international journal of livestock research issn 2277-1964

International Journal of Livestock Research ISSN 2277-1964 ONLINE www.ijlr.org Vol 3(2) May’13

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Rumen Microbial Biomass Synthesis and Its Importance in Ruminant

Production

T. Thirumalesh* and U. Krishnamoorthy

Department of Animal Nutrition, Veterinary College, Bidar-585 40

*Corresponding author: [email protected] Rec.Date: Nov 09, 2012 03:21; Accept Date: May 02, 2013 08:13

Abstract

Quantification of rumen microbial biomass synthesis assumes significance as rumen fermentation of feed

organic matter and microbial biomass synthesis meet 70-85% of energy and 70-100% of protein needs of

ruminants which necessitates understanding the feed factors influencing higher microbial biomass in the

rumen. Determination of feed factors affecting microbial biomass synthesis in vivo is too expensive.

Hence, the rumen in vitro gas production technique used for feed energy evaluation in ruminant feedstuffs

was adopted to assess microbial biomass production potential of feedstuffs based on the concept of

partitioning of fermented organic matter between microbial biomass and fermentation waste products.

Diets formulated with differing in microbial biomass synthesis termed as partitioning factor (PF) and

tested them for their efficiency of microbial biomass synthesis in vitro and assessed the same in vivo on

feed intake, nutrient digestibility, nitrogen balance, purine excretion in urine and production performance

in growing and lactating animals is compiled in this review.

Key words: Microbial biomass synthesis, predicting equations, in-vivo, in-vitro, purine derivative

excretion

Introduction

Optimization of crop residue utilization in ruminant production is thrust area of research because of their

poor nutritional qualities due to the presence of refractory and inhibitory substances. Quantification of

their impact on rumen fermentation characteristics, microbial biomass synthesis and fibre digestion

continues to be the problem. As the importance of feed constituents affecting rumen fermentation

characteristics and microbial biomass synthesis was realized (ARC, 1984; NRC, 1988; NRC, 2001),

search for new techniques for feed evaluation continued. In this direction, rumen in vitro techniques are

considered relatively simple and economical with many applications such as feed energy evaluation,

rumen fermentation characteristics and microbial biomass synthesis (Krishnamoorthy et al., 2005).

The rumen fermentation of feed organic matter and the microbial biomass synthesis are reported to have

the potential to meet 70 to 85% of the energy needs and 70 to 100% of the protein needs of ruminants,

even at higher levels of production. Determination of feed factors affecting microbial biomass synthesis

in vivo is too expensive. Therefore, application of this concept in practical diet formulation would be

difficult unless simple alternative techniques are developed. In this context, the RIVGPT used for feed

mailto:[email protected]


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energy evaluation in ruminant feedstuffs was adopted to assess microbial biomass production potential of

feedstuffs based on the concept of partitioning of fermented organic matter between microbial biomass

and fermentation waste products as proposed by BlÜmmel et al. (1997). In this context, nariK and

Krishnamoorthy (2007) have established in vitro microbial biomass synthesis indices, termed as

partitioning factor (PF) for commonly used Indian feedstuffs. If the variation in PF of feedstuffs can be

exploited by formulating diets to influence microbial efficiency, it can contribute to increased efficiency

of feed utilization.

Rumen microbes

The rumen microbial biomass is a consortium of bacteria, ciliate protozoa, flagellate protozoa,

phycomycete fungi, amoebae and bacteriophages. The bacteria accounts for about 50% of the total

microbial mass (1010

-1011

cells / g of rumen contents), protozoa represents 40% (105 – 10

6 cells/g of

rumen contents ; Van Soest, 1994) and fungi about 8% of the microbial biomass (105-10

7 cells/g of rumen

contents; Orphin, 1981). The diverse microbiota of rumen forms the key link between ruminants and diet

through the production of metabolic wastes (volatile fatty acids and gases) and microbial protein.

Nutritional significance of microbes in ruminants

In ruminants, microbial fermentation can supply 70 to 100% of amino acid requirement in the form of

microbial protein (AFRC, 1992), and 70 to 85% of energy requirement in the form of short chain fatty

acids (SCFA) (Dewhurst et al., 1986). Since protein requirement for milk or growth is obtained from the

microbial protein synthesized in the rumen and the undegraded dietary protein (UDP), high microbial

protein production in the rumen can decrease the need for undegraded dietary protein. Further, a

proportionally high carbon fixation into microbial cells can reduce fermentable carbon losses in the form

of carbon dioxide and methane (Leng, 1993 and BlÜmmel et al., 1999). Rumen microbial biomass

contributes hydrolytic enzymes for fiber digestion, in addition to serving as a source of amino acids for

tissue protein synthesis.

Composition of rumen microbes

The microbes that flow from the rumen to the lower tract represent a major portion of the animal’s diet

and the largest part of the protein nitrogen supplied to the animal. The indigestible portion of rumen

bacteria is the cell wall membrane, which is composed of peptidoglycon which contains about 7%

nitrogen and represents an average of about one third of the total microbial nitrogen. Microbial cells

contain, in addition, about 10% nucleic acids, which leaves, roughly one half to two thirds of the


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microbial nitrogen in the form of free protein (Table 1).The amino acid profiles of bacterial and protozoal

protein isolated from sheep rumen compared with amino acid profiles of casein is given in Table 2.

Neocallimastix patriciarum and Pyromyces communis contained, respectively, 24% and 30% protein by

dry weight. The amino acid profiles of the two fungi were similar, and compared favorably with casein

and Lucerne protein (Kemp et al., 1985).

