Equilibrium in the Gut Ecosystem for Productive Healthy Birds
Edgar O. Oviedo-Rondón, DVM, MSc., PhD., Dipl. ACPV
Prestage Department of Poultry Science, College of Agriculture and Life Sciences.
North Carolina State University, Raleigh, NC 27695-7608
Michael E. Hume, Ph.D.
USDA, ARS, SPARC, FFSRU. 2881 F&B Road, College Station, TX 77845
Abstract. Intestines of each bird are the niche of a complex and dynamic ecosystem with
important effects to the host. The members and final products of this ecosystem influence
nutrient digestion, absorption, mucosa metabolism, general physiology, and local and systemic
immunological responses of the avian hosts. A better understanding of the avian gut microbial
ecosystem is leading to improvements in poultry productivity, health, welfare, and reduction of
foodborne pathogens and the environmental impact of poultry production for a more sustainable
industry. Molecular methods of microbial ecology are key tools to acquire this knowledge. The
objective of this presentation is to demonstrate the positive correlation between diversity and
stability of gut microbial populations and improved broiler performance and health. Some
examples will illustrate this point and its relevance to advances in control methods for pathogens,
avoidance or managment of dysbiosis or subclinical intestinal diseases, reduced environmental
impact, elucidate effects of nutrients, diet form and feed additives on gut mucosa and microflora,
and in general to improve poultry productivity and health.
Key Words: Nutrition, intestinal microbial ecology, equilibrium and dysbiosis.
Introduction
Poultry and other animals, as well as humans, coexist with a complement of prokaryotic
symbionts that confer a variety of physiologic benefits. The microbes associated with mucosal
surfaces exceed the total number of somatic and germ cells by more than an order of magnitude
in any animal. The gastrointestinal tract (GIT) is home to the most complex and populous
society or ecosystem of microbes. These microbes are grouped in microbial communities (MC)
composed of groups of bacteria, protozoa, fungi, yeasts, bacteriophages and other virus that
constitute the microbiome of each animal. Dynamic ecosystems exist within the different
segments of the gut, each one having distinct luminal and mucosal niches. Countless diverse MC
within the different enteric niches are affected by the flow of nutrients from the diet, secretions
from the host and the systemic responses of the host (animal) dictated by its immune, endocrine
and nervous systems as well as by substances secreted from constituents making up these
complex niche micobiomes (Dibner and Richards, 2004, Thompson and Applegate, 2005,
Korver, 2005, Oviedo-Rondón, 2006).
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Microbes have profound effects on most of the physiological processes of their animal host
(Ewing and Cole, 1994; Fuller and Perdigon, 2003). It has been demonstrated that digestive MC
affect broiler, layer hen and turkey performance and health (Apajalahti and Bedford, 1999;
Hume et al., 2003, 2006,2011, 2012; Oviedo-Rondón et al., 2006a, 2010, 2012; Massias et al.,
2007; Parker et al., 2007). These effects in the host may be due primarily to the complex
interactions that influence the intestinal environment, and the development and responses of the
host immune system against pathogenic and non-pathogenic antigens (Cebra, 1999; Kelly and
Conway, 2005). Since all those complex associations cannot be simulated in laboratory
conditions with culture methods, molecular techniques based on DNA have been used more
frequently in research to analyze intestinal, cecal or fecal samples and to take snapshots of the
status of these dynamic relations.
Even though the net balance of the relationship between the microbiome and the animal host is
usually positive for the host, the members of each microbiome can be categorized as commensal
or pathogenic. Generally, pathogenic MC are present in very low concentrations and sometimes
are not detectable by culture methods. Some MC can have positive effects under stable gut
conditions, but the same MC can contribute hazardous metabolites to the host when intestinal
conditions change. This double-sided role means the same MC could be positive and negative
depending on circumstances. Consequently, maintaining the intestine in equilibrium is extremely
important to sustain this balance between commensals and pathogenic microbes. Drastic
qualitative and quantitative changes in the gut microflora characteristics are called dysbiosis,
dysbacteriosis, and alternately small intestinal bacterial overgrowth. These events generally
cause clinical signs, such as flushing (diuresis or diarrhea). Wet liter is a general outcome that
may affect the air quality of the house with higher production of ammonia accompanied by a
higher incidence of respiratory problems.
