- Email: [email protected]
A kinetic method for estimating the biomass of microbial functional groups in soil
ELSEVIER
Journal of Microbiological Methods 24 (1996) 219-230
A kinetic method for estimating the biomass of microbial functional groups in soil Nicolai S. Panikov*, institute of Microbiology,
Maria V. Sizova
Russian Academy of Sciences, Prospect 60-let Octyabrya 712, Moscow 117 811. Russia
Received 19 Juli 1994; revised 24 January 1995; accepted 21 March 1995
Abstract
A kinetic method is presented for estimating the biomass and characterizing the in situ physiological state of microorganisms capable of carping out specific metabolic function. Fresh soil samples are incubated with a particular substrate under specified controlled conditions, the rates of substrate uptake or product formation being continuously recorded. The observed dynamic curve is approximated by a mathematical model which simulates substrate-induced microbial growth, microbial biomass at zero time being identified as one of the model’s parameters. Examples of the developed technique are presented for the following groups of soil microorganisms: aerobic heterotrophic (including prokaryotic and eukaryotic) microorganisms, chemolythotrophic bacteria (hydrogen, carboxidobacteria, nitrifying bacteria), anaerobic bacteria (methanogenic, acetogenic, sulfate- reducing, denitrifying), phototrophic microorganisms, pesticide degraders, and microbial grazers. Keywords:
Soil microbial biomass; Mathematical
simulation;
1. Introduction There
are four classes of analytical
techniques
suitable for determining microbial biomass in soils: (i) ex situ germ enumeration (plating and MPN) , (ii) direct microscopy, (iii) biochemical methods, and (iv) kinetic methods. Table 1 outlines specific advantages and shortcomings of these approaches (for details see discussion in numerous experimental papers and reviews, e.g. (Jenkinson and Powlson, 1976; Jenkinson and Ladd, 1981; Mirchink and Panikov, 1985; Ingham et al., 1991; Parkinson and Coleman, 1991; Petersen et al., 1991). Obviously, the choice of the method depends on the targets of a par* Corresponding author. Elsevier Science B.V. 0167-7012(95)00074-7
SSDI
Physiological state of microorganisms
in situ
titular study, and not one of them could be regarded as having absolute preference. It is sufficient to mention that even plating and MPN techniques (which have been subjected to most severe criticism in the last decades) remain valuable and indispensable in specific fields of research. In the last decade there has been growing interest in non-traditional approaches based on biochemical determinations (class 3, Table 1). New generations of these methods gave a strong impulse to the development of quantitative microbial ecology. However, these methods have their limitations. Firstly, they use doubtful conversion factors from measured chemical index (ATP, DNA, phospholipid or chitin content, fumigation flush, etc) to real biomass. The main
220
N.S. Panikov, M.V. Sizova I Journal of Microbioiogical
Methods 24 (1996) 219-230
Table 1 Comparison of different method for soil biomass determination Methods
What is measured
Conversion factor
Shortcomings
Advantages
Plating and MPN
Number of CFU (colony forming units) or highest positive dilution
Absent or extremely unreliable
Direct microscopy
Bacterial number and mycelial length
Derived from biovolume of measured cells, as well as assumed dry matter density of cells; tedious and unreliable procedure
Low recovery as a result of cell adhesion and clumping, occurrence of nonculturable forms and “laboratory artifacts”, variability of CFU sizes Tiresome and time consuming procedure, person-to-person variations of count efficiency, unreliable differentiation of cells from soil particles and plant debris
Biochemical
Content of unique cell component in soil (ATP, DNA, chitin, muramic acid, lipids, or whole cell material released by biocidal treatment)
Estimated from (i) chemical analysis of isolates (ii) calibration in situ in incubation experiments, (iii) comparison with standard method (e.g. direct microscopy)
Occurrence of analyzed chemical species in plant roots, animals and in extracellular milieu, wide variation of conversion factors
Kinetic
Rates of specific metabolic reactions estimated in incubation experiments with amended soils samples
Found as parameters of mathematical model describing microbial growth on added substrate. Rapid and exact computer-aided identification procedure
Ignores resting forms of microorganisms
Isolation and identification of microbes, differentiation of physiological and taxonomic groups, detection of individual populations carrying genetic markers Characterization of biomorphological structure of soil community and its spatial community and its spatial organization, detection of individual populations (in combination with DNA probes and immunofluorescence technique) High precision, open to standardization and automatization, compatibility of results obtained in different laboratories, effective in combination with isotope technique to label microbial cells in situ All technical advantage of biochemical methods (see above), exact calibration, differentiates microbial biomass into functional groups, assays physiological state of microbes in situ
reason for this is variation of the content of respective compounds in microbial cells of different species and dependence on growth conditions. Secondly, some analyzed compounds display significant accumulation in soils outside the viable cells (slow degradation after microbial death). This may lead to considerable overestimation of the real biomass. Thirdly, the bio-
chemical methods as a rule give only an integrated estimate of the total biomass, neglecting the diversity of soil microbial community. The present study deals with the fourth class of analytical techniques which belong to the socalled kinetic methods (class 4, Table 1). Our aim was to refine these methods and free them from the above-mentioned limitations.
N.S. Panikov, M.V. Sizova I Journal of Microbiological
2. Theory 2.1. The principle of kinetic methods To determine microbial biomass short-term enrichment experiments are performed, adding to soil a specified substrate, and following its uptake or product formation. The observed dynamics are approximated by a mathematical model which describes microbial growth induced by substrate addition. Identification of the model’s parameters gives an estimate of microbial biomass at zero time, i.e. just before substrate amendment. 2.2. Requirements
to incubation procedure
The following requirements should be met: (i) Added substrate has to be uniformly distributed throughout the soil (this is a sensible requirement in the case of soluble or volatile chemicals). (ii) The soil amendment should be large enough to provide zero order kinetics of substrate transformation (sO> K,), but well below the inhibitory level. (iii) Environmental parameters have to be physiologically suitable and kept constant. If all these conditions are satisfied, then the growth of responsive microbial populations should be rather simple: uniform (almost homogeneous) and unlimited. Moreover, for some time the growth should be unaffected by biological interactions such as competition, antagonism, or grazing, due to excess of substrate, catabolic repression of antibiotic’s biosynthesis and delay in the development of protozoan grazers respectively (Panikov, 1991a). Hence, a multi-species soil community temporary behaves like a single population. This gives us some freedom in the choice of simulation model. In particular we may rely on relatively simple kinetic models which have been originally developed for homogeneous batch culture. 2.3. Requirements
to the mathematical model
However, the model should not be oversimplified. Simple kinetic models of exponential growth described elsewhere (Schmidt, 1992) are
Methods 24 (19%)
219-230
221
of limited use in respect to soil. Indeed, unamended soil most frequently is extremely poor in available energy sources. Substrate amendment is expected to bring about a dramatic changes in physiological state of starving soil populations: ‘famine-to-feast’ transition is associated with a long lag-phase, changes in the composition of microbial biomass, extensive dissipation of added substrate in energy-spilling reactions, growth yield variation, etc. A kinetic model must take into account all these effects resulting in strong deviation of microbial growth from the simple exponential pattern. Recently we proposed a relatively simple structured model which was able to describe the transient dynamics of this kind (Panikov, 1991b). It was called the ‘synthetic chemostat model’ (SCM), since it includes as elements many known kinetic models of substrate limited growth. Here SCM is outlined as a following simplified version. 2.4. Account of variation in cell composition This is the key kinetic problem which is essential for the understanding of microbial adaptation to a changeable environment. According to SCM all macromolecular cell constituents are divided into two groups: Primary cell constituents absolutely necessary for growth (P-components), and components needed for cell survival under growth restriction (U-components). The characteristic examples of P-components are ribosomes or rRNA and ribosomal proteins, other RNA fractions, enzymes of the primary metabolic pathways. Their intracellular content increases parallel to increase of growth rate, e.g. total RNA content varies from l-2% of cell dry weight in starving populations up to 20% in intensively growing culture. Contrary to P-components the contribution of U- components to cell biomass decreases with growth acceleration. An examples are enzymes of the secondary metabolism, protective pigments, reserved substances, transport systems of high affinity etc. An amount of P- and U-components expressed as a fraction of total cell mass (P and U, g per g
