Modern food safety: Managing the microbiome of food animals for performance and food safety goals
Brian B. Oakley, M.S., Ph.D. Associate Professor Western Health Sciences University College of Veterinary Medicine Pomona, CA
Importance of microbiota
courtesy of Gregory Plotnikoff, M.D.
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GI microbiome helps control energy balance
Importance of microbiota
Turnbaugh et al. 2006 Nature (444)
Bacteria truly inhabit a “Micro” Biome
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Surface Areas:
Mara-Serengeti = 25,000 km2
Human GI tract = 40 m2
All animals are mostly microbial
Animals are: !! 50-90% bacterial by cell
count !! 90-99% bacterial by gene
count
Veterinary & Food Animal Medicine MUST consider what comprises the majority of the organism
by Gaby D'Allesandro / © AMNH
Can modern food safety go back to the future?
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courtesy of Gregory Plotnikoff, M.D.
Q: Can we move from a mentality of ‘killing bad bugs’ to Ecosystem Management? Current state of the art is development of “Microbial Agriculture”
Microbial Ecology – Nascent field
!! Who is there? !! How many of each? !! What are they doing?
Renaissance of Microbial Ecology !! DNA sequencing costs are decreasing faster than
Moore’s law:
Toolkit of molecular microbial ecology
!!Compositional census
!!16S rRNA !! Taxonomic ID
!!Functional census !! Metagenomics
!!Metabolic potential
!! Metatranscriptomics !!Metabolic activity
Revolutionized by high-throughput sequencing
Toolkit of molecular microbial ecology
!!Compositional census
!!16S rRNA !! Taxonomic ID
Toolkit of molecular microbial ecology
!!Functional census !! Metagenomics
Renaissance of Microbial Ecology High-throughput sequencing
!! Great plate count anomaly - Has largely removed previous limitations !! Only 0.1 – 1% of taxa cultivable !! ‘uncultured majority’ – Only 26/52 phyla in cultivation
!! Comprehensive census once impossible, now routine
!! Bioinformatic and Computational Advances
Staley and Konopka 1985. Ann Rev Micro Rappe and Giovannoni 2003. Ann Rev Micro
Next-gen sequencing
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Old-school cloning/Sanger sequencing
Number of sequences
Cum
ulat
ive
num
ber
of s
eque
nce
type
s
NGS Output
Pre-processed dataset
Reformat, parse, build data tables,
map OTUs to taxonomy
OTU/Taxonomy
Assignment
Statistical & Graphical summaries
UCLUST, USEARCH SILVA, PERL
R
mothur
seconds - minutes
PERL
PERL, QIIME
Bioinformatics gives the keys to the kingdom
seconds - minutes
Huse et al. 2007 Gen Biol Quince et al. 2009 Nat Met Kunin et al. 2010 EM
Schloss et al. 2009 AEM Oakley et al. 2012 ISMEJ
Pipeline built from generic building blocks
Importance of Microbial Ecology Can now answer: •! Community composition – who’s there •! Community structure – how many of each •! Community function – what are they doing
!! Humans as ‘super-organisms’ !! New tools applied to microbial
ecosystems in and on our bodies !! “Second genome”
!! ~10x more microbial than human cells
!! ~100x more genes
!! Critical roles in nutrition, health,
disease http://www.nature.com/nature/focus/humanmicrobiota/
HMP – Humans as microbial ecosystems
Poultry microbiology and its importance for food safety
!! ~ 9,000,000,000 broilers processed per yr in U.S.
!! ~ 40,000,000 tons of feed per yr
!! Carbon footprint ~450,000,000,000 car miles yr-1
!! Major source of protein & foodborne illness
!! Microbiome of birds and production system as ‘super-organism’
Renaissance of Microbial Ecology
Broiler Production 101
Grow-out
Hatchery Transport
Consumer
Processing
Can we manage the micro biota?
!!What do natural communities look like?
!!New tools & approaches needed
!!Alternative antimicrobials
!!Can we put it all together? “Seed, Feed, & Weed”
Community changes significantly as bird grows
Temporal differences > feed additives
−3 −2 −1 0 1 2 3
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−1 0
1 2
3
−3
−2
−1
0
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2
3
CA1
CA2
CA3
CTL
FO
WO
FW
7d 21d 42d
Figure 1.
What do natural communities look like?
Oakley et al. 2015 BMC Vet Res.