In this presentation, we aim to demonstrate the importance of maintaining diversity of gut MC in
each ecosystem as a good indicator of adequate modulation when gut equilibrium has been
disrupted. This concept is important to advance in developing control methods for gut pathogens,
improve gut health for better and more sustainable feed conversion and nutrient utilization.
Additionally, it may be helpful to improve the control of foodborne pathogens and reduce
production of ammonia and volatile fatty acids responsible for gas emissions and noxious odors
in poultry houses.
How the animal host maintains the gut equilibrium?
The host animal has several physiological mechanisms to control bacterial proliferation within
their intestines, and the possible translocation from the intestinal lumen to the blood stream
(Oviedo-Rondón, 2006). The main intestinal barriers to pathogen infection that also modify the
microbial profile for each animal include: 1) peristalsis (flow rate, transit time), 2) secretions
(water, electrolytes, HCl, enzymes, bile salts, mucins and immunoglobulin A (IgA), 3) mucus
(physical properties, associated microflora, IgA), 4) mucosal integrity, 5) efficient nutrient
digestibility and absorption, and 6) the gut-associated lymphatic tissue (GALT).
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The high passage rates of digesta and the continuous sloughing of epithelial cells and mucus
washes out adhered bacteria. In addition to the polymeric immunoglobulin receptor on mucosal
epithelial cells, IgA antibodies can bind to receptors on a variety of leukocytes, which can
activate the alternative complement pathway, making IgA antibodies potential participants in
inflammatory reactions. Consequently, these interactions are responsible for part of the
immunological responses observed with some diets and specific nutrients. Nutrient absorption by
the host animal is highly competitive with enteric microflora by limiting substrates. Improving
nutrient digestibility and nutrient absorption in the foregut is the main way to minimize bacterial
proliferation.
What is the role of MC in gut equilibrium?
Gut microflora aid in colonization resistance, competition for intestinal attachment sites, and aid
in the early development and stimulation of the immune system (Bar-Shira and Friedman, 2005;
Bar-Shira et al. 2005). Intestinal and cecal symbiotic bacteria have important and specific
metabolic, trophic and protective functions. The first line of defense against pathogens for birds
is the normal gut microflora.
Most of the gut microbiota competes with the host for nutrients by various means. However,
competition for nutrient resources from commensal or symbiotic microbiota benefits the host by
1) promotion of gut maturation, 2) enhancement of gut integrity, 3) antagonisms against
pathogens (competitive exclusion), and 4) immune modulation. The symbiotic microflora also
plays a significant role in maintaining intestinal immune homeostasis by preventing
inflammation (Lan et al., 2004). Metabolic functions include fermentation of non-digestible
dietary residue and endogenous mucus, which is important for the recovery of energy as short
chain fatty acids, production of vitamin K, and absorption of ions. Symbiotic bacteria influence
epithelial cell proliferation and differentiation due to their production of short chain fatty acid.
The attachment of non-pathogenic bacteria to the brush border of intestinal epithelial cells can
prevent the attachment and subsequent entry of pathogens.
Symbiotic bacteria also
competitively exclude pathogenic bacteria by producing organic acids such as lactic, propionic
and butyric and compounds known as bactericins inhibitory to pathogens, or maintaining their
habitat by consuming resources of the gut (Nisbet et al., 1996). Reuterin, a bacteriocin produced
by Lactobacilli, has been shown in vitro to be inhibitory against Salmonella, Shigella,
Clostridium and Listeria (Naido et al., 1999). Increasing the numbers of these types of bacteria
and providing the appropriate substrate for their proliferation and metabolism improves the
efficiency of nutrient utilization by the host.
Competition for nutrient resources from pathogenic microbiota is to the detriment of the host
animal. This non-symbiotic microflora may 1) produce toxic phenolic/aromatic metabolites that
increase cell mucosa turnover, 2) increase mucus production, 3) cause deconjugation of bile salts
that reduces fat digestion, 4) increase protein and energy needs to maintain gut function and
health, and 5) reduce growth efficiency. High levels of the common bacterial toxic metabolites,
such as ammonia, phenol, 4-methylphenol (p-cresol), 4-ethyphenol, indole and 3-methylindole
(skatole), and biogenic amines, can cause or exacerbate enteritis (Gaskins et al., 2002). These
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compounds are responsible for noxious odors and air pollution caused by poultry manures. The
appropriate manipulation of gut MC may help to reduce this environmental impact.