222
N.S. Panikov, M.V. Sizova I Journal of Microbiological
of biomass respectively) vary within the upper (I’,, U,) and low (I’,,, U,,) limits. The sum of all PO and UO components comprise the constitutive cell fraction (not subjected to adaptive changes), while others represent inducible cell components. Mass conservation constrains is as follows:
iPi+&Jj=i:P”+&J;
i=l
j=l
i=l
j=l
=~P:+~up=~,i:xp,+t:x~i=x i=I
j=l
i=l
j=l
Methods 24 (1996) 219-230
4, = rQ q-$y+(l-r)Qz& 1
where the first and the second terms on the right side stand respectively for low (P-component) and high (U-component) affinity of transport system. Finally, the third rule explains how environmental factors control adaptive changes in microbial cells. Steady state r is an immediate function of s, limiting substrate concentration:
(1) where k and 1 are the numbers of assigned Pand U-components respectively. There is a great body of experimental evidence (Panikov, 1991b), that the variation of cell components is not stochastic but obeys the following rules: (1) an increase of one individual P-component is accompanied by increase of other P-components, (2) total enlargement of P-sum is accompanied by corresponding decrease of U-sum and vise versa, and (3) the P/U-ratio is controlled by limiting substrate concentration in environment. The first rule could be expressed in mathematical terms as follows: r=
P, - P; Py-P;=
P*-Pi P,-PO, p; _ pi = * ‘. = P;-Po,
(2)
where the variable I is the index of physiological state, a ratio between instant quantity of any particular P-component and its total changeable part. T varies from zero (when all P-components come down to low limits) to 1.0 (when they attain maximal values). The second rule states that P and U are complementary to each other, and the content of any U-component should be proportional to difference (1 - r). The introduction of variable r greatly simplifies the use of structured models, because the adaptive variation of cell composition (and metabolic activity which is determined by intracellular content of particular enzymes) now could be expressed via one single ‘master’ variable r. For example, the specific rate of substrate uptake q, is defined as:
2
(4) where K, is the saturation constant. Under chronic starvation s = 0 and r0. Under unlimited steady state growth when >K,, r is close to maximum r - 1. During transients caused by sudden s changes, an instant r-value approaches new steady state. The rate of change of r during transients is expected to be proportional to specific growth rate of total microbial biomass C and the difference between instant and final (steady-state) values of r (Panikov, 1991b), then:
f=p(-&-r)
(5)
Dissipative and productive consumption of energy source yield variation. Respiratory enzymes are divided into two groups in respect to both functional role and sensitivity to cyanide (Akimenko et al., 1973). CN-sensitive oxidases carry out productive substrate oxidation coupled with ATP generation and cell growth, their activity being positively correlated with growth rate and r-value (P-components). CN-resistant oxidases belong to constitutive cell components and carry out energy-spilling oxidative reactions uncoupled from ATP generation. Their probable physiological function is cell protection from surplus of catabolites (Tempest and Neijssel, 1984). Total specific respiration activity measured under excess of substrate is denoted as Q, and CN-resistant fraction as Q’, then the productive fraction of total respiration is equal to difference Q - Q’. Experimentally measured cell yield per
N.S. Panikov. M.V. Sizova I Journal of Microbiological
unit of energy source Y exp is dependent on relation between coupled (4) and uncoupled (q’) respiration: = Y(q/q’)
Ye_ = hrllis
(6)
where Y is ‘true’ yield by Pirt’s (1975) definition. 2.5. Equations and analytical solution Microbial growth in amended soil is described by the following set of differential equations derived from Eqn 4-6 under simplified conditions s >K,, s >K,: Dynamics of added substrate uptake coupled with growth = ds -4e - Q’)x dr _ uptake coupled from growth
(7)
Q’x growth Dynamics of biomass dx = Yr(Q - Q’)x + cL,rx ’ -G specific growth rate
(8)
P,,, = Y(Q - Q’, instant growth rate Dynamics of r-variable = dr !A Tt X
steady state r instant r
(9)
(1-r)
where Q is maximum specific rate of substrate uptake, Q’ is maximum specific rate of uncoupled substrate uptake, Y is biomass yield per unit of substrate consumed; x is concentration of microbial biomass per g of soil, and s is concentration of added substrate. The set of Eqn. 7-9 is integrated analytically for initial conditions x = x,, (microbial biomass in soil just before amendment), r = r0 (index of their physiological state in situ), and s = SO (initial concentration of added substrate) at t = 0: r = rJ(l
- rO)emc(m’ + r,]-’
(10)
x = X()[l - r0 + r&mf]
(11)
s=so_(l-ro)*oQ~-~Q(e”“‘_l)
Elht
Methods 24 (1996) 219-230
223
(Schmidt, 1992) Eqns. 6-8 are able to simulate lag-phase, which is well expressed at low r-0. The higher is r0, the smaller is deviation from simple exponential growth. If r0 = 0, then soil is devoid of viable biomass, and lag-phase goes to infinity. If r0 = 1, then Eqns. 6-8 are reduced to simple exponential model, when x = XOexp(Cmt). Substrate consumption rate v(t) is derived as explicit function of time after substitution of Eqn. 11 into Eqn. 7: ds
u(t)=-x=r(Q-Q’)x+Q’x = A + B - exp( p,,+)
(13)
where A=(l-rO)Q’xo, B=r, Qx,, p,,,= Y(Q - Q’). It is this equation which should be used for approximation of experimental data on consumption (transformation) in substrate amended soil. 2.6. Identification of kinetic parameters
There are 5 growth parameters in Eqn. 13 (x0, ro, Y, Q, and Q ‘), some of them may be excluded from routine identification trials. Extensive examination of diverse microbial isolates (>lOO species, (Akimenko et al., 1983)) revealed that the ratio (Q - Q’)/Q = >xxx varies in a rather narrow range so it may be accepted as a basic stoichiometric constant (~70.9). If not this ratio may be estimated directly as a CNsensitive fraction of the total soil activity. The biomass yield Y is also a uniform parameter (Payne, 1970) which may be borrowed from reference data or determined in specially designed experiments for every particular physiological group of microorganisms (see below Table 2). Going back to Eqn. 13, the composite parameters A, B and C, are easily estimated by nonlinear regression which provides the minimum of square residuals, then:
(12) m
As compared with a simple exponential
model
Q=$k,r,=
B(l -A) -- B A+B(l-A)‘XO-roQ
(13’)
224
N.S. Panikov, M.V Sizova I Journal of Microbiological
Methods 24 (1996) 219-230
Table 2 Diversity of kinetic methods Physiological or taxonomic group
Incubation conditions, added substrate
Recorded microbial process u(t)
Measuring of hx to calculate Y
Aerobic chemoorganotrophic microorganisms
Aerobic incubation and mineral salts (N, P, K) and organic substrates: Glucose Glu + actidione glu-act-penicillin Glu + chloramphenicol Sterile soil
Co2 evolution
C uptake minus CO,-C evolution
Glucose utilizing microorganisms Procaryotes (bacteria) Gram-negative bacteria Eucaryotes (fungi) Nearly total account Chemolythotrophic
bacteria
‘VO, incorporation soil
Aerobic incubation with reduced mineral of Cl compounds:
CH,
H, uptake CO uptake NO; production CH, uptake
Anaerobic incubation under Ar or N2 in air and: Glucose + NO, + C,H2 CO2 + HZ, or acetate CO: + H, Fatty acids + Hz
N,O production CH, production Acetate production SOi- uptake
C uptake minus C in products
Phototrophic microorganisms
Aerobic incubation under light and CO, in air
Light vs dark rate of CO? evolution
CO,-C uptake
Pesticide degraders
Aerobic incubation with xenobiotics (XB)
XB uptake in soil slurry
From pure culture data
Microbovorovs
Washed bacterial cells
CO, evolution uptake of bacteria
Direct microscopy
Hydrogen bacteria Carboxidotrophic bacteria Nitrifying bacteria Methanotrophic bacteria Anaerobic
microorganisms
Denitrifying bacteria Methanogenic bacteria Acetogenic bacteria Sulfate-reducing bacteria
protozoa
H* co NH, + N-serve
3. Applications of kinetic methods The kinetic approach described here is suitable for biomass determination and characterization of diverse groups of soil microorganisms. Below we will present several examples. 3.1. Aerobic microorganisms
utilizing glucose
Soil was amended with glucose and mineral salts and incubated under constant moisture and temperature. The response of soil microorganisms was followed by automatic recording of CO, evolution (Fig. 1). The biomass yield per
to
unit of CO, formed, Yco2, was assessed from the C-balance in the independent experiment, when CO, production >p was measured parallel to residual substrate s and exometabolites formed >m, then Y,, = >xl >p = (>s->m->p)l> p, where >s = i,, -s. Usually it is sufficient to record CO, evolution and the sum ‘residual glucose + exometabolites’ by determination of total water soluble carbon. For most of the studied soils the value of Y,, varied in the rather narrow range of 1.1-0.2 g biomass C per g of CO,-C. This range is really less than variation of other growth parameters, and we should not expect a serious calculation error by assum-
N.S. Panikov, M.V. Sizova I Journal of Microbiological
Methods 24 (1996) 219-230
225
Fig. 1. Laboratory device for automatic recording of CO, evolution rates from amended soil samples: air pump (l), CO, absorber (2), manifold of bottles (3) containing soil (4), blank bottle (5), air humidifier (6), flowstats (7), computer-operating valve (8), temperature bath (9), rotameter (IO), and infra-red CO, analyzer (11, model Infralyt-4, manufacturer Junkalor Dessau, East Germany).