Table 1. Composition of rumen bacteria and protozoa (%DM)

Constituent Bacteria Protozoa

Total – N 5 -12.4 3.8-7.9

True protein 38 -55 -

RNA 24.2 -

DNA 3.4 -

Lipid 4 -25 -

Polysaccharide 6 -23 -

Peptidoglycon 2 -

N-digestibility 44-86 76-85

(Van Soest, 1994)

Microbial efficiency or yield

Microbial efficiency is defined as proportion of substrate energy fixed into cells. Therefore, efficiency is

inversely related to production of SCFA. Microbial yield determines the microbial protein available to the

animal and the potential for the use of non protein nitrogen by rumen fermentation. The net metabolizable

protein received by the animal is the sum of true digestible microbial protein and feed protein escaping

the rumen. The microbial yield is expressed as grams of cells per mole glucose (Y glucose) or per 100 g of

fermented feed. The microbial yield per unit of glucose is divided into two components: the ATP yield

and YATP. The ATP yield is the moles of ATP formed from the fermentation of one mole glucose, and

YATP is grams of microbial cells formed from one mole ATP.

The yield and source of ATP for mixed rumen organisms is not known exactly, although it is well defined

for individual species grown in pure culture. It is thought that the synthesis of one mole of acetate yields

two ATP, and a mole of propionate via the succinate route, three ATP, and via the acrylate route one

ATP. Butyrate formation may produce two ATP per mole, and the formation of methane produces one

ATP.

Variations in Y ATP range from zero (all ATP used for microbial maintenance) to 30 and above (Van

Soest, 1994; Russel and Wallace, 1996). Microbial efficiency expressed as gram microbial protein (MP)

per MJ of ME intake was approximately 10 g (AFRC, 1992) and 7 to 14 g (Lebzien, 1996). Stern et al.


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(1994) reported microbial N as g microbial N / kg truly degraded organic matter (TDOM) as 30 whereas,

BlÜmmel and Lebzien (2001) expressed microbial efficiency as g microbial N per kg fermentable organic

matter (FOM). Microbial efficiency expressed as g microbial nitrogen per kg dry matter, organic matter,

total carbohydrate and 100 g N digested ranged from 20.8 to 26.6; 34.7 to 44.3; 35.1 to 50.3 and 89 to 95

g, respectively (Sniffen et al., 2006). The supply of microbial protein to the animal per unit of feed

ingested, usually expressed as g microbial N / kg digestible organic matter fermented in the rumen

(DOMR), varied by almost 4 folds ( 14 to 60 g N / kg DOMR; ARC, 1984).

Factors affecting microbial biomass synthesis

Dietary factors

Amino acids and peptides are stimulatory to growth rate and growth yield for ruminal microorganisms

growing on rapidly degraded energy sources (Chen et al., 1987; Cruz Soto et al., 1994). When peptides

were supplied with rapidly or slowly degraded fiber, microbial growth was enhanced only if the fiber was

degraded rapidly (Chikunya et al., 1996). The minimum contribution to microbial N from NH+

4 was 26%

when high concentrations of peptides and amino acids were present. Higher rumen degradable protein

decreased microbial protein flow to the small intestine (Santos et al. 1998).The asynchronous release of

ammonia and energy in the rumen results in inefficient utilization of fermentable substrates and reduced

synthesis of MCP. A value of ≥ 7.8 g RDN/ MJ ME would be sufficient in the feedstuff for the rumen

microflora to make full use of the readily fermentable carbohydrate (ARC, 1984).

MCP passage to the duodenum of lactating cows was highest (3.0 kg/d) when starch (barley) and protein

degradability (cotton seed meal) were synchronized for fast rates of digestion. Flows of MCP were lower

when the primary fermentable CHO and protein sources were either synchronized for slow degradability

(milo and brewer’s dried grains; 2.14 kg/d) or asynchronized (barley and brewer’s dried grains or milo

and cotton seed meal; 2.64 and 2.36 kg/d, respectively) (Herrea-Saldana et al. 1990). Average microbial

biomass yield for different classes of diets show a marked variation (Table 3). Silages and high

concentrate diets showed lower yield than do forages or mixed diets. Microbial biomass yield from

purified diets are highly variable which is probably due to quality of the diets. Diets contained 31 or 39%

NSC and 11.8 and 13.7% RDP in diet DM supported greater MCP synthesis than a diet contained 25%

NSC and 9% RDP (Stokes et al. 1991). The efficiency of MCP synthesis was 11 to 20% greater in sheep

given diets with CHO source (barley) synchronous with rapeseed meal than in asynchronous with urea

(Sinclair et al. 1995). MCP flow to the duodenum was increased by an average of 10%, when slowly

degradable sources of starch (Corn grain) were replaced by more rapidly degraded starch (Barley)


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Table 2. Amino acid composition (g AA/100g protein) of bacterial , protozoal

protein isolated from sheep rumen¶ and casein

#.

Bacteria Protozoa Casein

Threonine 5.37 5.07 4.9

Valine 5.49 5.24 7.2

Isoleucine 4.68 5.80 6.1

Leucine 6.47 7.18 9.2

Phenylalanine 3.98 5.29 5.0

Histidine 1.49 1.79 3.1

Lysine 6.99 10.14 8.2

Arginine 4.09 4.58 4.1

Methionine 1.78 12.62 2.8

Aspergine 12.10 12.62 7.1

Serine 4.24 4.10 6.3

Glutamine 11.98 13.81 22.4

Glycine 4.85 3.61 2.0

Alanine 6.12 3.48 3.2

Tyrosine 3.94 4.49 6.3 ¶ Buttery, (1981);

# Webb et al. (1998)

Table 3. Microbial biomass yield (g / kg TDOM)

Range Mean

Purified diets

Mixed diets

Silage diets

Concentrates

Forages

150-500

100-470

110-310

130-260

160-490

337

251

189

211

303

(Van Soest,1994)

(Sauvant and van Milgen, 1995). The diet with greater concentrations of RDP (12.4%) and NSC (40.6%)

had greater microbial N-flow (P<0.03) than that of low RDP (7.9%) and NSC (32.2%). Diets with similar

rates of RDP and higher rates (7.94% /h) of NSC degradation in the rumen showed higher MCP flow to

the duodenum (Lykos et al. 1997).