Throughout metabolite production and direct contact with specific cells, the microbiome has a
continuous “cross-talk” with the host mucosa. Despite its importance, very little is known about
the specific molecular mechanisms that allow components of the microflora to interact with their
hosts so as to establish relationships that are advantageous to both. Understanding such
relationships is important in elucidating the origins of opportunistic infections that can cause
common poultry diseases such as necrotic enteritis and colibacillosis, or increase the negative
impact of parasite infections such as in coccidiosis, and even the propagation of antibioticresistant organisms or resistance to other growth promotant products.
It can be concluded that the host (bird) has many natural mechanisms to control microbial
proliferation, but the interactions among MC and between MC and the mucosa are also important
for maintaining gut equilibrium. The conclusion is that any intervention to preserve gut
equilibrium should consider all these factors and the MC functions should be better understood.
Molecular methods to study gut MC.
Before discussing further details of the gut ecosystem it is important to understand the
methodologies currently used to study MC. The traditional methods of gut microbial diversity
and ecology were largely based on classical anaerobic culture techniques, phenotypic
characterization of culturable isolates, and light and electron microscopy. However, the real
complex and diverse digestive microflora cannot be studied accurately with these more
traditional methods, because only 1% of all MC are culturable (Hugenholtz et al., 1998).
Culture-based enumeration and characterization techniques also have three major problems, the
inevitable bias introduced by the selective culture media, the lack of a phylogenetically-based
classification scheme, and the unfeasibility to detect unculturable and fastidious bacterial species
or those present in very low abundance.
Consequently, fingerprinting of 16S rRNA genes is currently the more suitable set of
methodologies for monitoring communities’ shifts or comparing different MC. Many of the MC
can be detected with 16S rRNA techniques, but detection does not indicate that they are active,
because many bacteria can be dead or be present as spores, as has been determined by
fluorescent in situ hybridization (FISH) techniques. The molecular techniques normally used are:
1) Denaturing gradient gel electrophoresis (DGGE) (van der Wielen et al., 2002; Hume et al.,
2003, 2006, 2011, 2012; Oviedo-Rondón et al., 2006b, 2010); 2) Temperature gradient gel
electrophoresis (TGGE) and temporal temperature gradient gel electrophoresis (TTGE)
(Cocetiere et al., 2005; Zhu et al., 2002); 3) Single-strand conformation polymorphism (SSCP)
and terminal-restriction fragment length polymorphism (T-RFLP) (Gong et al. 2002; Marsh et al.
2000; Lan et al., 2004). All these fingerprinting techniques are PCR-based, and their respective
profiles represent the sequence diversity within ecosystems. For more detailed descriptions of
these fingerprinting techniques, refer to the review papers by Muyzer and Smalla (1998) and
Vaughan et al. (2000). Software has been developed to analyze fingerprinting data, compare
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between communities, and calculate similarity indices or coefficients by analysis of clustering
profiles. These fingerprinting approaches are more qualitative than quantitative, as quantitative
PCR methods are involved. However, the possibility of absolute quantification of targets
resulting in single amplicons in TGGE profiles has been demonstrated. A similar quantification
approach was performed by combining constant–denaturant capillary electrophoresis (CDCE)
and quantitative PCR (Zoetendal and Mackie, 2005).
The GC fractionation of total community DNA has been also widely used in poultry research to
compare MC structures in GIT of broilers (Holben and Harris, 1995; Apajalahti et al., 1998,
2001, 2002, Parker et al., 2006). The output from this approach is a fractionated profile of the
entire community that indicates relative abundance of DNA as a function of G+C content and
inferential information regarding the taxa comprising the community. In addition, this technique
physically fractionates total community DNA into aliquots that represent different G+C contents.
These highly purified fractions are of high molecular weight and thus are suitable for additional
molecular manipulations, including PCR amplification, DGGE analysis, and cloning. Its primary
limitation is the low resolution that does not indicate the number or identity of different taxa in a
particular G+C fraction.