ing Y to be a constant. An explanation of the Y constancy stems from the fact that soil incubation was done under uniform conditions of unlimited growth (all essential nutrients were added to soil, including sources of energy, C, N, P, K, Mg, etc.), meanwhile considerable changes of Y are expected only after shift from one type of growth limitation to another (Oanikov, 1995). To secure Y constancy in practical terms, we restricted kinetic analysis by those segments of total dynamics of soil respiration v, which correspond to phase of unlimited growth (i.e. when specific growth rate d(ln v)ldt remained to be constant or increased in time. Results are shown in Fig. 2. Suggested model did fit well to experimental points, residuals (l&1.8%) being no more than error of respiration measurement. The individual replicates of the same soil displayed almost coinciding data demonstrating the reproducibility of the measurements with automatic CO, analysis. Microbial growth in amended soil deviated considerable from simple exponential pattern, since r in this particular soil was rather low (O.OOSS-
0.0096) due to chronic starvation of microbial populations in situ. For other soils (see Panikov, 1991a) we have found a broad variation of parameter r0 ranging from as low as 0.0025 (in sphagnum peat) to 1.0 (rhizosphere soil). Independent determinations of uncoupled respiration as a regression parameter (A = (lrO)Q’x,) and from the direct measurement of CN-resistant respiration were consistent. For the entire soil community, the parameter >was close to the same value of 0.9 that was found in pure cultures. Microbial biomass is calculated from the respiration curve according to formula 13’. Let us take as an example the first respiration curve shown in Fig. 2. Non-linear regression provides the following best fit of experimental points to exponential equation 13: v(t) = 0.296 + 0.0349 exp(O.l49t), i.e. A = 0.296, B = 0.034, C,,, = 0.14 h-l. Putting all these figures as well as the values of stoichiometric constants Y = 1.1 and > = 0.9 into formula 13’, we arrived at Q = 0.141 mg C/h/mg biomass C, r,, = 0.011 and x0 = 21.15 mg biomass C per g of dry peat soil. In mineral soils
226
N.S. Panikov, M.V. Sizova I Journal of Microbiological Respiration
rate (mg CO2-C/h/g
soil)
1.2
0.8
0.6
0.2
0
0
2
4
6
8
10
12
14
16
18
20
22
h
Fig. 2. Dynamics of CO, evolution rate from peat soil amended with glucose, an example of incubation experiments used for determination of biomass of glucose utilizing microorganisms. Peat soil sampled from layer O-10 cm (ombrotrophic sphagnum bog, Tver) was amended with glucose, 5 mgig wet soil, (NH,)2SO,, 1.0 mgig, and KH,PO,, 1.0 mglg, and incubated at 258 and moisture 91.1 weight% under continuous flow of CO,-free air (0.3 1 per min). The experimental data obtained for three replicate samples were fitted to Eqn. 13. Dotted line is dynamics of CN-resistant respiration, measured in soil subsamples mixed with KCN, 10 mol C/g soil).
these values vary in a range 0.050-1.0 mg C/g soil. As compared with well known physiological method of soil biomass determination (Anderson and Domsch, 1978) our technique is refined in two points: (i) it gives estimation of biomass in absolute (not arbitrary!) units due to direct determination of conversion factor from glucoseinduced respiration rate to biomass in every analyzed soil type, and (ii) it characterizes the physiological state of microbial populations in situ. 3.2. Total account of heterotrophic microorganisms We should remember, however, that glucose is a rather selective substrate being used by many saprotrophic microorganisms, but not by the majority of them. In particular, many oligo-
Methods 24 (1996) 219-230
trophic bacteria do not respond to glucose amendment. Compared to a microscopic counting method, the glucose-kinetic method accounts for no more than 20-50% of microbial biomass (although direct microscopy is a very bad yardstick with all its bias and uncertainty). A more complete account of aerobic chemoorganotrophic microorganisms was achieved by the use of substrates less selective than glucose, e.g. sterilized soil. This amendment contains the full array of organic substances originating from killed microbial cells and partly decomposed soil humus. Probably nowadays the most popular technique to measure total soil microbial biomass is Jenkinson’s fumigation method and its recent developments. The major advantages of kinetic approach over fumigation is the possibility to evaluate a conversion factor for each individual soil sample. The conversion of fumigation flush to biomass is achieved only as an average factor for all soils from the enrichment experiments by adding a known quantity of 14C-labeled microbial biomass of different microorganisms grown in shake flasks to a soil, fumigating and measuring 14C0, in flush (Anderson and Domsch, 1978a). It is clear that conversion facors thus obtained are subjected to extremely large errors because they are obtained for microbial isolates and not necessarily are identical to that of the soil population. Other advantages of the kinetic method are that (i) it characterizes not only biomass but also the physiological state of soil microbes, (ii) it is rapid, and allows for automatization of the entire procedure, and (iii) it can be easily adapted to categorize total biomass into different functional groups. On the other hand, the fumigation technique provides the unique possibility to analyze intracellular compounds which originated directly from the cells of living soil organisms. This is especially valuable when fumigation is combined with isotope analysis. 3.3. Use of specific inhibitors Prokaryotic (mainly bacteria) (mainly fungi) microorganisms
and eukaryotic were differen-
N.S. Panikov, M.V. Sizova I Journal of Microbiological
tiated by the use of selective inhibitors of protein actidione and chloramphenicol biosynthesis, (Anderson and Domsch, 1973). We used this experimental approach in combination with mathematical analysis of respiration curves (Fig. 3). For this particular case kinetic equation 13 was modified (see the caption to Fig. 3) to account the growth of 2 microbial components (bacteria and fungi) as dependent on the presence of inhibitor(s). Besides we introduced the term Ae - kt which describes the decay of soil
Respiration rate (mg C02-C/h/g
227
Methods 24 (1996) 219-230
respiration in soil treated with both antibiotics. The operating mechanisms of this decay are not clear. It may be explained by (i) turnover of microbial proteins which always occurs in microbial cells and normally (in the absence of protein inhibitors) is masked by growth, or (ii) effects of mechanical disturbance (stirring) of soil sample, causing release of available substrates from disrupted cells and soil crumbles. In a specially designed experiment (data not shown), we have found that mixing alone contributed a small
soil)
Biomass (mg C/g soil)
0.7
0.6
0.5
0.4
0.3
0.2
. 0.1
0 0
10
20
30
40
h
60
h
Fig. 3. Dynamics of microbial growth and respiration in soil amended with glucose and specific inhibitors suppressing growth of bacteria and fungi. Peat soil was incubated at 20°C with 10 mg/g glucose (G) and mineral salts (see caption to Fig. 2). Also 1000 ppm of inhibitors were added: actidione (G + A), chloramphenicol (G + C), or their mixture (G + A + C). Control sample was glucose-amended soil without antibiotics (G). Recorded dynamic variables: (1) soil respiration; (2) bacterial population by plating on meat-peptone agar, the CFU number being converted to biomass (assumed density of wet biomass 1.1 g/ml, water content 60%, carbon content SO%, cell sizes were measured directly in soil with scanning electron microscope); and (3) biomass of fungi assessed by direct soil microcopy from measurements of hyphae length and width. The curves l-3 were calculated according to the following modification of Eqn. 13:
G G + A
v
zs
Ae-”
+
B/f
+
B
b
erb’
G+G
v = Ae-*I + B/f’
G+C+A
v=Ae-*‘+B,+B,
+ B,
v = Ae-“’ f B, + B,esb’
where G symbolizes soil amendment with glucose alone, G + C is glucose + chloramphenicol, G + A is glucose + actidione, and G + C + A is soil amendment with glucose and both antibiotics; v is respiration rate, t is time, C is specific growth rate, A and B have the same meaning as in original equation 13, subscripts f and b being labels of respectively fungi and bacteria, k is decay constant.
228
N.S. Panikov, M.V. Sizova I Journal of Microbiological
change to respiration rate. Thus, the first explanation seems to be more likely. Anyway mentioned decay in respiratory activity was described by first-order rate equation and assumed to occur in all soil treatments. Agreement of simulation with experimental data was excellent. The relevance of the kinetic method was confirmed also by direct microscopy i.e., the observed dynamics of bacterial and fungal growth was closely followed by the model predictions (curves 2 and 3, Fig. 3). In general bacteria displayed a higher maximum growth rate, a lower actual activity and a lower biomass standing crops, e.g. in a peat soil we found for bacteria Cb = 0.194 h - 1, r0 = 0.0025, and x0 = 464 Cg C/g soil, while for fungi Cf = 0.028 h - 1, r0 = 0.87, and x0 = 2195 Cg C/g soil. The prospects of biomass differentiation with group specific antibiotics seem to be not exhausted, e.g. one may use penicillin to differentiate Gram-negative and Gram-positive bacteria.