There was an increase of 22.1 g of microbial protein per 100g increase in fermentable organic matter

(Hagemeister et al., 1981). Where as 10 % more microbial protein synthesis was observed in cows with

ME intakes of 170 MJ ME / d than the cows with intakes of 140 MJ ME /d. On an average 22g of MCP

per 100g organic matter apparently degraded in the rumen (OMADR) was observed in lactating cows

when proportion of roughage was reduced from 40 to 32 %. High proportion of roughages in the ration

(extremely low energy) yielded only 15 to 20 g MCP/100g OMDR, where as with high levels of

concentrates in the ration yielded 14 to 18 g MCP/ 100g OMADR (Hagemeister et al., 1981). Heifers


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receiving higher proportion of roughage (80 %) with similar total DMI, microbial N flow of 11.8 to 14.7g

per kg DOMI was reported (Darshan et al., 2007). The microbial N synthesis was very low (21.9 and 13.3

g/kg FOM) in diets with maize compared with diets with barely (33.9 and 30.72 g /kg FOM) (Oldham et

al.1979). The concentrate supplement comprised 91.5 % wheat bran and 6 % GNC yielded 14.7 g MN/kg

DOMI, whereas concentrate supplement formulated with 87.6 % maize and 8.4 % GNC yielded 11.8 g

MN/kg DOMI in heifers fed adlibitum finger millet straw (Darshan et al., 2007). Microbial biomass yield

for different diets is presented in Table 2.7.

Improved microbial protein synthesis (20-30%) was noticed in cows when frequency of feeding was

increased, on contrary, increased feeding frequency of high roughage rations did not result in increased

amount of protein at the duodenum in sheep (Tamminga, 1979). Similarly, when mid lactation dairy cows

were provided diets that varied in rumen degradable OM and CP or fed at different feeding frequencies,

no differences were observed in MCP production or microbial efficiency (Shabi et al., 1998).

Some of the naturally occurring plant components like polyphenols and detergents have the potential to

modulate rumen fermentation towards maximizing microbial biomass synthesis. The decreased rate of

digestion of feedstuffs by tannins could help in synchronized release of various nutrients which in turn

might be responsible for increased microbial efficiency (Makkar et al., 1998). The higher molar

proportion of propionate in the in vitro fermentation system and lower protozoal counts produced by

tannins lead to higher microbial protein synthesis (Makkar, 2003). Saponins facilitate higher proportion of

the substrate degraded towards microbial mass production and lower proportion towards gas production.

Saponins can also shift the carbon from waste products of fermentation such as carbon dioxide and

methane to microbial mass (Makkar et al. 1998).

Microbial factors

On an average protozoal protein accounts for 20-25% of the microbial protein at the duodenum (Ling and

Buttery, 1978; Harrison et al., 1979) or 11% of total CP flow to the small intestine (Shabi et al., 2000),

although experiments with defaunated sheep show an increase of 20% in rate of bacterial protein

synthesis (Lindsay and Hogan, 1972), this effect is of minor importance because; i) the variation of the

protozoal count of cattle on normal rations is only of a very small magnitude and ii) only the proportion

of protozoal to bacterial protein at the duodenum is altered, and not the total supply of microbial protein.

Composition of microbial cells also influences the microbial efficiency. Direct storage of lipid and

carbohydrates is much more efficient energetically than biosynthesis and storage of protein. Storage

rather than synthesis will increase microbial efficiency, when expressed as grams of dry cells per ATP.


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But, when expressed as grams of protein synthesized per unit of organic matter truly fermented, the

microbial efficiency decreases with storage of carbohydrates, because ATP is used to polymerize

carbohydrates (Yokoyama and Johnson, 1979). Storage of carbohydrates will lower the nitrogen content

of organisms and is probably the major factor influencing variation in protozoal nitrogen (Weller, 1957).

Interaction between various microbes also play a role in influencing biomass synthesis in the rumen.

Ammonia is a preferred nitrogen source for many rumen bacteria. Some species, which require ammonia

such as the cellulolytics, must depend on cross-feeding interactions with either proteolytic or ureolytic

species, which produce ammonia. Release of peptides and amino acids from proteins by protozoa and the

more proteolytic bacterial species continually replenish these nitrogen sources required by other bacterial

species. Such interaction could help in production of more microbial nitrogen (Van Soest, 1994). The

effective removal of H2 by methanogenic species encourages important H2 producing species such as

Ruminococcus albus, R. flavefaciens, Selenomonas ruminantium to produce more H2 and thus, alter their

metabolism towards high energy yielding pathways. When R. albus is grown in the presence of

methanogen in co-culture, the end products are only acetate and CH4. These results indicate that CH4

production rather than being a wasteful process to ruminants would promote a more efficient fermentation

and higher yields of ATP synthesis by keeping the H2 concentration low in rumen (Wolin and Miller,

1988). The higher yield of ATP results in synthesis of more microbial cells, which increases the available

protein to the ruminants. Increasing the available protein to ruminants in the rumen will increase

microbial protein synthesis leading to higher microbial biomass synthesis.

Feed evaluation for microbial biomass synthesis

The ruminant feedstuffs can be evaluated for their microbial biomass synthesis efficiency by prediction

equations, in vivo by means of marker and labeling systems and in vitro through controlled fermentation

studies (Van Soest, 1994).

a. Prediction of microbial-N flow to the duodenum

In NRC (1988), bacterial crude protein (BCP) production in lactating dairy cows was predicted from net

energy intake using the equation: BCP=6.25(-30.93 + 11.45 NEL). For growing animals, BCP was

predicted from TDN intake using the equation: BCP=6.25(-31.86 + 26.12 TDN). NRC (1996) for Beef

cattle adopted TDN intake to predict BCP flow to the lower tract (130g BCP/kg of TDN intake). NRC

(1985) proposed an equation MCP, g/day = 10.6 MEI – 193 to predict MCP flow to the lower tract

whereas, the Germen protein system for dairy cattle proposed by Rhor et al. (1986), predicts MCP by an

equation MCP (g/day) = 11.92 MEI (MJ/d) – 15 DMI (kg/d).