The limitations of DDGE and GC fractionation of total community DNA techniques can be
overcome by combining methods. Holben et al. (2004) combined two mechanistically different
community analysis methods, the GC fractionation and DGGE (GC-DGGE) with phylogenetic
analysis of DNA and 16S rRNA gene sequences to obtain information on minority populations
or taxa in the GIT that were not detected by a typical random cloning survey of the same
community due to low abundance. The initial fractionation of total community DNA based on
G+C content effectively reduces the complexity of the community DNA mixture being analyzed
such that the total diversity within each fraction can be more effectively assessed. Additionally,
by cloning and sequencing DGGE bands from individual fractions, it is possible to gain insight
into the identity of specific taxa of interest (Holben et al., 2004).
Selected electrophoretic bands of DGGE gels can be extracted and cloned for identification of
bacteria. Currently, the most common method used to perform deep and semi-quantitative
diversity analysis of GIT populations is the bacterial tag encoded FLX amplicon pyrosequencing
(bTEFAP) approach (Bosch and Grody, 2008). Pyrosequencing and parallel sequencing of
individual DNA molecules allows examination of the dynamics of intestinal flora quantitatively
and far more precisely than previously possible with other techniques. This bTEFAP is relatively
inexpensive in terms of both time and labor due to the implementation of a novel tag priming
method and an efficient bioinformatics pipeline. In 24 hours, this method is able to generate and
analyze a nominal 1,000, potentially unique, sequences per sample.
What has been learned from using molecular methods about gut MC?
The new molecular methods have indicated that the majority of gut microbes has never been
cultured. The classification criteria for bacterial species are changing, due to the large sequence
analysis of microbial genomes, and the unexpected diversity between closely-related species.
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Taking into consideration the new knowledge on MC it is even more important to redefine
populations as functional groups among the microflora, and according to the mechanisms by
which they impact their host. The gut MC are very host- and GIT niche-specific (Gong et al.,
2002). The intestinal microbiome of chickens is very different from that found so far in turkeys,
sharing only 16% similarity at the species-equivalent level (Wei et al., 2013). Most of the novel
sequences from GIT samples are grouped in the low G+C Gram-positive phyla. The most
predominant gut MC genera found in poultry sequence data sets are Clostridium, Ruminococcus,
Lactobacillus, and Bacteroides. Compared with the gut microbiome of other animals, the
numbers of sequences recovered from both chickens and turkeys, and the diversity represented
by these sequences, are relatively small (Wei et al., 2013). This difference can be due to the fast
transit of digesta in the GIT can be only 4 hours for chickens. This difference also emphasizes
the importance of maintaining this diversity. Very little is known about the functions of bacteria
and their metabolite products. Consequently DNA arrays, microbial genomics, proteomics and
metabolomics will be more necessary, useful, and potentially informative tools in the near future.
What factors modify the gut MC profiles?
The MC evolves in the intestinal ecosystems as the birds age following changes in physiological,
immunological characteristics and feeding behavior (Hume et al., 2003; Lu et al., 2003). This
process is known as microbial succession. However, the strongest determinant of the gut
microbial profile is the host’s diet. It has been shown that is possible to shift the MC from
pathogenic to beneficial bacteria by changing the dietary composition of ingredients (Gibson and
Roberfroid, 1995; Collins and Gibson, 1999) or using feed additives (Hume et al., 2006, 2011,
2012; Oviedo-Rondón et al., 2006, 2010; Parker et al., 2007, Martynova-Van Kley et al., 2012).
Diet composition, amounts of digesta reaching each section of the intestine, passage rate, enzyme
production and secretions change as the chicken ages.
Apajalahti et al. (2001) used molecular methods to survey MC of broilers raised at eight
commercial poultry farms in Finland. These birds were fed different commercial wheat-based
diets, some with locally-added whole wheat. This survey covered different seasons (spring and
fall) and years (1997, 1998, and 2000). They found that diet was the strongest individual
determinant of the total MC structure in the ceca of broiler chickens, whereas profiles of
individual farms with identical feed regimes hardly differed from each other. In this study, there
was also no significant variation of the colonic MC due to season or year. Thus, to avoid
variability in dietary factors it is critical to minimize dysbiosis problems, but this strategy is not
easy under commercial conditions and MC modulators become a necessity.