3.4. Chemolithotrophic
microorganisms
Traditional microbiological technique is not able to estimate the biomass of these microbial populations extremely important playing biogeochemical role. Enumeration of nitrifiers, hydrogen bacteria, carboxidobacteria, sulphur bacteria and many other specific groups of chemolithoautotrophs may be carried out by MPN or plating technique. However these methods provide information only in terms of CFU (not biomass!) and significantly underestimate the real microbial abundance. Kinetic methods give full account of microbial biomass and are much more exact and rapid than traditional technique. For quantification of chemolithotrophs the soil is incubated with respective inorganic substrate. For instance, to determine the biomass of hydrogen, methanotrophic and carboxidotrophic bacteria soil was incubated under air enriched with respectively H,, CH,, and CO (Table 2 (see for details Popov and Panikov, 1990; Paleyeva et al., 1990). If incubation conditions are adequate, then the rates of substrate
Methods 24 (1996) 219-230
consumption or oxidation v(t) should increase for some time according to Eqn. 13. Y evaluation is very important, since growth stoichiometry is very specific and the ratio >xl > s varies in broad range. The best precision in estimation of >x provides the measuring of 14C0, uptake in soil amended with respective inorganic substrate, unamended soil serving as reference to account for heterotrophic CO, fixation. In the special case of methanotrophic bacteria Y is calculated from C-balance or label incorporation from 14CH4. The use of specific inhibitors allows to differentiate strictly autotrophic and mixotrophic components. That is especially important for differentiation of autotrophic and heterotrophic nitrifiers.
3.5. Anaerobic soil microorganisms Application of kinetic methods to anaerobic bacteria has the same advantages as in the case of chemolithotrophs, as soon as their metabolism is highly specific and the range of environment conditions for growth is extremely narrow. The recorded variable v(t) is generally the rate of end products formation. Thus biomass of denitrifying, methanogenic, acetogenic and sulfate-reducing bacteria were determined from the production rates of respectively N, + N,O, CH,, acetate and S, -. Evaluation of Y from the growth massbalance is unreliable, especially in the case of methanogenic, sulfate-reducing and acetogenic bacteria, thus it is better to borrow yield data from pure culture studies. As an example we display recent unpublished result of kinetic determination of biomass of methanogenic bacteria (Fig. 4).
3.4. Phototrophic soil microorganisms Soil illumination and elevated CO, supply give rise to the development of soil algae and phototrophic bacteria. A characteristic feature of their growth in soil is its isurface localization (crust of phototrophic Stoichiometry formation). microorganisms is extremely simple, since they
N.S. Panikov, M.V. Sizova I Journal of Microbiological CH4 production (mg Ch/dm3 of soil)
Methods 24 (1996) 219-230
229
tions being the main route of decomposition. In such cases the kinetic approach in the described version is no longer applicable (see below).
0.3
3.8, Microbial grazers (protozoa)
0
40
80
120
160
Fig. 4. Dynamics of methane formation in a peat soil during anaerobic incubation, an example of determination of biomass of methanogenic bacteria. Incubation condition: soil was amended with 10 mgig of glycerol and incubated under N,:CO? (99:l) at 22.5”C, CH, concentration was determined with FID in subsamples withdrawn from gas phase. The curve was calculated according to Eqn. 13 with the following parameters: A = 0, B = 0.0034 (g C/h/ml, (WI= 0.089 h-l. Non-linear regression was used over time range from 0 to 48 h (corresponds to exponential segment of the observed dynamics). Biomass yield of methanogenic bacteria was assumed to be Y = 90 mg biomass C per g of CH,-C, then we arrived to the value of microbial biomass in soil before amendment n, =3.4 10-3 (g C/g of wet peat soil. Experimental points represent two replicates.
use CO, as a source of cell carbon, hence, Y71 (Dedysh et al., 1991). 3.7. Decomposition
of xenobiotics
The biomass of pesticides degraders was evaluated from the xenobiotic substrate depletion curves in soil slurry or suspension. The characteristic feature of this microbial process is its low rate, significant substrate inhibition and strong adsorption of analyzed substances by soil. Hence it is not reasonable to measure the initial response of microorganisms to substrate consumption. The better approach is to analyze entire curve od substrate consumption as suggested by Schmidt (1992). Some xenobiotics could not be used at all as a growth substrate, the co-metabolism in combination with abiotic chemical reac-
Their determination is carried out with washed microbial cells as added substrate. The biomass is calculated from the dynamic curve of bacterial cell consumption or soil sample respiration after subtraction of respective control trial (amended soil with actidione, the inhibitor of predator growth). The evaluation of stoichiometric parameter Y from growth balance is rather difficult. So the most reliable seems to be calibration of kinetic methods by direct microscopy.
4. Limitations It would be unreasonable to restrict ourselves only to appraisal of suggested technique. To forestall possible disappointment we will outline the problem areas of kinetic methods. Mainly it is the determination of biomass in such situations when substrate conversion is not coupled with growth of responsible microbial populations. Apart from pesticide degradation mentioned above microbial processes of such kind include decomposition of soil humus, lignocellulose and, moreover, any polymeric or low molecular weight substances which need to be decomposed by extracellular enzymes. The rate of substrate transformation under the action of extracellular enzymes is no longer proportional to biomass of microbial degraders. Very often products of enzymatic hydrolysis are consumed by other microorganisms, associated with hydrolytics. Additional difficulty is caused by accumulation of enzymes immobilized on soil particles; functionally they are completely independent of microbial biomass. The other possible mechanism of uncoupling between microbial process and microbial growth are co-metabolic reactions and production of secondary metabolites, like antibiotics, toxins, some pigments etc. For these situations a kinetic approach may be used. However,
230
N.S. Panikov,
a completely different should be developed.
M.V. Sizova
theoretical
I Journal of Microbiological
background
Acknowledgements This work was partly supported by the International Science Foundation, Grant No MIQ 000, and the Russian Fund for Fundamental Research, Grant No 93-04-7202.
References [l] Akimenko, V.K., Trutko, SM., Medentsev, A.G., Korobov, V.P. (1983) Distribution of cyanide-resistant respiration among yeasts and bacteria and its relation to oversynthesis of metabolites. Arch. Microbial. 136, 234-241. [2] Anderson, J.P.I. and Domsch, K.H. (1973) Quantification of bacterial and fungal contribution to soil respiration. Arch. Mikrobiol. 93, 113-127. [3] Anderson, J.P.I. and Domsch, K.H. (1978) A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215221. [4] Dedysh, S.N., Paleyeva, M.V. and Panikov, N.S. (1991) A kinetic method for the determination of algal biomass in soil. Microbiology (Engl. transl.) 60, 387-394. [51 Jenkinson, D.S. and Ladd, J.N. (1981) Microbial biomass in soils: measurement and turnover. In Soil Biochemistry, Vol. 5 (ed. by E.A. Paul and J.N. Ladd), 415-471. Marcel Dekker, New York. 161 Jenkinson, D.S. & Powlson, D.S. (1976) The effect of biocidal treatments on metabolism in soil. V. A method for measuring soil biomass. Soil Biol. Biochem. 17, 57-63. [71 Ingham, E.R., Griffiths, R.P., Cromack, K. & Entry, J.A. (1991) Comparison of direct vs fumigation incubation microbial biomass estimates from ectomycorrhizal mat and non-mat soils. Soil Biol. Biochem. 23,465-471.
Methods
24 (1996) 219-230
PI Mirchink, T.G. and Panikov, N.S. (1985) Modern approaches for determining the biomass and productivity of fungi and bacteria in soil. Adv. Microbial. (Usp. Mikrobiol.) 20, 198-226 (in Russian). 191 Paleyeva, M.V., Svetlov, S.V. and Panikov, N.S. (1990) Measurement of the biomass of soil microorganisms utilizing molecular hydrogen. Microbiology (Engl. transl.) 59, 344-349. [lOI Panikov, N.S. (1991a) Kinetics of Microbial Growth: General Principles and Ecological Applications. Nauka Publ., Moscow. ill1 Panikov, N.S. (1991a) A synthetic chemostat model as a means of describing the complex dynamic behavior of microorganisms. Microbiology (Engl. transl.) 60, 299307. [=I Parkinson, D. and Coleman, D.C. (1991) Microbial communities, activity and biomass. Agricult., Ecosyst. Envir. 34: 3-33. [I31 Payne, W.J. (1970) Energy yields and growth of heterotrophs. Annu. Rev. Microbial. 24, 17-52. P41 Popov, A.I. and Panikov, N.S. (1990) A kinetic method for the measurement of the numbers of nitrifying bacteria in soil. Microbiology (Engl. trans.) 59, 482486. P51 Petersen, S.O., Henriksen, K., Blackburn, T.H., and King, G.M. (1991) A comparison of phospholipid and chloroform fumigation analysis for biomass in soil: potentials and limitations. FEMS Microbial. Ecol. 85, 257-268. Ml Pitt, S.J. (1975) Principles of Microbe and Cell Cultivation. Blackwell Science, London. growth-reP71 Schmidt, SK. (1992) A substrate-induced sponse method for estimating the biomass of microbial functional groups in soil and aquatic systems. FEMS Microbial. Ecol. 101, 197-206. [18] Tempest. D.W. and Neijssel, O.M. (1984) The status of YATP and maintenance energy as biologically interpretable phenomena. Ann. Rev. Microbial. 38, 459-486.