0 7 14 21 28 35 42
050
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Time (day post-hatch)
Ric
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Div
ersi
ty (S
hann
on In
dex)
RichnessDiversity
Figure 4.
Time
A)
B)
Network nodesNetwork edges
7d
92
165
21d
122
680
42d
147
611
Community changes significantly as bird grows
What do natural communities look like?
Oakley et al. 2015 BMC Vet Res.
Community changes significantly through time …and space
Oakley et al. 2016
What do natural communities look like?
Cecal
Fecal
S1F1
S1F2
S1F3
S1F4
S1F5
S2F1
S2F2
S2F3
S2F4
S2F5
S3F1
S3F2
S3F3
S3F4
S3F5
S4F1
S4F2
S4F4
S4F5
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1F2
G1F
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G1F
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G2F
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G3F
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G3F
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G4F
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4F3
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F1F2
F1F3
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F3F2
F3F3
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F4F3
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seq
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es
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ProteobacteriaFirmicutesBacteroidetesActinobacteriaChrysiogenetesCyanobacteriaSynergistetesTenericutesVerrucomicrobia
S1C
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C3
S1C
4S1
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S2C
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C2
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es
FirmicutesBacteroidetesProteobacteriaActinobacteriacandidate ZB3ChrysiogenetesFusobacteriaSpirochaetesSynergistetesTenericutesVerrucomicrobia
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Knowledge of community composition can guide management strategies
0.0
0.2
0.4
0.6
0.8
1.0 Clostridium sensu strictoSyntrophococcusLachnospiracea_i-sLactonifactorPseudoflavonifractorOscillibacterButyricicoccusBlautiaClostridium XlVaFlavonifractorDoreaRoseburia
AnaerotruncusRobinsoniellaHowardellaFaecalibacteriumAminiphilusMahellaSubdoligranulumPapillibacterHydrogenoanaerobacteriumClostridium XVIIIClostridium XlVbAlistipesPr
op. o
f seq
uenc
es
7 d0 d 21 d 42 dCT
L
CTL FO WO FW FO WO FW CTL
FO WO FW
Treatment
Figure 2.
ClostridialesSynergistalesThermoanaerobacteralesBacteroides
Oakley et al. 2015 BMC Vet Res.
What do natural communities look like?
Birds exposed to wide range of environmental bacteria, but reproducibly colonized by limited subset…..
PJ Turnbaugh et al. Nature 000, 1-5 (2008) doi:10.1038/nature07540
Humans: Taxonomic variability but functional conservation
What do natural communities look like?
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
D14_C
D14_M
D14_T
D14_V
D35_C
D35_M
D35_T
D35_V
sample
pro
po
rtio
n
Amino acid transport and metabolism
Carbohydrate transport and metabolism
Cell envelope biogenesis, outer membrane
Coenzyme metabolism
DNA replication, recombination, and repair
Energy production and conversion
Inorganic ion transport and metabolism
Intracellular trafficking and secretion
Nucleotide transport and metabolism
Secondary metabolites biosynthesis, transport, and catabolism
Signal transduction mechanisms
Transcription
Anaerostipes
Anaerotruncus
Blautia
Butyricicoccus
Clostridium IV
Clostridium XlVa
Coprococcus
Escherichia/Shigella
Ethanoligenens
Flavonifractor
Hespellia
Lachnospiracea_i_c
Lactobacillus
Moryella
Papillibacter
Parasporobacterium
Pseudoflavonifractor
Reichenbachiella
Roseburia
D14_C
D14_M
D14_T
D14_V
D35_C
D35_M
D35_T
D35_V
sample
Danzeisen et al. PLoS ONE 2011 Oakley et al. FEMS Micro Let 2014
!! Why is this important? !! Functional redundancy
!! Possible to construct minimal community?
What do natural communities look like?
Poultry: Taxonomic variability but functional conservation
Can we manage the microbiota?
!!What do natural communities look like?