The dietary levels of specific nutrients such as fat (Knarreborg et al., 2002), protein (Parker et
al., 2007) calcium and zinc (Hume et al., 2003) and pH of the diet (Dibner and Richards, 2004)
have also been associated with shifts in MC. The association of dietary glycine supplementation
with C. perfringens numbers and location, α-toxin production and gut lesion scores has been
reported (Dahiya et al., 2005).
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Better feed utilization by the host seems to be a critical factor to determine the gut MC profile.
Several research groups (Torok et al., 2011; Apajalahti et al., 2012; Singh et al., 2012) have
reported the link between specific cecal and fecal microbiota and feed conversion efficiency
(Figure 1). This relationship could be due to two different, but possibly not conflicting and
actually coexistent mechanisms. 1) Specific groups of MC can directly transform dietary
components into high energy metabolites. This extra energy could be used by the host improving
its feed conversion. 2) The MC communities in ceca and feces reflect how well digestion and
absorption occured in the foregut (Apajalahti et al., 2012). Independent of the most dominant
mechanism, it is interesting that MC profiles could be used to rapidly determine the best nutrient
utilization.
Figure 1. nMDS ordination of cecal
microbial communities from male
broilers in an experiment conducted by
Torok et al., 2011. Microbial
communities are identified as being
from either birds with improved
performance (▾) or poorer-performing
birds (▴).
According to the data (Figure 2) presented by Apajalahti et al. (2012), the diversity of those MC
profiles is also important to determine best utilization of nutrients in healthy animals. Apajalahti
et al. (2012) even proposed a “Performance-linked Microbial Index" (PMI), which correlated
with feed conversion (r= 0.82, P<0.0001) and ME (r=0.58, P<0.0001). This index could be used
to evaluate effects of multiple factors on nutrient utilization even under farm conditions and still
allows corrective actions to be taken before the birds are slaughtered (Apajalahti et al., 2012;
Singh et al., 2012).
What factors break the gut equilibrium?
Host factors. Any perturbation in gastroenteric physiology or immunity of the bird caused by
temperature stress (heat or cold) or other environmental discomfort can cause dysbacteriosis
and/or enteritis associated with lower absorption of nutrients by the host. Exposure to stress
hormones, norepinephrine and epinephrine, significantly increases the proliferation of several
enteropathogenic bacteria such as Escherichia coli, Yersinia enterocloitica, Pseudomonas
aeurinosa, Salmonella enteritidis, Salmonella cholerasuis, Salmonella typhimurium (Thompson
and Applegate, 2005). Even under the best nutritional conditions, environmental stresses under
7
commercial production conditions can increase intestinal bacteria proliferation and make broilers
more prone to enteric problems.
Figure 2. Chicken caecal microbial profiles arranged according to the FCR of the farm from
which they originated. Bacterial profiles from five farms with the lowest FCR are on the left and
those from five farms with the highest FCR on the right. (Apajalahti et al., 2012)
The changes in intestinal motility, modifications of gastric acidity, decreases in the production of
bacteriostatic peptides in the pancreas, alterations in the amounts of mucus produced or in its
composition, reduced IgA secretion, and focal ulcerations of mucosa result in failure of nutrient
absorption, tissue necrosis, and shifts in gut MC numbers and metabolism.
Enteropathogens. The gut MC are also affected by enteropathogen infections such as those
caused by Eimeria spp. Coccidiosis is a common parasitism in poultry and one of the more
common causes of enteric problems. Coccidiosis is still one of the most endemic enteric diseases
in broiler production worldwide (Williams, 2005). Coccidial stress consistently has been shown
to sensitize broilers to enteritis including necrotic enteritis (Van Immerseel et al., 2004;
Williams, 2005). Coccidia infection causes reduced weight gain and poor feed conversion
efficiency, reduced feed and water intake, increased intestinal passage time, decreased digesta
viscosity and nutrient digestion, villous atrophy, intestinal leakage of plasma proteins, and
increased intestinal acidity (Williams, 2005). Apajalahti (2004) indicated that infection with
Eimeria maxima changes MC and the patterns of fermentation in the ilea and ceca of broilers.
These possible outcomes suggest that when coccidiostats are not used in the diets or coccidian
vaccines are administered, gut MC are different and during coccidiosis outbreaks microbial
succession is less stable affecting physiology and nutrient utilization (Hume et al., 2003; OviedoRondón et al., 2006a, 2010; Parker et al., 2007; Martynova-Van Kley et al., 2012).