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Oldick et al. (1999) has developed equations based on NEL intake, DMI and dietary NEL concentration,

DMI and measured diet composition, ruminal organic matter digestion and ruminal carbohydrate

digestion to evaluate NRC equations to predict microbial-N flow to the duodenum in lactating cows

(Table 4). These equations are improvements over the current NRC (1988) equation. Separate equations

were not needed to predict microbial-N flow for lactating cows verses non-lactating cattle. ARC (1984)

defined the average values for microbial growth efficiency based on specific feeding situations for well

balanced mixtures of roughages and concentrate feed, diets consisting solely of grass silage and

combinations of grass silage and concentrations (8.4, 6.25 and 8.75 g MCP/ MJ ME, respectively). The

rumen microbes’ ability to synthesize MCP in relation to fermentable ME intake (FME), g MCP per MJ

of FME is recognized to be variable, in particular with plane of feeding. AFRC (1992) suggested MCP

yield for different levels of animal performance viz., all animals at maintenance level of feeding, growing

sheep and cattle (2 x M), late pregnancy or lactating ewes and lactating dairy cows (3 x M) were 9,10 and

11g MCP / MJ of FME . At the average NEL intake of 17.4 Mcal/d, NRC (1985) equation predicts N-

flow to be 168 g/d, equation (2) and (3) in Table 4 predict microbial-N flow to be 155 to 128g/d,

respectively. Prediction based on equations (2) and (3) represent reduction of 7.7 and 23.8% respectively,

relative to the NRC (1985) prediction.

Table 4. Best fit equations for the prediction of microbial N flow (g/d) to the duodenum of cattle

(lactating and non lactating).

Sl. No.

Equation

Reference

1

MN (g/d)= -30.93+11.45x NEL I (Mcal/d)

NRC (1988)

2 MN (g/d)= -20.6+11.6x NELI (Mcal/d)- 0.0867x NEL I (for

cattle not fed supplemental fat)

Oldick et al. (1999)

3 MN (g/d)= 5.52+7.16x NEL I (Mcal/d)- 0.0055x NEL I (for

cattle fed supplemental fat)


4 MN (g/d)= -32.9+19.7x DMI (kg/d)- 0.245x DMI

(lactating and non lactating)


5 MN (g/d)= 16.1+22.9x DMI (kg/d) – 0.365x DMI- 1.74x

NDF (% of DM)


6 MN (g/d)= -13.8+39.8x TOMDR (kg/d)- 1.01x TOMDR Oldick et al. (1999)

7 MN (g/d)= -149+108x CHODR (kg/d)- 7.41x CHODR Oldick et al. (1999)


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MN, Microbial nitrogen; NELI, Net energy for lactation intake; DMI, Dry matter intake; NDF, Neutral

detergent fiber; TOMDR, Truly organic matter digested in the rumen; CHODR , Carbohydrate digested in

the rumen.

b. In vivo evaluation

i. Use of microbial markers

Since metabolizable protein available at the duodenum comprises microbial protein and dietary protein,

microbial protein must be distinguished from that of dietary origin to quantify the protein of microbial

origin. The separation of microbial from dietary protein has been accomplished by means of use of

internal microbial markers (Ling and Buttery, 1978) such as diaminopimelic acid (DAPA - an amino acid

found only in bacteria), nucleic acids (RNA, DNA, individual purines and pyrimidines or total purines),

amino ethyl phosphonic acid (AEPA - an amino acid found only in protozoa) and the external isotopic

markers like 35

S, 32

P or 15

N (Sadik et al., 1990; Broderic and Merchen, 1992). Total purines represent

robust microbial markers that should be adaptable by most investigators. Principal concerns about total

purines relate to unequal purine:N ratios in protozoal and bacterial pools and to the need to assume that

dietary purines are completely degraded in the rumen (McAllan and Smith, 1973). A theoretically

sounder, but more costly method is continuous intraruminal infusion of 15

N ammonium salts. However,

15N enrichments of bacterial and protozoal pools are not equal, so that basis for calculating microbial

yield is uncertain. If the ratio of RNA- Nitrogen to total nitrogen is constant in rumen bacteria, it is

possible to calculate the amount of bacterial protein synthesized. In addition to these uncertainties,

requirement of post-ruminally cannulated animals, complicated and error-associated procedures to

determine digesta flow are major limitations in these methods. Hence, urinary purine excretion may prove

to be a noninvasive method for estimating microbial protein yields in intact dairy cows (Broderick and

Merchen, 1992).

ii. Quantification of purine derivatives in urine

Purines are heterocyclic rings with nitrogenous bases with varying functional groups. The purine bases,

adenine and guanine are found in both DNA and RNA. Microorganisms have high concentrations of

purine containing compounds (RNA and DNA) relative to concentrations in plant and mammalian cells.

Furthermore, rumen microbes in general, rapidly degrade purines in diets. They are, therefore, likely to be

present in only negligible amounts in digesta leaving the rumen. The purines present in digesta entering

the small intestine is, therefore, almost totally of microbial origin. These purine components are then

metabolized in ruminants to form purine derivatives (PD) such as xanthine, hypoxanthine, uric acid and

allantoin that are excreted, mainly in the urine. All four components are found in urine of sheep, goats,


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red deer and llamas, but only allantoin and uric acid are present in cattle and buffalo urine. With estimates

of daily urine volume and urinary concentration of PD, the total daily urinary excretion of PD can be

determined and used to predict the rates of absorption of purines from the small intestine using

relationships developed for individual ruminant species, since only urine is needed, an estimation of

microbial protein supply can be incorporated into nitrogen balance and digestibility trials without much

additional labour inputs. The advantages of this technique are its non-invasive nature and relative ease of

use.