!!New tools & approaches needed
!!Alternative antimicrobials
!!Can we put it all together? “Seed, Feed, & Weed”
Fluorescence in-situ hybridization (FISH) !!Sequence-based discrimination of
bacterial cells (and colonies)
Stringent hybridization and washing
SPECIES A
Ribosome (rRNA + proteins)
Thousands per cell
Thousands per cell
TGCAAGAT TGCAAGAT
TGCAAGAT TGCAAGAT
ACGTTCTA ACGTTCTA ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA ACGTTCTA
ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA
ACGTTCTA ACGTTCTA ACGTTCTA ACGTTCTA
GGCAAGTT GGCAAGTT
GGCAAGTT GGCAAGTT
SPECIES B
ACGTTCTA ACGTTCTA ACGTTCTA TGCAAGAT TGCAAGAT
TGCAAGAT TGCAAGAT
ACGTTCTA ACGTTCTA
ACGTTCTA
TGCAAGAT
ACGTTCTA ACGTTCTA ACGTTCTA
ACGTTCTA TGCAAGAT TGCAAGAT
ACGTTCTA
GGCAAGTT GGCAAGTT
GGCAAGTT GGCAAGTT
Campylobacter hominis
Campylobacter rectusCampylobacter rectus
Campylobacter showaeCampylobacter gracilis
Campylobacter concisusCampylobacter curvus
Campylobacter mucosalisCampylobacter fetus subsp. venCampylobacter fetus subsp. fetCampylobacter hyointestinalis
Campylobacter hyointestinalis Campylobacter lanienae
Campylobacter coliCampylobacter lari RM2100Campylobacter lari subsp. lariCampylobacter peloridis
Campylobacter insulaenigraeCampylobacter jejuni subsp. jejuni 81116Campylobacter jejuni subsp. jejuni 81−176ATCC33250Campylobacter jejuni subsp. jejuni NCTC 11168Campylobacter jejuni subsp. jeCampylobacter jejuni subsp. doCampylobacter lari subsp. conc
Campylobacter coli RM2228Campylobacter jejuni RM1221
Campylobacter upsaliensisCampylobacter upsaliensis RM3195
Campylobacter helveticusCampylobacter cuniculorum
Campylobacter canadensisArcobacter cryaerophilus
Helicobacter canisHelicobacter sp. ABHU4stomsp
Helicobacter bilisHelicobacter sp. GA−3
Helicobacter sp. CLO−3Helicobacter mastomyrinusHelicobacter sp. hokurin−1
Helicobacter sp. WYS−2001Helicobacter fennelliae
Synechococcus sp. HOG0.10
!!Can use massive rRNA sequence repositories to design specific and sensitive probes:
Fluorescence in-situ hybridization (FISH)
Proteobacteria
Bacteria
Hierarchical probe concept
Campylobacter hominis
Campylobacter rectusCampylobacter rectus
Campylobacter showaeCampylobacter gracilis
Campylobacter concisusCampylobacter curvus
Campylobacter mucosalisCampylobacter fetus subsp. venCampylobacter fetus subsp. fetCampylobacter hyointestinalis
Campylobacter hyointestinalis Campylobacter lanienae
Campylobacter coliCampylobacter lari RM2100Campylobacter lari ATCC 43675Campylobacter peloridis
Campylobacter insulaenigraeCampylobacter jejuni subsp. jejuni 81116Campylobacter jejuni subsp. jejuni 81−176Campylobacter jejuni ATCC 33250Campylobacter jejuni subsp. jejuni NCTC 11168Campylobacter jejuni subsp. jejuni ATCC 49943Campylobacter jejuni subsp. doylei LMG8843Campylobacter lari LMG21009T
Campylobacter coli RM2228Campylobacter jejuni RM1221
Campylobacter upsaliensisCampylobacter upsaliensis RM3195
Campylobacter helveticusCampylobacter cuniculorum
Campylobacter canadensisArcobacter cryaerophilus
Helicobacter canisHelicobacter sp. ABHU4stomsp
Helicobacter bilisHelicobacter sp. GA−3
Helicobacter sp. CLO−3Helicobacter mastomyrinusHelicobacter sp. hokurin−1
Helicobacter sp. WYS−2001Helicobacter fennelliae
0.10
- - A - - - - - - - - - - - - - - -- - A - - - - - - - - - - - - - - -- - A - - - - - - - - - - - - - - -- - A - - - - - - - - - - - - - - -- - A G - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - -
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- - - G - - - - - - - - - - - - - -
- - - - - - - - - - - - U - - - - -
- - - - - - - - - - - - U - - - - -- - - - - - - - - - - - U - - - - -
unlabeled1
unlabeled2
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* * ** ** * ***+ +, , , , ,-Campylobacter jejuni subsp. jejuni NCTC 11168 labeledCampylobacter jejuni subsp. jejuni ATCC 49943Campylobacter jejuni subsp. doylei LMG8843
Campylobacter jejuni subsp. jejuni NCTC 11168Campylobacter jejuni subsp. jejuni ATCC 49943
labeled
Campylobacter jejuni subsp. jejuni 81116Campylobacter jejuni subsp. jejuni 81−176Campylobacter jejuni ATCC 33250Campylobacter jejuni ATCC 33250
Labeled probe (49943)
Competitor probe (81-176)
Competitor probe (81-176)
Labeled probe (11168)
●
●
●
●
●
●
●
●
0 10 20 30 40 50 60 70
050
100
150
200
Formamide (%)
Rel
ativ
e flu
ores
cenc
e in
tens
ity
● Perfect Match (49943/11168)
Single mismatch plus competitor probeSingle mismatch (81-176/33250)
Three probe cocktail: 1. Campy~Cy3 2. Competitor probe 3. EUB~Alx488
!! New tools & approaches Can we manage the microbiota?
