r O2 from the oxycaloric equivalent. Three different cases can be distinguished. First, if peroxides or other reactive oxygen species (ROS) are formed, the oxycaloric equivalent is violated. For example, Oroszi measured the photo-synthetically absorbed heat of the diatom Phaeodactylum tricornutum and compared it with oxygen production at different irradiances. He observed strong deviations from oxycaloric equivalent and assigned this to photosynthetic protection mechanisms that may be associated with the synthesis of ROS or peroxides [52]. Second, and of more practical importance are deviations that are caused by either partially anaerobic metabolism or anaerobic zones in the considered bioreactor or ecosystem. Fig. 4 illustrates the effect of anaerobiosis taking glucose as substrate, ignoring anabolic reactions and assuming three different types of anaerobic catabolic reactions (A – alcoholic fermentation, B – lactic acid fermentation, C – homoacetic acid fermentation). Fig. 4 shows clearly that a small percentage of anaerobiosis (caused by anaerobic zones in a bioreactor or ecosystem or by mixed fermentative metabolism) has only a little influence on the Q_ =rO2 . This influence increases with increasing ratio of anaerobiosis and approaches theoretically to infinity for 100% anaerobiosis. Indeed such deviations are reported by a few publications (e.g., [17]) reaching values of 11,000 kJ/mol-O2, which have been attributed to lactic acid formation under anoxic conditions by the authors [53]. The third reason for potential deviations from the oxycaloric equivalent of the total system could be deviations in

Fig. 4. Influence of anaerobiosis on deviation from the oxycaloric equivalent. 460 kJ mol1 was assumed as value for the oxycaloric equivalent. The graph shows the percentage deviations from this value. The thermodynamic base data were taken from [10].

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Thornton-Rule for educts consumed or products formed during the bioconversion. As an example, the ratio Q_ =r O2 provides theoretically a real time measure for the biomass related yield coefficient YX/S (Eq. (9)).

4½dS  Y X=S ðdX  X 3 dN Þ Q_ =r O2 ¼ 4DC He þ cS  Y X=S ðcX  3X 3 Þ

Table 1 Limits of detection for the cell concentration as a function of the cell specific heat production rate Q_ C . A typical Q_ D ¼ 100 nW (limit of detection of the applied calorimeter) and V = 2 mL (sample size) were assumed. Note, these values provide just a raw picture as they obviously depend on the respective growth conditions. Data were taken from [1,87].

ð9Þ

Cell type

dX, dS and dN describe the deviation of the biomass, substrate and ammonia as nitrogen source from the Thornton-Rule. The calculated typical dX and dS are less than 10% based on published values [10]. dN is approximately 2% taking the data of [54]. The total effect of these deviations on Q_ =rO2 is less than 10%. Indeed, the observed Q_ =r O2 ratio for complete aerobic growth without product formation vary between 385 and 495 kJ/mol-O2 [55]. This means that for exploiting the relation in Eq. (9), the Q_ as well as rO2 have to be measured very accurately. However, Regestein and co-worker overcame that problem and showed in 2013 that deviations from the oxycaloric equivalent can be applied to monitor biotechnological lysine formation in real time in a bioreactor [56]. 2.3. Data interpretations using a constant cell specific heat production rate In medicine, pharmacy, food industry and water management, the number of microorganisms is often more important than the exact knowledge of their metabolic activities. This is also the case for many toxicological studies in environmental sciences. The simplest and most applied assumption in these cases is a constant cell specific heat production rate Q_ C . We will later discuss how to get and how to apply a more realistic estimation of Q_ C . But, related to the number of cells, how sensitive can calorimetric measurements be? A modern commercially available isothermal calorimeter achieves a detection limit of Q_ D ¼ 100 nW (see for instance: www.tainstruments.com or [4]). With Q_ C a relation between the measured heat signal Q_ , the cell concentration X and the sample volume V can be written (Eq. (10)).