Feed withdrawal. The MC can change in periods of hours and the most crucial factor is the
lack of digesta. Thompson et al. (2008) using molecular methods were able to observe that feed
withdrawal in broilers alters the MC of the intestine by decreasing bacterial diversity in the
ileum.
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How to modulate gut MC to minimize effects of disequilibrium?
Some feed additives and mineral levels can help to maintain healthy MC in regions of the guts of
broilers independent of feed withdrawal (Thompson et al., 2008, Hume et al., 2003), intestinal
infections with coccidia (Oviedo-Rondón et al.a, b, 2006; Hume et al., 2006, 2011, 2012; Parker
et al., 2007; Martynova-Van Kley et al., 2012), or heat stress (Lan et al., 2004) and even
maintain the normal diversity of MC observed in control groups of chickens (Lee et al., 2002;
Guo et al., 2004; Martynova-Van Kley et al., 2012).
Growth promotant antibiotics are well known for the inhibition of undesired MC and the
negative effects of their metabolites (Anderson et al., 1999; Van Immerseel et al., 2004), and
selection for beneficial bacteria (Collier et al., 2003; Engberg et al., 2000), but somehow they
diminish natural diversity of gut MC. Other products have been proposed as alternatives to
growth promotant antibiotics utilization (Thomke and Elwinger, 1998) taking into consideration
the increasing bacterial resistance to some antibiotic categories, ban of their use in some
countries, and poultry consumer rejection.
Alternative new feed additives have been classified as probiotics, prebiotics, enzymes, organic
acids, and herb extracts. Probiotics introduce desirable live microorganisms into the GIT.
Prebiotics promote the growth of desirable bacteria in the GIT (Patterson and Burkholder, 2003).
Enzymes help to eliminate the anti-nutritional effects of water-soluble polysaccharides, and/or
change the substrates to improve proliferation of some beneficial MC, while organic acids cause
the inhibition of bacterial growth. Finally, phytobiotics have very variable working mechanisms
that depend on their composition. Phytobiotics can be bacteriostatic or immune-stimulating. One
category of phytobiotics is the specific essential oil (EO) blends. These products are mixtures of
phytochemical compounds, such as carvacrol, thymol, cinnamaldehyde among others, with
selective antimicrobial properties (Lee et al., 2002; Guo et al., 2004). Some specific EO blends
have shown promising results towards the reduction of Clostridium perfringens colonization and
proliferation, control of coccidia infection and consequently may help to reduce necrotic enteritis
(Guo et al., 2004; Mitsch et al., 2004; Oviedo-Rondón, 2006). The combination of probiotics
and prebiotics has been called symbiotics. Each type of product has demonstrated varied efficacy
while administered independently or in combinations, but some evidence suggests the
combination or dual administration of prebiotics and probiotics might be the more effective
strategy (Langhout, 2000).
Our research groups have used several molecular methods to study the effects of antibiotics and
ionophores, EO, enzymes, and probiotics as feed additives in broilers vaccinated against coccidia
and/or in challenges with pathogenic sporulated oocysts of mixed Eimeria spp. We used DGGE
(Figure 3) to examine the digestive microbial composition and to determine community
succession in the duodena, ilea, and ceca of broilers infected with Eimeria spp. oocysts, and fed
corn-soybean meal diets without feed additives or supplemented with either an antibiotic +
anticoccidial (BMD + Coban; AI) or two specific EO blends, Crina Alternate (CA) and
Crina Poultry (CP) (DSM Nutritional Products) (Hume et al., 2006). Similar treatments were
also evaluated in broilers vaccinated at first day of age against Eimeria spp. (Oviedo-Rondón et
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al., 2006a). Although the mixed coccidia challenge was associated with the greatest relative
shifts in the post-challenge MC in all three sections of the intestine, independent of the treatment
(UI) (Figures 4, 5 and 7) and, only in cecal samples (Panel C in Figures 4, 7) was it possible to
observe a clear difference between pre- and post-challenge MC for all treatments as well as the
effect of coccidia challenge (UI) (Oviedo-Rondón et al., 2006b).