Lindberg (1985) noted that allantoin excretion in urine of goats was highly correlated with the intake of

digestible organic matter and suggested that the allantoin excretion in urine could be used as an index of

microbial protein synthesis in the rumen. Similarly, allantoin excretion was positively correlated with the

digestible dry matter intake in buffaloes (Bos bubalis), but compared with the cattle, the excretion was too

low (Liang et al., 1994; Chen et al., 1996). Research on purine derivatives was extended to several local

cattle species, a linear correlation between PD excretion and DOMI was observed in all animals (IAEA,

1997). The measurement of PD in urine of sheep, cattle and other species except buffaloes will continue

to serve as an indicator of microbial protein supply in ruminants and as a useful tool to aid understanding

how various dietary factors affect rumen microbial protein production.

c. In vitro evaluation

In vivo feeding trials are of course expensive and tedious and so unsuitable for routine roughage

evaluations. In many circumstances they are impractical, since the material to be tested is only available

in small quantities especially in plant breeding programmes. Therefore, a strong demand for laboratory

methods to evaluate feeds for microbial biomass synthesis efficiency.

i. Total purine quantification (RNA equivalent)

The method of Zinn and Owens (1986), based on release of purine bases by HClO4 followed by their

precipitation with AgNO3, was used to study recovery of purines from lyophilized rumen microbial or

Escherichia coli preparations added to matrices such as cellulose, starch and neutral detergent fiber. The

recovery of purines from the rumen microbial preparations measured using HPLC method was 95-102%

but internal standards (adenine and allopurinol) used in HPLC were heat labile. Hence, Makkar and

Becker (1999) have proposed changes in hydrolysis conditions for accurate determination of purine bases

by spectrophotometeric method using Torula yeast RNA as the standard.


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Purine concentrations of pellet obtained after incubation of feeds (SBM, MG, LH, OBH, MCR and 5

diets) with rumen liquor showed higher concentration with maize grain and lowest with oat-berseem

clover hay, except for SBM where guanine concentration was higher than adenine concentration. Purine

yield per unit ATP produced (µmol/mmol) from the incubation of 500 mg substrate ranged from 1.28 in

SBM to 2.56 in MG. There was no significant relationship between purine yield per unit ATP produced

and EMP in vivo either across diets and roughages or within diets and roughages. The relationship tended

to be inverse (P≤0.22) for the roughages (BlÜmmel et al., 2003).

ii. Partitioning factor (PF)

Partitioning factor is an indicator of high efficiency of microbial protein, this can be estimated by simpler

in vitro technique, which consists of a combination of two in vitro measurements in one 24.0 h incubation

using rumen inoculum, where the ratio of mg of OM truly degraded to ml of gas produced thereby is

determined (BlÜmmel et al., 1997). Gas volume produced during the incubation is recorded as described

by Menke et al. (1979) and the substrate truly degraded is gravimetrically quantified by the modification

of the technique of Tilley and Terry (1963) as suggested by Goering and Van Soest (1970). The

degradability measurement reflects how much substrate was used for the formation of all products of

fermentation, namely short-chain fatty acids (SCFA), gases and microbial biomass. The gas measurement

indicates how much substrate was used for the formation of SCFA and gases. Since SCFA and gas

production are very closely associated stoichiometrically (Wolin, 1960; BlÜmmel and Ørskov , 1993;

BlÜmmel et al., 1999c) with a stoichiometric factor ranging from 2.20 in roughages to 2.34 with cereal

grains, the substrate conversion efficiency into microbial biomass can be calculated as (PF – SF)/ PF.

Therefore, the feedstuffs with higher PF are regarded as those promoting higher efficiency of microbial

biomass synthesis (EMBS) (BlÜmmel and Lebzien, 2001).

The PF values were determined after 24.0 and 16.0 h of incubation as well as at t½ (the time at which

half of the maximum gas production was achieved). Preliminary data about kinetics of in vitro gas

production and microbial biomass yield had suggested that t½ could serve as a common time

denominator to facilitate across substrate comparison of PF values (BlÜmmel et al., 1999 b). However,

the PF for a given feedstuff can vary with the incubation time partly because of the dynamics of microbial

growth. PF values measured at t ½ and 16 h of incubation, result in an over estimation of in vitro true OM

degradability and consequently the PF value due to incomplete fermentation of truly degraded substrates

(BlÜmmel et al., 2003). Whereas, 24.0 h findings is a distortion of PF measurement through secondary

fermentation of lysed microbial cells in to SCFA and consequently, of the gases after microbial peak yield

(BlÜmmel and Ørskov, 1993; Cone et al., 1997). Thus, the magnitude by which these factors influence PF


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would depend on feed characteristics and microbial growth. Therefore, BlÜmmel et al.(2003)

recommended that the incubation time for feedstuffs should be substrate specific at half asymptotic gas

production (t ½ ).

Kiran et al. (2007) suggested for particular incubation time to suit all feedstuffs with an ultimate objective

to meaningfully relate the in vitro microbial efficiencies of the feedstuffs or the mixed diets to in vivo

efficiencies, therefore the recommended PF determination at 24 h or 16 h in addition to t ½ , until the

advantage of one over the other is proven with more evidences.

Microbial efficiency of feedstuffs

The efficiency of microbial production in vitro for ruminant feedstuffs estimated by partitioning factor

(PF, ratio of mg of OM truly degraded to ml gas produced thereby) is given in Table 5.

Associative effects of feed ingredients on microbial biomass synthesis

Based on PF values of individual feed ingredients and roughages, diets of various combinations of feed

ingredients can be formulated to achieve difference in efficiency of microbial protein synthesis among

diets. This will help in formulating suitable diets to have higher microbial flow to the duodenum. Kinetics

of gas production and PF values of various diets achieved by summation of the respective PF values of

the feed components according to their proportion in the diets were tested by in vitro (BlÜmmel et al.,

2003).

Table 5. Partitioning factor (PF) of feedstuffs measured at time of asymptotic gas production (t1/2)

from 500 mg substrate incubation.