Q_ ¼ X Q_ C V

ð10Þ

Putting Q_ D in Eq. (10) allows the calculation of the detection limit for the cell concentration for a typical sample volume in dependency on Q_ C (Table 1). The cell concentration X in Eq. (10) is not constant. Its increase can be described using the SKIP (sum kinetics with interaction parameter) model [57–59] (Eq. (11)).

dX ¼ lX dt

with

l¼

M X i¼1

lmax;i Si K S;i þ Si þ

XM

ð11Þ

I S j¼1 j;i j

Here are M the number of all nutrients considered as essential, Si the concentration of these nutrients, and KS,i, lmax,i, Ij,i growth parameters. At the beginning of the calorimetric experiment all P nutrients are sufficiently available and Si  K s;i þ M j¼1 I j;i Sj . This means, that l can be considered constant for a certain time period (Eq. (12)).

Q_ ¼ X 0 Q_ C V expðltÞ

ð12Þ

But during this period of time calorimetry can be applied to determine the maximum specific heat production rate of a given cell type in a defined medium simply by plotting the logarithm of Q_ versus time t. Of course a constant cell specific heat production rate can be called into question. Based on Eq. (6), this controversy can be overcome by considering the definition of the growth rate rX as product

Condition

Q_ C (pW cell1)

Limit of detection (103 cells mL1)

Endogenous Anaerobic Aerobic Aerobic

0.05 0.2 0.8 2.5

1000 250 62 20

Aerobic

11

4.5

Yeast Schizosaccharomyces pombe

Aerobic

63

0.79

Protozoa Tetrahymena pyriformis

Aerobic

3,300

0.015

250 ± 6 2,000

0.2 0.025

Bacteria Escherichia coli Escherichia coli Escherichia coli Staphylococcus aureus Mycoplasma hominis

Mammalian cell lines Hepatozytena Aerobic Myocardial cells Aerobic a

Own measurements.

of biomass concentration X with the cell specific growth rate l (Eq. (13)).

Q_ ¼ rX DR HX ¼ lX DR HX

ð13Þ

The comparison of Eq. (13) with Eq. (12) shows a linear relation between the cell specific heat production rate Q_ C and the specific growth rate l (Eq. (14)).

Q_ C ¼ lQ C

with Q C ¼ C C DR HX

ð14Þ

The correlation factor QC can be understood as growth reaction heat (in J) per cell. For estimation of QC the knowledge of the enthalpy of growth reaction DRHX and the mean carbon content (in mole) of a single cell CC is required. A simple expression for the enthalpy change of growth reaction DRHX as function of degree of reductance of the substrate cS and the biomass cX (Eq. (14)) results from applying Eq. (14) to growth stoichiometry.

DR HX ¼ 115kJ=C-molðcX  Y S=X cS Þ

ð15Þ

Many authors (for instance, [60–64]) recognized that the yield coefficient YS/X depends on the energy content of the substrate. The energy content of the substrate depends on the relative degree of reductance cS as known from the Rule of Thornton. The simplest relation between the relative degree of reductance cS of the substrate and the yield coefficient YS/X (Eq. (16)) is described by [65].

Y S=X ¼

7:69

cS

for cS  4:67

ð16Þ

Y S=X ¼ 1:67 for cS > 4:67 The enthalpy change of the growth reaction DRHX can be calculated based there upon (Eq. (17)).

DR HX ¼ 115kJ=C-molðcX  7:69Þ for cS  4:67 DR HX ¼ 115kJ=C-molðcX  1:67cS Þ for cS > 4:67

ð17Þ

This means that for many naturally occurring substrates (e.g., carbohydrates, approx. 70% of natural amino acids, proteins, DNA and RNA) the reaction enthalpy per C-mol formed bacterial mass is independent from the applied carbon-substrate. Describing the elemental composition of biomass by CH1.70O0.42N0.25 (M = 23.99 g/C-mol) [14] approx. 440.5 kJ/C-mol results for carbon sources with a degree of reduction 64.67.