The similarity coefficients obtained with the analyses of DGGE images (Figures 5 and 8) showed
that feed additives modulate MC in coccidial challenges, although they do vary in their
influences over MC in each intestinal compartment. Under the conditions of the present
experiment the specific EO blends CA and CP appear to be effective in modulating MC and
avoid drastic changes in MC after a mixed coccidia challenge. Pyrosequencing and parallel
sequencing of individual DNA molecules gave us a good look at the microbial diversity in the
digesta samples affected by treatments (Martynova-Van Kley et al., 2012). Results partially
presented in Figure 9 show the drastic reduction in diversity of MC when chickens were infected
with coccidia. The same analyses demonstrated how EO were able to maintain the diversity
even after a mixed coccidian infection. The antibiotic growth promotant also aided in
maintaining diversity, but not in the same degree. These results were coherent with the
performance results obtained (Oviedo-Rondón et al., 2006a).
FIGURE 3. Denaturing gradient gel electrophoresis
of A) duodenal, B) ileal, and C) cecal microbial
communities from broiler chickens at 19 d of age
(pre-challenge). Relative similarity of band patterns
is indicated by their grouping on the dendogram and
the percentage similarity coefficient (bar). UU =
unmedicated-uninfected
control;
UUFp
=
unmedicated-uninfected floor pen control; UI =
unmedicated-infected control; AI = BMD at 50
g/ton and monensin (Coban) at 90 g/ton; CP =
essential oil blend Crina Poultry, and CA =
essential oil blend Crina Alternate. Source: Hume et
al., 2006.
FIGURE 4. Denaturing gradient gel electrophoresis
of A) duodenal, B) ileal, and C) cecal microbial
communities from broiler chickens at 26 d of age
(post-challenge) and 7 days after mixed Eimeria spp.
oral infection. Relative similarity of band patterns is
indicated by their grouping on the dendogram and
the percentage similarity coefficient (bar). UU =
unmedicated-uninfected control; UI = unmedicatedinfected control; AI = BMD at 50 g/ton and10
monensin (Coban) at 90 g/ton; CP = essential oil
blend Crina Poultry, and CA = essential oil blend
Crina Alternate. Source: Hume et al., 2006.
Duodenal
100
Ileal
Cecal
89.9
86.4
83.3
81.8
Similarity Coefficients, %
78.4
76.4
75.3
75
69.7
66.7
65.8
57.4 60.0
55.4
50
36.7
36.2
25
0
UU
UI
AI
CP
CA
FIGURE 5. Percentage similarity coefficients from comparisons of microbial communities in pre- and
post-coccidia challenge samples within each treatment and intestinal compartment. UU = unmedicateduninfected control; UI = unmedicated-infected control; AI = BMD at 50 g/ton (Alpharma) and monensin
(Coban) at 90 g/ton (Elanco); CP = essential oil blend Crina Poultry (DSM Nutritional Products), and
CA = essential oil blend Crina Alternate (DSM Nutritional Products). Source: Hume et al., 2006.
FIGURE 6. Denaturing gradient gel electrophoresis of A)
FIGURE 7. Denaturing gradient gel electrophoresis of A)
duodenal, B) ileal, and C) cecal microbial communities from broiler
chickens at 19 d of age (preinfection). Relative similarity of band
patterns is indicated by their grouping on the dendogram and the
percentage similarity coefficient (bar). UU = unmedicated-uninfected
control; UI = unmedicated-infected control; COV = coccidiavaccinated with Advent (Viridus Animal Health LLC—Novus
International Inc., St. Louis, MO); CP = essential oil blend Crina 
Poultry; CA = essential oil blend Crina Alternate; CVFp =
coccivaccinated floor pen. Source: Oviedo-Rondón et al., 2006b.
duodenal, B) ileal, and C) cecal microbial communities from broiler
chickens 7 d after mixed Eimeria species infection (26 d of age).
Relative similarity of band patterns is indicated by their grouping on
the dendogram and the percentage similarity coefficient (bar). UU =
unmedicated-uninfected control; UI = unmedicated-infected control;
COV = coccidia-vaccinated with Advent (Viridus Animal Health
LLC—Novus International Inc., St. Louis, MO); CP = essential oil
blend Crina Poultry; CA = Essential oil blend Crina Alternate.
Source: Oviedo-Rondón et al., 2006b.