Journal of Microbiological Methods 24 (1996) 219-230
A kinetic method for estimating the biomass of microbial functional groups in soil Nicolai S. Panikov*, institute of Microbiology,
Maria V. Sizova
Russian Academy of Sciences, Prospect 60-let Octyabrya 712, Moscow 117 811. Russia
Received 19 Juli 1994; revised 24 January 1995; accepted 21 March 1995
Abstract
A kinetic method is presented for estimating the biomass and characterizing the in situ physiological state of microorganisms capable of carping out specific metabolic function. Fresh soil samples are incubated with a particular substrate under specified controlled conditions, the rates of substrate uptake or product formation being continuously recorded. The observed dynamic curve is approximated by a mathematical model which simulates substrate-induced microbial growth, microbial biomass at zero time being identified as one of the model’s parameters. Examples of the developed technique are presented for the following groups of soil microorganisms: aerobic heterotrophic (including prokaryotic and eukaryotic) microorganisms, chemolythotrophic bacteria (hydrogen, carboxidobacteria, nitrifying bacteria), anaerobic bacteria (methanogenic, acetogenic, sulfate- reducing, denitrifying), phototrophic microorganisms, pesticide degraders, and microbial grazers. Keywords:
Soil microbial biomass; Mathematical
simulation;
1. Introduction There
are four classes of analytical
techniques
suitable for determining microbial biomass in soils: (i) ex situ germ enumeration (plating and MPN) , (ii) direct microscopy, (iii) biochemical methods, and (iv) kinetic methods. Table 1 outlines specific advantages and shortcomings of these approaches (for details see discussion in numerous experimental papers and reviews, e.g. (Jenkinson and Powlson, 1976; Jenkinson and Ladd, 1981; Mirchink and Panikov, 1985; Ingham et al., 1991; Parkinson and Coleman, 1991; Petersen et al., 1991). Obviously, the choice of the method depends on the targets of a par* Corresponding author. Elsevier Science B.V. 0167-7012(95)00074-7
SSDI
Physiological state of microorganisms
in situ
titular study, and not one of them could be regarded as having absolute preference. It is sufficient to mention that even plating and MPN techniques (which have been subjected to most severe criticism in the last decades) remain valuable and indispensable in specific fields of research. In the last decade there has been growing interest in non-traditional approaches based on biochemical determinations (class 3, Table 1). New generations of these methods gave a strong impulse to the development of quantitative microbial ecology. However, these methods have their limitations. Firstly, they use doubtful conversion factors from measured chemical index (ATP, DNA, phospholipid or chitin content, fumigation flush, etc) to real biomass. The main
220
N.S. Panikov, M.V. Sizova I Journal of Microbioiogical
Methods 24 (1996) 219-230
Table 1 Comparison of different method for soil biomass determination Methods
What is measured
Conversion factor
Shortcomings
Advantages
Plating and MPN
Number of CFU (colony forming units) or highest positive dilution
Absent or extremely unreliable
Direct microscopy
Bacterial number and mycelial length
Derived from biovolume of measured cells, as well as assumed dry matter density of cells; tedious and unreliable procedure
Low recovery as a result of cell adhesion and clumping, occurrence of nonculturable forms and “laboratory artifacts”, variability of CFU sizes Tiresome and time consuming procedure, person-to-person variations of count efficiency, unreliable differentiation of cells from soil particles and plant debris
Biochemical
Content of unique cell component in soil (ATP, DNA, chitin, muramic acid, lipids, or whole cell material released by biocidal treatment)
Estimated from (i) chemical analysis of isolates (ii) calibration in situ in incubation experiments, (iii) comparison with standard method (e.g. direct microscopy)
Occurrence of analyzed chemical species in plant roots, animals and in extracellular milieu, wide variation of conversion factors
Kinetic
Rates of specific metabolic reactions estimated in incubation experiments with amended soils samples
Found as parameters of mathematical model describing microbial growth on added substrate. Rapid and exact computer-aided identification procedure
Ignores resting forms of microorganisms
Isolation and identification of microbes, differentiation of physiological and taxonomic groups, detection of individual populations carrying genetic markers Characterization of biomorphological structure of soil community and its spatial community and its spatial organization, detection of individual populations (in combination with DNA probes and immunofluorescence technique) High precision, open to standardization and automatization, compatibility of results obtained in different laboratories, effective in combination with isotope technique to label microbial cells in situ All technical advantage of biochemical methods (see above), exact calibration, differentiates microbial biomass into functional groups, assays physiological state of microbes in situ
reason for this is variation of the content of respective compounds in microbial cells of different species and dependence on growth conditions. Secondly, some analyzed compounds display significant accumulation in soils outside the viable cells (slow degradation after microbial death). This may lead to considerable overestimation of the real biomass. Thirdly, the bio-
chemical methods as a rule give only an integrated estimate of the total biomass, neglecting the diversity of soil microbial community. The present study deals with the fourth class of analytical techniques which belong to the socalled kinetic methods (class 4, Table 1). Our aim was to refine these methods and free them from the above-mentioned limitations.
N.S. Panikov, M.V. Sizova I Journal of Microbiological
2. Theory 2.1. The principle of kinetic methods To determine microbial biomass short-term enrichment experiments are performed, adding to soil a specified substrate, and following its uptake or product formation. The observed dynamics are approximated by a mathematical model which describes microbial growth induced by substrate addition. Identification of the model’s parameters gives an estimate of microbial biomass at zero time, i.e. just before substrate amendment. 2.2. Requirements
to incubation procedure
The following requirements should be met: (i) Added substrate has to be uniformly distributed throughout the soil (this is a sensible requirement in the case of soluble or volatile chemicals). (ii) The soil amendment should be large enough to provide zero order kinetics of substrate transformation (sO> K,), but well below the inhibitory level. (iii) Environmental parameters have to be physiologically suitable and kept constant. If all these conditions are satisfied, then the growth of responsive microbial populations should be rather simple: uniform (almost homogeneous) and unlimited. Moreover, for some time the growth should be unaffected by biological interactions such as competition, antagonism, or grazing, due to excess of substrate, catabolic repression of antibiotic’s biosynthesis and delay in the development of protozoan grazers respectively (Panikov, 1991a). Hence, a multi-species soil community temporary behaves like a single population. This gives us some freedom in the choice of simulation model. In particular we may rely on relatively simple kinetic models which have been originally developed for homogeneous batch culture. 2.3. Requirements
to the mathematical model
However, the model should not be oversimplified. Simple kinetic models of exponential growth described elsewhere (Schmidt, 1992) are
Methods 24 (19%)
219-230
221
of limited use in respect to soil. Indeed, unamended soil most frequently is extremely poor in available energy sources. Substrate amendment is expected to bring about a dramatic changes in physiological state of starving soil populations: ‘famine-to-feast’ transition is associated with a long lag-phase, changes in the composition of microbial biomass, extensive dissipation of added substrate in energy-spilling reactions, growth yield variation, etc. A kinetic model must take into account all these effects resulting in strong deviation of microbial growth from the simple exponential pattern. Recently we proposed a relatively simple structured model which was able to describe the transient dynamics of this kind (Panikov, 1991b). It was called the ‘synthetic chemostat model’ (SCM), since it includes as elements many known kinetic models of substrate limited growth. Here SCM is outlined as a following simplified version. 2.4. Account of variation in cell composition This is the key kinetic problem which is essential for the understanding of microbial adaptation to a changeable environment. According to SCM all macromolecular cell constituents are divided into two groups: Primary cell constituents absolutely necessary for growth (P-components), and components needed for cell survival under growth restriction (U-components). The characteristic examples of P-components are ribosomes or rRNA and ribosomal proteins, other RNA fractions, enzymes of the primary metabolic pathways. Their intracellular content increases parallel to increase of growth rate, e.g. total RNA content varies from l-2% of cell dry weight in starving populations up to 20% in intensively growing culture. Contrary to P-components the contribution of U- components to cell biomass decreases with growth acceleration. An examples are enzymes of the secondary metabolism, protective pigments, reserved substances, transport systems of high affinity etc. An amount of P- and U-components expressed as a fraction of total cell mass (P and U, g per g