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The second unknown information in correlation factor QC is the carbon content of a single cell CC. This carbon content depends obviously on the size of the considered cell. This value can be estimated from the cell specific weight (in g) and the cell composition. Taking Escherichia coli as a typical bacterial example a mean carbon content per cell of 15  1015 mol was estimated by this way [66]. Thereof the mean cell specific growth enthalpy for bacteria of a similar size as E. coli can be estimated to be 6.6 nJ/cell. With specific growth rates between 0.1 and 1 h1 cell specific heat production rate for aerobe growing ‘‘typical’’ bacteria between 0.18 and 1.8 pW/cell can be estimated according to literature values (see Table 1).

much larger than reaction enthalpy of the conversion. However it could be argued that YEtOH/S and Y CO2 =S are not independent of each other. They have to fulfill balances of the elements and redox states. Indeed, for the alcoholic fermentation is YEtOH/S = Y CO2 =S = 2 fixed. What happens in a more complex reaction with analytical errors which fulfill elemental and redox balances? A good example might be the mixed acid fermentation of glucose to acetic (AC) and butyric (BU) acid. This reaction can be conceptually separated into two sub-reactions (Eqs. (19) and (20)).

C6 H12 O6 þ YH2 0=S H2 O ! YAC=S C 2 H3 O2 þ YCO2 =S CO2 þ YH2 =S H2 þ YH=S Hþ

ð19Þ

3. Reasons for possible misinformation Sometimes the interpretability of the calorimetric measurement reaches their limit due to peculiarities of the investigated biological system or by characteristics of the design of the calorimetric experiment. Two respective examples will be discussed in the following. 3.1. Apparent discrepancies between enthalpy values measured with high performance calorimetry and calculated from chemical analysis Nowadays, high performance reaction calorimeter can measure with a high accuracy and resolution. Depending on the experimental setup and being able to minimize undesired heat flows (see Section 2.1) reaction calorimetry can achieve a limit of detection of better than 5 mW/L and good long term baseline stability (0.2 mW/(L h)). Such performance parameters allow to monitor even low enthalpy yielding reactions (e.g., anaerobic fermentations) and to analyze them based on full enthalpy balances. However, new challenges of data interpretation may arise thereof. There is no doubt that biological growth processes have to fulfill the first law of thermodynamics. Any discrepancy between measured and calculated heat production rate based on enthalpy balances should indicate some not considered metabolic events. The applied metabolic model might be over-simplified. However, errors in chemical analysis of educts or metabolic products may bias the result. In case of aerobic growth the substrate related growth reaction enthalpy is often in the magnitude of the combustion enthalpy of the substrate. For instance, the pure biological combustion of glucose delivers 2813 kJ/mol whereas growth on glucose (assuming a typical yield coefficient of 0.4 g/g) provides 1398.1 kJ/mol. The values where calculated applying the Law of Hess (Eq. (5)), taking the already explained typical biomass composition and estimating the energy content of biomass using the Thornton-Rule. Assuming an error in the growth yield coefficient of 10% (maintaining the balances of redox and elements) caused by chemical analysis an error in growth reaction enthalpy of 141 kJ/mol or 10.1% can be calculated. This corresponds with the general experience that the heat production rate can be well predicted after knowing the relevant conversion rates. However the situation changes for anaerobic systems. The simplest example is the fermentation of glucose (Gluc) to ethanol (EtOH) and carbon dioxide (Eq. (18)).

C6 H12 O6 ! Y EtOH=S C2 H6 O þ Y CO2 =S CO2ðgÞ

C6 H12 O6 ! YBU=S C4 H2 O þ YCO2 =S CO2 þ YH2 =S H2 þ YH=S Hþ

ð20Þ

The yield coefficients of each sub-reaction are fixed by the balances of the elements and redox states to YH2 O=S = YAC/ YH2 =S = 4 (Eq. (18)) and YBU/S = YH/S = 1; S = YCO2 =S = YH/S = 2; YCO2 =S = YH2 =S = 2 (Eq. (19)). The respective reaction enthalpies are 76.6 kJ/mol (Eq. (18)) and 57.6 kJ/mol (Eq. (19)). The advantage of this approach is that analytical measurement error can be understood as a shift of the ratio of the two reactions and that thereby the elementary and redox balances are not violated. Fig. 5 shows the influence of analytical errors (5% acetate rate) on the theoretical error of the reaction enthalpy. Depending on the ratio of the formation of acetate and butyrate, a large range of enthalpies swept. Even a change from exothermic to an endothermic reaction happens at the ratio of 0.43. This switching point is a discontinuity with the consequences that a small error in chemical analysis results in infinite error in reaction enthalpy. The upper part of Fig. 5 show the error in reaction enthalpy for experimentally observed reaction ratios [67]. Even in this range causes an error of chemical analysis of 5% an error in reaction enthalpy between 7% and 20%. The chosen example demonstrates clearly that in case of anaerobic growth, high performance calorimetry may provide more accurate process information as the chemical reference analysis. The chosen example certainly simplifies the realities. But even if biomass formation is taken into account, for example, the main message of the error estimation changes with little respect (unpublished results).