Feedstuffs Partitioning factor Reference

Roughages Finger millet straw 3.05

3.66

Krishnamoorthy et al. (2005)

Darshan et al (2007)

Rice straw 2.93 Kiran and Krishnamoorthy (2007)

Bengal gram straw 3.31 Biradar et al. (2007)

Black gram straw 3.74 Biradar et al. (2007)

Green gram straw 3.49 Biradar et al. (2007)

Red gram straw 2.75 Biradar et al. (2007)

Bengal gram husk 2.52 Krishnamoorthy et al. (2005)

Lucerne hay 4.55 BlÜmmel et al. (2003)

Oat berseem clover hay 3.79 BlÜmmel et al. (2003)

Maize crop residue 2.64 BlÜmmel et al. (2003)

Hybrid maize stover leave 3.10 BlÜmmel et al. (1999b)

Local maize stover leave 3.30 BlÜmmel et al. (1999b)

Rye grass silage 2.96 BlÜmmel et al. (1999a)

Sugar cane bagasse 2.79 Krishnamoorthy et al. (2005)


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Tamarind seed hulls 3.94 Krishnamoorthy et al. (2005)

Protein supplements

Ambadi cake exp. 6.48 Kiran and Krishnamoorthy (2007)

Coconut meal s.e 3.92 Kiran and Krishnamoorthy (2007)

Cotton seed meal s.e 3.86 Kiran and Krishnamoorthy (2007)

Gingly cake exp. 5.71 Kiran and Krishnamoorthy (2007)

Groundnut meal s.e 5.97 Kiran and Krishnamoorthy (2007)

Rapeseed meal s.e 6.31 Kiran and Krishnamoorthy (2007)

Soybean meal s.e 5.69

5.21

Kiran and Krishnamoorthy (2007)

BlÜmmel et al. (2003)

Sunflower meal s.e 4.85 Kiran and Krishnamoorthy (2007)

Groundnut cake 5.86 Darshan et al. (2007)

Chunnies

Bengal gram 2.67 Biradar et al. (2007)

Black gram 3.23 Biradar et al. (2007)

Green gram 3.17 Biradar et al. (2007)

Red gram 3.80 Biradar et al. (2007)

Energy supplements

Fox tail millet 4.04 Kiran and Krishnamoorthy(2007)

Finger millet 3.28 Kiran and Krishnamoorthy(2007)

Jowar, red 3.98 Kiran and Krishnamoorthy(2007)

Jowar, white 4.53 Kiran and Krishnamoorthy(2007)

Maize 3.38

3.39


Darshan et al. (2007)

Rice polish 4.26 Kiran and Krishnamoorthy (2007)

Tapioca peel 3.65 Kiran and Krishnamoorthy(2007)

Tapioca meal 4.10 Kiran and Krishnamoorthy(2007)

Wheat bran, coarse 4.07 Kiran and Krishnamoorthy(2007)

Wheat bran, fine 3.78

3.93


Darshan et al. (2007)

The mean potential of gas production (D) and rate of gas production (k) values were 1.5 and 13% higher

than those calculated whereas t1/2 was about 13% less in the diets as compared to those calculated from

the dietary components. There was no significant difference between the PF values of calculated and

measured among the diets. Kiran and Krishnamoorthy, (2007) reported significant (P<0.01) increase in

associative effect of feedstuffs on k and PF values by 24.29% and 22.48%, respectively for mixed diets

and tended to be significant during the early incubations (6 and 12 hrs). Possibility of identifying

associative effect among the ingredients at t1/2 could be meaningful in feed compounding and diet

formulations to achieve higher microbial biomass synthesis. Darshan et al. (2007) found difference in the

PF of two concentrates formulated with minimum ingredient combinations between directly determined

and those calculated from the PF of the individual ingredients. The calculated PF were 3.97 and 3.48 for

higher partitioning factor concentrate and low partitioning factor concentrate, whereas directly determined


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were 4.16 and 3.73 respectively. Partitioning factor of diets made of various ingredient combinations is

presented in Table 6.

Table 6. Partitioning factor (PF) of diets formulated with various ingredient combinations

measured at time of asymptotic gas production (t1/2) from 500 mg substrate incubation.

Sl.

No.