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In a previous study (Parker et al., 2007), we used GC fractionation of total community DNA to
evaluate the effect of enzyme supplementation on cocci-vaccinated birds pre- and post-challenge.
Microbial profiles described by G+C% were affected (P ≤ 0.05) by both dietary CP level and
vaccination or feed additives (Figure 10). On average, treatments infected with mixed coccidia
species hosted gut MC characterized by higher relative abundance of bacteria in the 50 to 80
G+C% range when the diet had either 21 or 23% CP (P ≤ 0.01). Cocci-vaccinated chicks
supplemented with enzymes (COV + EC) fed diets 19% CP showed very similar G+C% profiles
related to the UU controls (Figure 10, panel A). The t-test comparing COV and COV + EC
treatments indicated that microbial profiles changed (P≤ 0.001) due to enzyme supplementation
in all G+C% increments except in those from 60 to 69% in chickens fed 23% CP diets (Figure
10, panel C).
We have observed in several studies (Hume et al., 2006; Oviedo-Rondón et al., 2006b, Parker et
al., 2007; Martynova-Van Kley et al., 2012) that vaccination against coccidia with viable oocysts
alone caused small changes on intestinal microflora (Figure 6), and that stresses and coccidia
challenge resulted in drastic shifts in MC (Figure 6 vs. Figure 7 and Figure 8) that can partially
be modulated with feed additives. However, the response changes according to diet composition
(Figure 9) even in broilers fed corn-soybean meal diets with different levels of protein (Figure 9)
(Parker et al., 2007).
Duodenal
100
Ileal
Cecal
86.4
Similarity Coefficients, %
75
66.5
66.7
66.4
81.9
81.8
79.5
78.4
73.3
67.9
59.7
60.4
55.4
50
36.7
36.2
25
0
UU
UI
CV
CV + CP
CV + CA
FIGURE 8. Similarity coefficients (%) comparing microbial communities in pre- and
postchallenge samples within each treatment and intestinal compartment. UU = unmedicateduninfected control; UI = unmedicated-infected control; COV = coccidia vaccinated with
Advent (Viridus Animal Health LLC—Novus International Inc., St. Louis, MO); CP = essential
oil blend Crina Poultry; CA = essential oil blend Crina Alternate. Source: Oviedo-Rondón et
al., 2006.
12
B
A
FIGURE 9. Cecal microbial communities of broilers quantified and identified by bacterial tag
encoded FLX amplicon pyrosequencing (bTEFAP) from selected DGGE bands observed in
experiments described in Hume et al., (2006). (A) Microbial communities in ceca of chickens
fed a basal corn-soybean meal diet without feed additives during the pre-challenge period. (B)
Microbial communities in ceca of chickens fed a basal corn-soybean meal diet without feed
additives after a challenge with mixed coccidia species (Martynova-Van Kley et al., 2012).
FIGURE 10. The G + C% profile of a cecal
microbial community DNA corrected with
microbial numbers from 12 broilers per
treatment. Treatments are compared within each
dietary protein level: 19% (panel A), 21% (panel
B), and 23% CP (panel C): UU = unmedicateduninfected controls; IO = ionophore monensin;
COV = coccidia vaccination; COV + EC =
coccidia vaccination + dietary enzyme
supplementation (Avizyme 1502, Danisco
Animal Nutrition). ***P≤ 0.001. Source: Parker
et al., 2007.
13
Our studies indicated that the predominant gut MC are relatively stable over time in healthy
animals, but shifts in MC occur with ageing, especially in young and old animals, periods of
starvation or intestinal insults such as coccidiosis infections. It is noteworthy that unstable MC
are frequently correlated with GIT disorders and reduced growth or worsening of feed
conversion. Feed additives or diets that maintain more stable MC even during periods of
intestinal stress, also support better animal performance in those conditions.
Conclusions
Maintaining the equilibrium of the gut ecosystem is key for performance and health. Diversity
and stability of MC and specific profiles of microbiomes are related with better feed conversion
in healthy animals and those challenged by coccidia. The understanding and description of
intestinal MC are very important for the development of new feed additives and appropriate
manipulation of diets to improve poultry performance, health, welfare, and to reduce foodborne
pathogens and the environmental impact of poultry production. Molecular methods of microbial
ecology are indispensable tools to characterize these communities and establish their function in
these dynamic ecosystems and their relationships with the host.
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