with Y EtOH=S ¼ Y CO2 =S ¼ 2 ð18Þ

The reaction enthalpy is 100 kJ/mol of glucose. Already small errors in chemical analysis e.g., in EtOH production (rate) measurement, would translate into relatively large errors in calculated reaction enthalpy (e.g., if the rate is 3.7% too big then DRHgluc = 0) since combustion enthalpy values of the educts and products are

Fig. 5. Influence of 5% analytical error in acetate formation rate on the error in reaction enthalpy balance. The total reaction enthalpy as function of the ratio of formation rates (acetate or butyrate) is also shown. For better resolution the part of the graph where the most experiments are located is shown magnified in the top right corner.

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3.2. Distortions of calorimetrically derived growth parameter Distortions of the calorimetric signal due to overlapping physical signals (e.g., stirring of the sample, evaporation, neutralization, etc.) or instrumental errors (e.g., undefined heat flows, baseline variations) can complicate the calorimetric data interpretation but will be not discussed in the following. Here we will focus on distortions of the metabolic activity caused by changing physical environments. For simplicity, calorimetric measurements are often performed (semi)automatically in sealed ampoules. This measuring technique is in the following discussed. In closed ampoules any concentration is expected to change over time as result of metabolic activity. Especially, relatively large cells (e.g., fungi, yeasts or micro algae) have the tendency to sediment and to cause heterogeneity. The latter can be avoided by adjusting the density of the measurement solution to the density of the biological entity or by enhancing the viscosity of the measurement solution by adding metabolically inactive additives [12]. More serious problems arise from the oxygen consumption due to metabolic activities. In case of heat produced by aerobic metabolic activity above certain threshold (e.g., 100 J/L at 37 °C) oxygen diffusion and not the metabolism alone starts to govern the heat trace. The threshold is caused by oxygen solubility in the given medium and temperature and the oxycaloric equivalent [68]. Potential consequences of ignoring the threshold are that important parameters like the specific growth rate or the growth reaction enthalpy might be determined incorrectly [13]. Although the heat trace suggests a sequence of metabolic events (first aerobic later anaerobic) in reality a mixed respiro-fermentative metabolism occurs. The specific growth reaction rate and growth reaction enthalpy differ significantly depending on the type of metabolism. More details are given in [13].

4. Conclusions and outlook Essentially, three types of calorimetric data interpretation (enthalpy balances, constant heat per exchanged electron, and constant heat per cell) have been established during the long history of biocalorimetric research. What is applied at the end depends on the scientific question but also on the available non-calorimetric data. Beyond the formal modeling or hypothesis testing task of calorimetric experiments, exploratory data analysis should be developed to extract more information from the data. As a possible example, calorimetric data of contaminated sites could be related to soil microbiome data to better understand the relation between degradation rate, degree of mineralization and the chemical properties of the pollutants, habitat characteristics and microbiome composition. As statistical tools factor analysis (FA) principle compartment analysis (PCA), correspondence analysis (CA), and cluster analysis are thinkable just to mention a few. First attempts to analyze antimicrobial properties [15,69–72] and interaction between different microorganisms [73,74] using calorimetry, chemometry and multivariate statistics have already been reported. We would expect an essential contribution of calorimetry to the further understanding of the performances of microbes in ecosystems, human gut, and bioreactors if the calorimetric data are combined with omic-technologies using models or even multivariate statistics. Despite their potential, further problems arise from the recent trend of calorimeter miniaturization. The smaller the volume of the calorimetric chamber, the more limited is the sampling. A solution is the so-called lab-on-a-chip (LOC) which integrates one or several laboratory functions on a single chip of only millimeters to a few square centimeters in size. LOCs and their further development into Micro Total Analysis Systems (lTAS) aim to integrate the