Ingredient composition of diets (%) PF Reference

1 RGS-94.4, PM-5.6 3.15 BlÜmmel et al. (1999a)

2 RGS-90.8, SBM-4.5, PM-4.7 2.94 BlÜmmel et al. (1999a)

3 RGS-84, SBM-10.7, PM-5.3 2.82 BlÜmmel et al. (1999a)

4 RGS-79.9, SBM-14, PM-6.1 2.99 BlÜmmel et al. (1999a)

5 GS-44,CM-27.4,Wheat-15.1,Tapioca-12.9,soya, oil-0.56 2.93 BlÜmmel and Lebzien(2001)

6 GS-46,PKM-29.7,Wheat-11.9,Tapioca-22,soya, oil-0.54 2.83 BlÜmmel and Lebzien (2001)

7 GS-25.5, MS-24.5, Tapioca-15.5, soya oil-1,SBP-17,

ADE18-1, Citrus pulp-15.5, potato by-product-1.4

2.87 BlÜmmel and Lebzien (2001)

8 GS-55, Tapoica-7.4, soya oil-0.45, RSM-13.5, Barley-

13.5, SBH-9, MM-1.1


9 GS-53,Soyaoil-0.47,SBP-11.7,SBM-5.6,Barley-15,Oat-

14.1


10 GS-50, Wheat-10, Soya oil-0.5, SBP-6, WB-3.5, SBM-

6.5, Barley-22.5, MM-1


11 MCR-79, MG-5, SBM-16 3.10 BlÜmmel et al. (2003)

12 MCR-69, OBH-10.3, MG-8.3, SBM-12.4 3.19 BlÜmmel et al. (2003)

13 MCR-45, OBH-35, MG-15, SBM-5 3.36 BlÜmmel et al. (2003)

14 MCR-69.1, LH-10.3, MG-10.3, SBM-10.3 3.25 BlÜmmel et al. (2003)

15 MCR-45, LH-35, MG-20 3.70 BlÜmmel et al. (2003)

16 FMS-70, CSM-14, M-8, RP-4, WB-4 4.58 Kiran and Krishnamoorthy (2007)

17 FMS-70, SBM-12.4, M-13.6, RP-2, WB-2 4.86 Kiran and Krishnamoorthy (2007)

18 FMS-70, SFM-22.9, M-3.1, RP-2, WB-2 4.37 Kiran and Krishnamoorthy (2007)

19 FMS-70, GNM-13.5, M-7.5, RP-6, WB-3 4.83 Kiran and Krishnamoorthy (2007)

20 FMS-70, GGE-14, M-2, RP-12, WB-2 4.81 Kiran and Krishnamoorthy (2007)

21 FMS-70, RSM-16, M-5, RP-5, WB-4 4.95 Kiran and Krishnamoorthy (2007)

22 M-87.6,GNC-8.4,Urea-1.5,MM-1.5,Salt-0.5,Na2SO4-0.5 3.73 Darshan et al. (2007)

23 WB-91.5, GNC-6, MM-1.5, Salt-0.5, Na2SO4-0.5 4.12 Darshan et al. (2007)

24 BGS-50, BGC-48, MM-1, Salt-0.5, LP-0.5 2.72 Biradar et al. (2007)

25 BLKS-50, BLKC-48, MM-1, Salt-0.5, LP-0.5 3.55 Biradar et al. (2007)

26 GRGS-50, GRGC-48, MM-1, Salt-0.5, LP-0.5 3.59 Biradar et al. (2007)

27 RDGS-50, RDGC-48, MM-1, Salt-0.5, LP-0.5 3.14 Biradar et al. (2007)

RGS-rye grass silage; PM-premix; SBM-soybean meal; GS-grass silage; CM-coconut meal; PKM-

palmkernel meal; MS-maize silage; SBP-sugar beet pulp; WB-wheat bran; RSM-rapeseed meal; MM-

mineral mixture; MCR-maize crop residue; MG-maize grain; OBH-oat-berseem clover hay; LH-lucerne

hay; FMS-finger millet straw; CSM-cotton seed meal; M-maize; RP-rice polish; SFM-sunflower meal;

GNM-groundnut meal; GGE-gingley extraction; GNC-groundnut cake; BGS-bengal gram straw; BGC-

bengal gram chunni; BLKS-black gram straw; BLKC-black gram chunni; GRGS-green gram straw;

GRGC-green gram chunni; RDGS-red gram straw; RDGC-red gram chunni; LP-lime powder.


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Validation of PF through in vivo

The relationship between variations in in vitro microbial biomass production and microbial protein supply

to the host animal can be examined through total urinary purine derivative excretions or through use of

markers. The two groups of Malawian goats fed hybrid maize stover leave (HSL) and local maize stover

leave (LSL) with PF of 3.1 and 3.3 yielded significantly higher microbial biomass of 114.9 and 101.4 mg

respectively, which was supported by significantly higher purine derivative excretion in LSL fed

compared with the HSL fed group (BlÜmmel et al., 1999b). BlÜmmel et al.(1999a) found increased

microbial CP synthesis as the PF of the diet increases in an experiment on steers fed with four diets

having PF of 3.15, 2.94, 2.82 and 2.99 which correspondingly produced 187, 174, 155 and 149 g MCP/kg

effectively degradable organic matter intake. The efficiency of microbial protein synthesis (EMP) of nine

mixed diets consisting of approximately even parts of roughage and concentrate investigated both by in

vitro and in vivo showed significant positive correlation between PF values and EMP in vivo in an

experiment conducted on lactating cows (BlÜmmel and Lebzien, 2001). BlÜmmel et al. (2003) reported

significant relationship between PF at t1/2 and EMP measured in vivo in the diets varying PF from 3.10 to

3.70, fed to rumen and duodenum cannulated wether sheep.

In vivo verses in vitro microbial biomass yield

In vitro microbial efficiencies as estimated by PF value and in vivo microbial efficiencies as estimated by

renal purine derivatives or markers of various experiments conducted by different investigators are

presented in Table 7.

Table 7. Estimation of microbial efficiency of diets by in vitro (PF values) and in- vivo (Markers or

renal Purine derivatives) in different species.

An

imal

Die

t

PF

of

die

t

Met

hod

PD

e

(mm

ol/

d)

In vitro MN

yield

(mg/500mg

TDOM)

In vivo MN

yield (g/kg)

Reference

Goat* HSL 3.1 PDe 3.41 14.8 7.62£ BlÜmmel et al. (1999b)

LSL 3.3 PDe 3.91 16.9 9.0£ BlÜmmel et al. (1999b)

Steers 1** 3.15 PDe 148.5 16.3 29.9¶ BlÜmmel et al. (1999a)

2 2.94 PDe 154.8 13.3 27.8¶ BlÜmmel et al. (1999a)



Cattle 5 3.15 N15

- 10.2 32.5 Δ


6 2.83 N15

- 9.9 27.9 Δ


7 3.11 N15

- 10.1 34.6 Δ


8 3.15 N15

- 9.4 31.2 Δ


9 3.22 N15

- 10.4 32.9 Δ



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10 2.97 N15

- 9.8 30.7 Δ


11 2.99 N15

- 10.8 31.9 Δ


Sheep 12 3.10 Cr - 11.1 49.9¶ BlÜmmel et al. (2003)

13 3.19 Cr - 11.3 53.3¶ BlÜmmel et al. (2003)

14 3.36 Cr - 12.6 55.0¶ BlÜmmel et al. (2003)

15 3.25 Cr - 11.9 52.2¶ BlÜmmel et al. (2003)

16 3.70 Cr - 17.7 58.2¶ BlÜmmel et al. (2003)

LH 4.55 Cr - 20.7 47.2¶ BlÜmmel et al. (2003)

OBH 3.79 Cr - 13.6 48.5¶ BlÜmmel et al. (2003)

MCR 2.64 Cr - 4.5 38.6¶ BlÜmmel et al. (2003)

Bulls A*** - PDe 25.5 - 13.5¶ Shem et al. (1999)

B - PDe 35.6 - 15.2¶ Shem et al. (1999)

C - PDe 48.9 - 17.4¶ Shem et al. (1999)

Bulls D - PDe 22.9 - 12.9¶ Shem et al. (1999)

E - PDe 16.5 - 11.1¶¶

Shem et al. (1999)

F - PDe 24.1 - 13.8¶ Shem et al. (1999)

G - PDe 22.4 - 11.5¶ Shem et al. (1999)

H - PDe 30.8 - 14.1¶ Shem et al. (1999)

I - PDe 38.2 - 17.1¶ Shem et al. (1999)