222
N.S. Panikov, M.V. Sizova I Journal of Microbiological
of biomass respectively) vary within the upper (I’,, U,) and low (I’,,, U,,) limits. The sum of all PO and UO components comprise the constitutive cell fraction (not subjected to adaptive changes), while others represent inducible cell components. Mass conservation constrains is as follows:
iPi+&Jj=i:P”+&J;
i=l
j=l
i=l
j=l
=~P:+~up=~,i:xp,+t:x~i=x i=I
j=l
i=l
j=l
Methods 24 (1996) 219-230
4, = rQ q-$y+(l-r)Qz& 1
where the first and the second terms on the right side stand respectively for low (P-component) and high (U-component) affinity of transport system. Finally, the third rule explains how environmental factors control adaptive changes in microbial cells. Steady state r is an immediate function of s, limiting substrate concentration:
(1) where k and 1 are the numbers of assigned Pand U-components respectively. There is a great body of experimental evidence (Panikov, 1991b), that the variation of cell components is not stochastic but obeys the following rules: (1) an increase of one individual P-component is accompanied by increase of other P-components, (2) total enlargement of P-sum is accompanied by corresponding decrease of U-sum and vise versa, and (3) the P/U-ratio is controlled by limiting substrate concentration in environment. The first rule could be expressed in mathematical terms as follows: r=
P, - P; Py-P;=
P*-Pi P,-PO, p; _ pi = * ‘. = P;-Po,
(2)
where the variable I is the index of physiological state, a ratio between instant quantity of any particular P-component and its total changeable part. T varies from zero (when all P-components come down to low limits) to 1.0 (when they attain maximal values). The second rule states that P and U are complementary to each other, and the content of any U-component should be proportional to difference (1 - r). The introduction of variable r greatly simplifies the use of structured models, because the adaptive variation of cell composition (and metabolic activity which is determined by intracellular content of particular enzymes) now could be expressed via one single ‘master’ variable r. For example, the specific rate of substrate uptake q, is defined as:
2
(4) where K, is the saturation constant. Under chronic starvation s = 0 and r0. Under unlimited steady state growth when >K,, r is close to maximum r - 1. During transients caused by sudden s changes, an instant r-value approaches new steady state. The rate of change of r during transients is expected to be proportional to specific growth rate of total microbial biomass C and the difference between instant and final (steady-state) values of r (Panikov, 1991b), then:
f=p(-&-r)
(5)
Dissipative and productive consumption of energy source yield variation. Respiratory enzymes are divided into two groups in respect to both functional role and sensitivity to cyanide (Akimenko et al., 1973). CN-sensitive oxidases carry out productive substrate oxidation coupled with ATP generation and cell growth, their activity being positively correlated with growth rate and r-value (P-components). CN-resistant oxidases belong to constitutive cell components and carry out energy-spilling oxidative reactions uncoupled from ATP generation. Their probable physiological function is cell protection from surplus of catabolites (Tempest and Neijssel, 1984). Total specific respiration activity measured under excess of substrate is denoted as Q, and CN-resistant fraction as Q’, then the productive fraction of total respiration is equal to difference Q - Q’. Experimentally measured cell yield per
N.S. Panikov. M.V. Sizova I Journal of Microbiological
unit of energy source Y exp is dependent on relation between coupled (4) and uncoupled (q’) respiration: = Y(q/q’)
Ye_ = hrllis
(6)
where Y is ‘true’ yield by Pirt’s (1975) definition. 2.5. Equations and analytical solution Microbial growth in amended soil is described by the following set of differential equations derived from Eqn 4-6 under simplified conditions s >K,, s >K,: Dynamics of added substrate uptake coupled with growth = ds -4e - Q’)x dr _ uptake coupled from growth
(7)
Q’x growth Dynamics of biomass dx = Yr(Q - Q’)x + cL,rx ’ -G specific growth rate
(8)
P,,, = Y(Q - Q’, instant growth rate Dynamics of r-variable = dr !A Tt X
steady state r instant r
(9)
(1-r)
where Q is maximum specific rate of substrate uptake, Q’ is maximum specific rate of uncoupled substrate uptake, Y is biomass yield per unit of substrate consumed; x is concentration of microbial biomass per g of soil, and s is concentration of added substrate. The set of Eqn. 7-9 is integrated analytically for initial conditions x = x,, (microbial biomass in soil just before amendment), r = r0 (index of their physiological state in situ), and s = SO (initial concentration of added substrate) at t = 0: r = rJ(l
- rO)emc(m’ + r,]-’
(10)
x = X()[l - r0 + r&mf]
(11)
s=so_(l-ro)*oQ~-~Q(e”“‘_l)
Elht
Methods 24 (1996) 219-230
223
(Schmidt, 1992) Eqns. 6-8 are able to simulate lag-phase, which is well expressed at low r-0. The higher is r0, the smaller is deviation from simple exponential growth. If r0 = 0, then soil is devoid of viable biomass, and lag-phase goes to infinity. If r0 = 1, then Eqns. 6-8 are reduced to simple exponential model, when x = XOexp(Cmt). Substrate consumption rate v(t) is derived as explicit function of time after substitution of Eqn. 11 into Eqn. 7: ds
u(t)=-x=r(Q-Q’)x+Q’x = A + B - exp( p,,+)
(13)
where A=(l-rO)Q’xo, B=r, Qx,, p,,,= Y(Q - Q’). It is this equation which should be used for approximation of experimental data on consumption (transformation) in substrate amended soil. 2.6. Identification of kinetic parameters
There are 5 growth parameters in Eqn. 13 (x0, ro, Y, Q, and Q ‘), some of them may be excluded from routine identification trials. Extensive examination of diverse microbial isolates (>lOO species, (Akimenko et al., 1983)) revealed that the ratio (Q - Q’)/Q = >xxx varies in a rather narrow range so it may be accepted as a basic stoichiometric constant (~70.9). If not this ratio may be estimated directly as a CNsensitive fraction of the total soil activity. The biomass yield Y is also a uniform parameter (Payne, 1970) which may be borrowed from reference data or determined in specially designed experiments for every particular physiological group of microorganisms (see below Table 2). Going back to Eqn. 13, the composite parameters A, B and C, are easily estimated by nonlinear regression which provides the minimum of square residuals, then:
(12) m
As compared with a simple exponential
model
Q=$k,r,=
B(l -A) -- B A+B(l-A)‘XO-roQ
(13’)
224
N.S. Panikov, M.V Sizova I Journal of Microbiological
Methods 24 (1996) 219-230
Table 2 Diversity of kinetic methods Physiological or taxonomic group
Incubation conditions, added substrate
Recorded microbial process u(t)
Measuring of hx to calculate Y
Aerobic chemoorganotrophic microorganisms
Aerobic incubation and mineral salts (N, P, K) and organic substrates: Glucose Glu + actidione glu-act-penicillin Glu + chloramphenicol Sterile soil
Co2 evolution
C uptake minus CO,-C evolution
Glucose utilizing microorganisms Procaryotes (bacteria) Gram-negative bacteria Eucaryotes (fungi) Nearly total account Chemolythotrophic
bacteria
‘VO, incorporation soil
Aerobic incubation with reduced mineral of Cl compounds:
CH,
H, uptake CO uptake NO; production CH, uptake
Anaerobic incubation under Ar or N2 in air and: Glucose + NO, + C,H2 CO2 + HZ, or acetate CO: + H, Fatty acids + Hz
N,O production CH, production Acetate production SOi- uptake
C uptake minus C in products
Phototrophic microorganisms
Aerobic incubation under light and CO, in air
Light vs dark rate of CO? evolution
CO,-C uptake
Pesticide degraders
Aerobic incubation with xenobiotics (XB)
XB uptake in soil slurry
From pure culture data
Microbovorovs
Washed bacterial cells
CO, evolution uptake of bacteria
Direct microscopy
Hydrogen bacteria Carboxidotrophic bacteria Nitrifying bacteria Methanotrophic bacteria Anaerobic
microorganisms
Denitrifying bacteria Methanogenic bacteria Acetogenic bacteria Sulfate-reducing bacteria
protozoa
H* co NH, + N-serve
3. Applications of kinetic methods The kinetic approach described here is suitable for biomass determination and characterization of diverse groups of soil microorganisms. Below we will present several examples. 3.1. Aerobic microorganisms
utilizing glucose
Soil was amended with glucose and mineral salts and incubated under constant moisture and temperature. The response of soil microorganisms was followed by automatic recording of CO, evolution (Fig. 1). The biomass yield per
to
unit of CO, formed, Yco2, was assessed from the C-balance in the independent experiment, when CO, production >p was measured parallel to residual substrate s and exometabolites formed >m, then Y,, = >xl >p = (>s->m->p)l> p, where >s = i,, -s. Usually it is sufficient to record CO, evolution and the sum ‘residual glucose + exometabolites’ by determination of total water soluble carbon. For most of the studied soils the value of Y,, varied in the rather narrow range of 1.1-0.2 g biomass C per g of CO,-C. This range is really less than variation of other growth parameters, and we should not expect a serious calculation error by assum-
N.S. Panikov, M.V. Sizova I Journal of Microbiological
Methods 24 (1996) 219-230
225
Fig. 1. Laboratory device for automatic recording of CO, evolution rates from amended soil samples: air pump (l), CO, absorber (2), manifold of bottles (3) containing soil (4), blank bottle (5), air humidifier (6), flowstats (7), computer-operating valve (8), temperature bath (9), rotameter (IO), and infra-red CO, analyzer (11, model Infralyt-4, manufacturer Junkalor Dessau, East Germany).