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total sequence of lab processes to perform biochemical and microbiological analysis. Indeed several thermal micro-sensors which have the potential to be integrated into lTAS have already been developed during the past two decades [75–77]. They achieve resolutions of a few nW and are able to manipulate samples of pL [78]. Even arrays of thermal LOCs are possible [79,80]. However, for better data interpretation thermal LOC’s have to be combined with other methods (e.g., micro-sensors for different substances or impedance spectroscopy for biomass) to get real calorimetrically based lTAS. The other two promising ways to combine calorimetric signals with elevated process analysis is performing the experiments in optimized bioreactors where an energy balance is drawn around the complete reactor or inserting a calorimetric immersion probe. The first development was already initiated 25 years ago by von Stockar [81] and led to the calorimetric monitoring of a bioreactor in the m3 scale [82]. Recent developments achieve resolutions of up to 20 mW/L [24] and are applicable to reactors of elevated pressure [83]. However, this sensitivity is not good enough if, for example, anaerobic bioprocesses have to be analyzed or even controlled. Thus, methods have to be developed to increase sensitivity, accuracy and stability of calorimetrically monitored bioreactors. A first step in this direction using an improved thermal shield was just recently published [84]. A calorimetric immersion probe was suggested for the first time in 1975 [85] and in 2012 developed on the basis of chip-calorimetry [86]. The latter was sensitive enough for low cell concentrations but failed at high cell densities due to technical limitations. The development of a calorimetric immersion probes for microbial suspensions of large activity range is still an open but also rewarding task. Numerous applications in bioprocess engineering are possible. In general, problems also arise from the growing importance of fed-batch, continuous and perfusion cultures in biotechnology. Here, R & D work is required to reduce distortions of the calorimetric signal due to convective heat flow and to mathematically model the remaining effects (e.g., via multiphysics simulation of whole calorimetric systems). Beside the development of calorimetric instruments which provide more data the developments of mathematical models that actually process those data is rewarding. For applications of calorimetry in medicine or food industries which often trust on measurement of simple closed ampoules an automated parameter fit would be helpful. These parameters would provide the number of bacterial cells at the beginning of the experiment and their growth kinetics depending on biocide or antibiotic concentration, physical parameters etc. First steps of such developments are recently published [11] and deserve further development. References [1] T. Maskow, T. Schubert, A. Wolf, F. Buchholz, L. Regestein, J. Buechs, F. Mertens, H. Harms, J. Lerchner, Appl. Microbiol. Biotechnol. 92 (2011) 55–66. [2] A. Zogg, F. Stoessel, U. Fischer, K. Hungerbühler, Thermochim. Acta 419 (2004) 1–17. [3] K. Nagarajan, K.C. Loh, Appl. Microbiol. Biotechnol. 98 (2014) 6907–6919. [4] O. Braissant, D. Wirz, B. Göpfert, A. Daniels, Sensors 10 (2010) 9369–9383. [5] X.-M. Rong, Q.-Y. Huang, D.-H. Jiang, P. Cai, W. Liang, Pedosphere 17 (2007) 137–145. [6] O. Braissant, D. Wirz, B. Goepfert, A.U. Daniels, FEMS Microbiol. Lett. 303 (2010) 1–8. [7] F. Buchholz, H. Harms, T. Maskow, Biotechnol. J. 5 (2010) 1339–1350. [8] L. Wadsö, F. Gómez Galindo, Food Control 20 (2009) 956–961. [9] S.I. Sandler, H. Orbey, Biotechnol. Bioeng. 38 (1991) 697–718. [10] U. von Stockar, L. Gustafsson, C. Larsson, I. Marison, P. Tissot, E. Gnaiger, Biochim. Biophys. Acta 1183 (1993) 221–240. [11] O. Braissant, G. Bonkat, D. Wirz, A. Bachmann, Thermochim. Acta 555 (2013) 64–71. [12] A.J. Fontana, L.D. Hansen, R.W. Breidenbach, R.S. Criddle, Thermochim. Acta 172 (1990) 105–113.

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