J - PDe 17.2 - 11.2¶ Shem et al. (1999)

K - PDe 20.4 - 12.0¶ Shem et al. (1999)

L - PDe 31.5 - 14.6¶ Shem et al. (1999)

M - PDe 26.4 - 14.0¶ Shem et al. (1999)

N - PDe 5.6 - 5.5¶ Shem et al. (1999)

O - PDe 9.6 - 6.6¶ Shem et al. (1999)

In vivo microbial N yield:¶ - g/kg DOMR, £ - g/kg TDDMI, Δ – g/kg FOM, * SBM was included

at 7.5 and 10 % level in the diet of HSL and LSL fed groups, respectively ; ** Composition of diets from

1 to 16 is given in Table 2.10; LH-lucerne hay; OBH-oat-berseem clover hay; MCR-maize crop residue;

*** Maize stover varieties (A to F): A-green kilima; B-green Malawi; C-5% urea treated Malawi; D-3%

urea supplemented Malawi; E-dry Malawi; F-green Malawi maize tops; G-Canadian wonder bean straw;

H-Belabela bean straw; I-Guatemala grass; J-Setaria grass; K-Napier grass; L-green Rhodes grass; M-

Rhodes grass hay; N-banana leaves; O-banana pseudostem (From A to O residues were supplemented

with 200g cotton seed cake/d).

Effect of diets varying in microbial biomass synthesis on intake, nutrient utilization and productive

performance

Since PF is the index of efficiency of microbial production of ruminant feedstuffs, diets of higher PF

produce more of microbial biomass in rumen, which in turn influences the feed intake and utilization of

feed nutrients for higher productive performance of the animals. Roughages with high substrate

degradability but comparatively low gas production (higher PF) had higher DMI (BlÜmmel and

Bullerdieck, 1997; BlÜmmel et al., 1997). BlÜmmel et al. (1999b) recorded higher dry matter intake of

425 g/d in goats fed local maize stover leaves (PF-3.3 ) when compared to the goats fed hybrid maize

stover leaves (PF-3.1) with DMI of 416g/d. Johnson et al. (1998) reported DMI of 17.5 and 13.4 kg/d in


Pag

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lactating cows, which were fed the diets with microbial production efficiency of 208 and 124 g/d

respectively. Shem et al. (1999) noticed positive relationship between MNS and DMI and digestibility of

DM and OM ranged from 506 to 768 and 525 to 782 g/ kg in Bos taurus x Bos indicus bulls fed different

tropical feeds with the efficiency of microbial-N supply ranged from 5.5 to 17.48 g N/kg of DOMR. On

the contrary, the results obtained from the feeding experiments of lactating cows fed 9 diets varying PF

from 2.83 to 3.19 showed no significant relationship between DMI and efficiency of microbial production

(P= 0.408) and EMP (P= 0.323) (BlÜmmel et al., 2001). In another experiment, BlÜmmel et al. (1999a)

noticed OMI of 7.83, 9.56, 8.55 and 7.36 kg/day, and OMD of 82.0, 79.0, 74.2 and 69.7 % for the diets

with PF of 3.15, 2.94, 2.82 and 2.99, respectively. The decreased OMD with depressed PF value was due

to the date of harvest of rye grass silage at different stages of maturity, which was used as a component of

diets. BlÜmmel et al. (2003) fed three roughages, lucerne hay (LH), oat berseem clover hay (OBH), maize

crop residue (MCR) and five diets (Table 6) designed from three roughages (adjusted to isonitrogenous

by maize grain and soybean meal) to sheep. LH, OBH and MCR with PF of 4.55, 3.79 and 2.64

influenced daily DMI by 45.1, 31.1 and 16.9 g/ kg live wt. respectively whereas, in all the dietary groups

DMI increased as the PF of diets increased. Valadares et al. (1999) evaluated effect of replacing alfalfa

silage with 20, 35, 50 and 65% concentrate mixture with microbial production of 278, 366, 419 and 335

g/d on daily excretion of urinary urea-N in lactating Holstein cows. Urea-N excretion was 304, 342, 308

and 239 g/d in diet comprised 20, 35, 50 and 65 % concentrate feed respectively. Urea-N excretion

declined as dietary concentrate increased from 35 to 65%, indicated better utilization of microbial protein

synthesized in the rumen. Sniffen et al. (2006) reported improved weight gain and body condition score

during the experiment in cows fed diets with higher microbial-N efficiency (43.3g MN/kg DOM) than

diet lower in microbial-N efficiency (34.7g MN/kg DOM). However, there was no difference (P> 0.05)

between the groups on body weight change or body condition score change. He noticed total tract

digestibility of DM, OM, CP, ADF, NDF, hemicellulose , cellulose, EE, starch and NFC as 67.9, 68.1,

62.3, 47.4, 46.7, 45.2, 54.6, 80.6, 96.0 and 89.7% respectively, whereas, diets with higher microbial

efficiency fed group produced higher milk (P= 0.01) and fat yield (35g/kg) and energy corrected milk

tended to be higher (P= 0.07) in this group. Further, it had higher milk true protein yield (P= 0.04), and

milk lactose yield (P= 0.02) compared with cows fed the diets with lower microbial efficiency.

Darshan et al. (2007) reported non significant difference in DMI, weight gain and digestibility of DM,

OM, NDF and ADF except for CP (P< 0.05) between the two groups of heifers fed high partitioning

factor concentrate (4.12) and low partitioning factor concentrate (3.73) with FMS as a sole source of

roughage. Further, he recorded no significant difference in N-intake but there was significantly higher

(P<0.05) N-retention and urinary-N excretion in heifers fed with high partitioning factor concentrate


Pag

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supplement. It is likely that the differing PF of the diets influence N-retention. Similarly, diets formulated

to differ in microbial biomass synthesis did not influence the microbial protein supply to the duodenum,

as indicated by purine derivative excretion(PDe) in urine in lactating cows (Thirumalesh and

Krishnamoorthy, 2008) and growing ram lambs (Thirumalesh and Krishnamoorthy, 2009).

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