ing Y to be a constant. An explanation of the Y constancy stems from the fact that soil incubation was done under uniform conditions of unlimited growth (all essential nutrients were added to soil, including sources of energy, C, N, P, K, Mg, etc.), meanwhile considerable changes of Y are expected only after shift from one type of growth limitation to another (Oanikov, 1995). To secure Y constancy in practical terms, we restricted kinetic analysis by those segments of total dynamics of soil respiration v, which correspond to phase of unlimited growth (i.e. when specific growth rate d(ln v)ldt remained to be constant or increased in time. Results are shown in Fig. 2. Suggested model did fit well to experimental points, residuals (l&1.8%) being no more than error of respiration measurement. The individual replicates of the same soil displayed almost coinciding data demonstrating the reproducibility of the measurements with automatic CO, analysis. Microbial growth in amended soil deviated considerable from simple exponential pattern, since r in this particular soil was rather low (O.OOSS-
0.0096) due to chronic starvation of microbial populations in situ. For other soils (see Panikov, 1991a) we have found a broad variation of parameter r0 ranging from as low as 0.0025 (in sphagnum peat) to 1.0 (rhizosphere soil). Independent determinations of uncoupled respiration as a regression parameter (A = (lrO)Q’x,) and from the direct measurement of CN-resistant respiration were consistent. For the entire soil community, the parameter >was close to the same value of 0.9 that was found in pure cultures. Microbial biomass is calculated from the respiration curve according to formula 13’. Let us take as an example the first respiration curve shown in Fig. 2. Non-linear regression provides the following best fit of experimental points to exponential equation 13: v(t) = 0.296 + 0.0349 exp(O.l49t), i.e. A = 0.296, B = 0.034, C,,, = 0.14 h-l. Putting all these figures as well as the values of stoichiometric constants Y = 1.1 and > = 0.9 into formula 13’, we arrived at Q = 0.141 mg C/h/mg biomass C, r,, = 0.011 and x0 = 21.15 mg biomass C per g of dry peat soil. In mineral soils
226
N.S. Panikov, M.V. Sizova I Journal of Microbiological Respiration
rate (mg CO2-C/h/g
soil)
1.2
0.8
0.6
0.2
0
0
2
4
6
8
10
12
14
16
18
20
22
h
Fig. 2. Dynamics of CO, evolution rate from peat soil amended with glucose, an example of incubation experiments used for determination of biomass of glucose utilizing microorganisms. Peat soil sampled from layer O-10 cm (ombrotrophic sphagnum bog, Tver) was amended with glucose, 5 mgig wet soil, (NH,)2SO,, 1.0 mgig, and KH,PO,, 1.0 mglg, and incubated at 258 and moisture 91.1 weight% under continuous flow of CO,-free air (0.3 1 per min). The experimental data obtained for three replicate samples were fitted to Eqn. 13. Dotted line is dynamics of CN-resistant respiration, measured in soil subsamples mixed with KCN, 10 mol C/g soil).
these values vary in a range 0.050-1.0 mg C/g soil. As compared with well known physiological method of soil biomass determination (Anderson and Domsch, 1978) our technique is refined in two points: (i) it gives estimation of biomass in absolute (not arbitrary!) units due to direct determination of conversion factor from glucoseinduced respiration rate to biomass in every analyzed soil type, and (ii) it characterizes the physiological state of microbial populations in situ. 3.2. Total account of heterotrophic microorganisms We should remember, however, that glucose is a rather selective substrate being used by many saprotrophic microorganisms, but not by the majority of them. In particular, many oligo-
Methods 24 (1996) 219-230
trophic bacteria do not respond to glucose amendment. Compared to a microscopic counting method, the glucose-kinetic method accounts for no more than 20-50% of microbial biomass (although direct microscopy is a very bad yardstick with all its bias and uncertainty). A more complete account of aerobic chemoorganotrophic microorganisms was achieved by the use of substrates less selective than glucose, e.g. sterilized soil. This amendment contains the full array of organic substances originating from killed microbial cells and partly decomposed soil humus. Probably nowadays the most popular technique to measure total soil microbial biomass is Jenkinson’s fumigation method and its recent developments. The major advantages of kinetic approach over fumigation is the possibility to evaluate a conversion factor for each individual soil sample. The conversion of fumigation flush to biomass is achieved only as an average factor for all soils from the enrichment experiments by adding a known quantity of 14C-labeled microbial biomass of different microorganisms grown in shake flasks to a soil, fumigating and measuring 14C0, in flush (Anderson and Domsch, 1978a). It is clear that conversion facors thus obtained are subjected to extremely large errors because they are obtained for microbial isolates and not necessarily are identical to that of the soil population. Other advantages of the kinetic method are that (i) it characterizes not only biomass but also the physiological state of soil microbes, (ii) it is rapid, and allows for automatization of the entire procedure, and (iii) it can be easily adapted to categorize total biomass into different functional groups. On the other hand, the fumigation technique provides the unique possibility to analyze intracellular compounds which originated directly from the cells of living soil organisms. This is especially valuable when fumigation is combined with isotope analysis. 3.3. Use of specific inhibitors Prokaryotic (mainly bacteria) (mainly fungi) microorganisms
and eukaryotic were differen-
N.S. Panikov, M.V. Sizova I Journal of Microbiological
tiated by the use of selective inhibitors of protein actidione and chloramphenicol biosynthesis, (Anderson and Domsch, 1973). We used this experimental approach in combination with mathematical analysis of respiration curves (Fig. 3). For this particular case kinetic equation 13 was modified (see the caption to Fig. 3) to account the growth of 2 microbial components (bacteria and fungi) as dependent on the presence of inhibitor(s). Besides we introduced the term Ae - kt which describes the decay of soil
Respiration rate (mg C02-C/h/g
227
Methods 24 (1996) 219-230
respiration in soil treated with both antibiotics. The operating mechanisms of this decay are not clear. It may be explained by (i) turnover of microbial proteins which always occurs in microbial cells and normally (in the absence of protein inhibitors) is masked by growth, or (ii) effects of mechanical disturbance (stirring) of soil sample, causing release of available substrates from disrupted cells and soil crumbles. In a specially designed experiment (data not shown), we have found that mixing alone contributed a small
soil)
Biomass (mg C/g soil)
0.7
0.6
0.5
0.4
0.3
0.2
. 0.1
0 0
10
20
30
40
h
60
h
Fig. 3. Dynamics of microbial growth and respiration in soil amended with glucose and specific inhibitors suppressing growth of bacteria and fungi. Peat soil was incubated at 20°C with 10 mg/g glucose (G) and mineral salts (see caption to Fig. 2). Also 1000 ppm of inhibitors were added: actidione (G + A), chloramphenicol (G + C), or their mixture (G + A + C). Control sample was glucose-amended soil without antibiotics (G). Recorded dynamic variables: (1) soil respiration; (2) bacterial population by plating on meat-peptone agar, the CFU number being converted to biomass (assumed density of wet biomass 1.1 g/ml, water content 60%, carbon content SO%, cell sizes were measured directly in soil with scanning electron microscope); and (3) biomass of fungi assessed by direct soil microcopy from measurements of hyphae length and width. The curves l-3 were calculated according to the following modification of Eqn. 13:
G G + A
v
zs
Ae-”
+
B/f
+
B
b
erb’
G+G
v = Ae-*I + B/f’
G+C+A
v=Ae-*‘+B,+B,
+ B,
v = Ae-“’ f B, + B,esb’
where G symbolizes soil amendment with glucose alone, G + C is glucose + chloramphenicol, G + A is glucose + actidione, and G + C + A is soil amendment with glucose and both antibiotics; v is respiration rate, t is time, C is specific growth rate, A and B have the same meaning as in original equation 13, subscripts f and b being labels of respectively fungi and bacteria, k is decay constant.
228
N.S. Panikov, M.V. Sizova I Journal of Microbiological
change to respiration rate. Thus, the first explanation seems to be more likely. Anyway mentioned decay in respiratory activity was described by first-order rate equation and assumed to occur in all soil treatments. Agreement of simulation with experimental data was excellent. The relevance of the kinetic method was confirmed also by direct microscopy i.e., the observed dynamics of bacterial and fungal growth was closely followed by the model predictions (curves 2 and 3, Fig. 3). In general bacteria displayed a higher maximum growth rate, a lower actual activity and a lower biomass standing crops, e.g. in a peat soil we found for bacteria Cb = 0.194 h - 1, r0 = 0.0025, and x0 = 464 Cg C/g soil, while for fungi Cf = 0.028 h - 1, r0 = 0.87, and x0 = 2195 Cg C/g soil. The prospects of biomass differentiation with group specific antibiotics seem to be not exhausted, e.g. one may use penicillin to differentiate Gram-negative and Gram-positive bacteria.
3.4. Chemolithotrophic
microorganisms
Traditional microbiological technique is not able to estimate the biomass of these microbial populations extremely important playing biogeochemical role. Enumeration of nitrifiers, hydrogen bacteria, carboxidobacteria, sulphur bacteria and many other specific groups of chemolithoautotrophs may be carried out by MPN or plating technique. However these methods provide information only in terms of CFU (not biomass!) and significantly underestimate the real microbial abundance. Kinetic methods give full account of microbial biomass and are much more exact and rapid than traditional technique. For quantification of chemolithotrophs the soil is incubated with respective inorganic substrate. For instance, to determine the biomass of hydrogen, methanotrophic and carboxidotrophic bacteria soil was incubated under air enriched with respectively H,, CH,, and CO (Table 2 (see for details Popov and Panikov, 1990; Paleyeva et al., 1990). If incubation conditions are adequate, then the rates of substrate
Methods 24 (1996) 219-230
consumption or oxidation v(t) should increase for some time according to Eqn. 13. Y evaluation is very important, since growth stoichiometry is very specific and the ratio >xl > s varies in broad range. The best precision in estimation of >x provides the measuring of 14C0, uptake in soil amended with respective inorganic substrate, unamended soil serving as reference to account for heterotrophic CO, fixation. In the special case of methanotrophic bacteria Y is calculated from C-balance or label incorporation from 14CH4. The use of specific inhibitors allows to differentiate strictly autotrophic and mixotrophic components. That is especially important for differentiation of autotrophic and heterotrophic nitrifiers.
3.5. Anaerobic soil microorganisms Application of kinetic methods to anaerobic bacteria has the same advantages as in the case of chemolithotrophs, as soon as their metabolism is highly specific and the range of environment conditions for growth is extremely narrow. The recorded variable v(t) is generally the rate of end products formation. Thus biomass of denitrifying, methanogenic, acetogenic and sulfate-reducing bacteria were determined from the production rates of respectively N, + N,O, CH,, acetate and S, -. Evaluation of Y from the growth massbalance is unreliable, especially in the case of methanogenic, sulfate-reducing and acetogenic bacteria, thus it is better to borrow yield data from pure culture studies. As an example we display recent unpublished result of kinetic determination of biomass of methanogenic bacteria (Fig. 4).
3.4. Phototrophic soil microorganisms Soil illumination and elevated CO, supply give rise to the development of soil algae and phototrophic bacteria. A characteristic feature of their growth in soil is its isurface localization (crust of phototrophic Stoichiometry formation). microorganisms is extremely simple, since they
N.S. Panikov, M.V. Sizova I Journal of Microbiological CH4 production (mg Ch/dm3 of soil)
Methods 24 (1996) 219-230
229
tions being the main route of decomposition. In such cases the kinetic approach in the described version is no longer applicable (see below).
0.3
3.8, Microbial grazers (protozoa)
0
40
80
120
160
Fig. 4. Dynamics of methane formation in a peat soil during anaerobic incubation, an example of determination of biomass of methanogenic bacteria. Incubation condition: soil was amended with 10 mgig of glycerol and incubated under N,:CO? (99:l) at 22.5”C, CH, concentration was determined with FID in subsamples withdrawn from gas phase. The curve was calculated according to Eqn. 13 with the following parameters: A = 0, B = 0.0034 (g C/h/ml, (WI= 0.089 h-l. Non-linear regression was used over time range from 0 to 48 h (corresponds to exponential segment of the observed dynamics). Biomass yield of methanogenic bacteria was assumed to be Y = 90 mg biomass C per g of CH,-C, then we arrived to the value of microbial biomass in soil before amendment n, =3.4 10-3 (g C/g of wet peat soil. Experimental points represent two replicates.
use CO, as a source of cell carbon, hence, Y71 (Dedysh et al., 1991). 3.7. Decomposition
of xenobiotics
The biomass of pesticides degraders was evaluated from the xenobiotic substrate depletion curves in soil slurry or suspension. The characteristic feature of this microbial process is its low rate, significant substrate inhibition and strong adsorption of analyzed substances by soil. Hence it is not reasonable to measure the initial response of microorganisms to substrate consumption. The better approach is to analyze entire curve od substrate consumption as suggested by Schmidt (1992). Some xenobiotics could not be used at all as a growth substrate, the co-metabolism in combination with abiotic chemical reac-
Their determination is carried out with washed microbial cells as added substrate. The biomass is calculated from the dynamic curve of bacterial cell consumption or soil sample respiration after subtraction of respective control trial (amended soil with actidione, the inhibitor of predator growth). The evaluation of stoichiometric parameter Y from growth balance is rather difficult. So the most reliable seems to be calibration of kinetic methods by direct microscopy.
4. Limitations It would be unreasonable to restrict ourselves only to appraisal of suggested technique. To forestall possible disappointment we will outline the problem areas of kinetic methods. Mainly it is the determination of biomass in such situations when substrate conversion is not coupled with growth of responsible microbial populations. Apart from pesticide degradation mentioned above microbial processes of such kind include decomposition of soil humus, lignocellulose and, moreover, any polymeric or low molecular weight substances which need to be decomposed by extracellular enzymes. The rate of substrate transformation under the action of extracellular enzymes is no longer proportional to biomass of microbial degraders. Very often products of enzymatic hydrolysis are consumed by other microorganisms, associated with hydrolytics. Additional difficulty is caused by accumulation of enzymes immobilized on soil particles; functionally they are completely independent of microbial biomass. The other possible mechanism of uncoupling between microbial process and microbial growth are co-metabolic reactions and production of secondary metabolites, like antibiotics, toxins, some pigments etc. For these situations a kinetic approach may be used. However,
230
N.S. Panikov,
a completely different should be developed.
M.V. Sizova
theoretical
I Journal of Microbiological
background
Acknowledgements This work was partly supported by the International Science Foundation, Grant No MIQ 000, and the Russian Fund for Fundamental Research, Grant No 93-04-7202.
References [l] Akimenko, V.K., Trutko, SM., Medentsev, A.G., Korobov, V.P. (1983) Distribution of cyanide-resistant respiration among yeasts and bacteria and its relation to oversynthesis of metabolites. Arch. Microbial. 136, 234-241. [2] Anderson, J.P.I. and Domsch, K.H. (1973) Quantification of bacterial and fungal contribution to soil respiration. Arch. Mikrobiol. 93, 113-127. [3] Anderson, J.P.I. and Domsch, K.H. (1978) A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215221. [4] Dedysh, S.N., Paleyeva, M.V. and Panikov, N.S. (1991) A kinetic method for the determination of algal biomass in soil. Microbiology (Engl. transl.) 60, 387-394. [51 Jenkinson, D.S. and Ladd, J.N. (1981) Microbial biomass in soils: measurement and turnover. In Soil Biochemistry, Vol. 5 (ed. by E.A. Paul and J.N. Ladd), 415-471. Marcel Dekker, New York. 161 Jenkinson, D.S. & Powlson, D.S. (1976) The effect of biocidal treatments on metabolism in soil. V. A method for measuring soil biomass. Soil Biol. Biochem. 17, 57-63. [71 Ingham, E.R., Griffiths, R.P., Cromack, K. & Entry, J.A. (1991) Comparison of direct vs fumigation incubation microbial biomass estimates from ectomycorrhizal mat and non-mat soils. Soil Biol. Biochem. 23,465-471.
Methods
24 (1996) 219-230
PI Mirchink, T.G. and Panikov, N.S. (1985) Modern approaches for determining the biomass and productivity of fungi and bacteria in soil. Adv. Microbial. (Usp. Mikrobiol.) 20, 198-226 (in Russian). 191 Paleyeva, M.V., Svetlov, S.V. and Panikov, N.S. (1990) Measurement of the biomass of soil microorganisms utilizing molecular hydrogen. Microbiology (Engl. transl.) 59, 344-349. [lOI Panikov, N.S. (1991a) Kinetics of Microbial Growth: General Principles and Ecological Applications. Nauka Publ., Moscow. ill1 Panikov, N.S. (1991a) A synthetic chemostat model as a means of describing the complex dynamic behavior of microorganisms. Microbiology (Engl. transl.) 60, 299307. [=I Parkinson, D. and Coleman, D.C. (1991) Microbial communities, activity and biomass. Agricult., Ecosyst. Envir. 34: 3-33. [I31 Payne, W.J. (1970) Energy yields and growth of heterotrophs. Annu. Rev. Microbial. 24, 17-52. P41 Popov, A.I. and Panikov, N.S. (1990) A kinetic method for the measurement of the numbers of nitrifying bacteria in soil. Microbiology (Engl. trans.) 59, 482486. P51 Petersen, S.O., Henriksen, K., Blackburn, T.H., and King, G.M. (1991) A comparison of phospholipid and chloroform fumigation analysis for biomass in soil: potentials and limitations. FEMS Microbial. Ecol. 85, 257-268. Ml Pitt, S.J. (1975) Principles of Microbe and Cell Cultivation. Blackwell Science, London. growth-reP71 Schmidt, SK. (1992) A substrate-induced sponse method for estimating the biomass of microbial functional groups in soil and aquatic systems. FEMS Microbial. Ecol. 101, 197-206. [18] Tempest. D.W. and Neijssel, O.M. (1984) The status of YATP and maintenance energy as biologically interpretable phenomena. Ann. Rev. Microbial. 38, 459-486.