!! Now have direct window to follow individual strains in G-I tract
!! Developed high-throughput image analysis tools
C. jejuni 11168 gavage, image cecal sample directly:
!! New tools & approaches Can we manage the microbiota?
Can we manage the microbiota?
!!What do natural communities look like?
!!New tools & approaches needed
!!Alternative antimicrobials
!!Can we put it all together? “Seed, Feed, & Weed”
!CP26-F
!CP39-O
Can we manage the microbiota? !! Alternative antimicrobials
Can we manage the microbiota? !! Alternative antimicrobials
•! Amidases
•! Muramidases (Lysozyme)
•! Glucosaminidases
•! Endopeptidases
•! Holins form lesions in bacterial membranes
•! Autolysins and prophage enzymes present in bacterial genomes
Structural modeling suggest potential for mix-and-match use of phage lytic enzyme domains to tailor specificity as alternative antimicrobial agents:
•! N-acetylmuramoyl-L-alanine amidase sequence conservation varies by domain
•! Percent protein sequence conservation is shown from highest 100% (red) to lowest 60% (blue).
Oakley et al. BMC Genomics 2011
Can we manage the microbiota? !! Alternative antimicrobials
Can we manage the microbiota?
!!What do natural communities look like?
!!New tools & approaches needed
!!Alternative antimicrobials
!!Can we put it all together? “Seed, Feed, & Weed”
Importance of early development
De
sire
d
ph
en
oty
pe
May not be possible to return to desired condition from dysbiotic state
Pert
urb
atio
n
Desired phenotype Dys
bio
sis
Proper management
X ?
?
Poor management
Initial State
Dysbiotic state
Knowledge of community composition can guide management strategies
0.0
0.2
0.4
0.6
0.8
1.0 Clostridium sensu strictoSyntrophococcusLachnospiracea_i-sLactonifactorPseudoflavonifractorOscillibacterButyricicoccusBlautiaClostridium XlVaFlavonifractorDoreaRoseburia
AnaerotruncusRobinsoniellaHowardellaFaecalibacteriumAminiphilusMahellaSubdoligranulumPapillibacterHydrogenoanaerobacteriumClostridium XVIIIClostridium XlVbAlistipesPr
op. o
f seq
uenc
es
7 d0 d 21 d 42 d
CTL
CTL FO WO FW FO WO FW CTL
FO WO FW
Treatment
Figure 2.
ClostridialesSynergistalesThermoanaerobacteralesBacteroides
Oakley et al. 2015 BMC Vet Res.
Managing the micro biota…
0 5 10 15 20 25
0.0
0.2
0.4
0.6
0.8
1.0
Mean cytokine
Rel
ativ
e pr
opor
tion
F
Firmicutes: −0.553Proteobacteria: 0.696
Managing the micro biota…
IL6
Microbiome datasets are amenable to high-throughput data mining to identify potentially desirable & undesirable taxa. Statistical associations of particular taxa with pro- and anti-inflammatory cytokines…
Ultimate objective is to manipulate taxonomic composition to achieve desired immune responses…
0 5 10 15 20 25
0.0
0.1
0.2
0.3
0.4
0.5
Mean cytokine
Rel
ativ
e pr
opor
tion
S
Faecalibacterium: −0.56
0 5 10 15 20 25
0.0
0.1
0.2
0.3
0.4
0.5
Mean cytokine
Rel
ativ
e pr
opor
tion
F
Clostridium: −0.506Ruminococcus: 0.531
Microbiome datasets can reveal associations… Answer may be different for different age birds:
Managing the micro biota…
IL-1B
Can we identify a microbial contribution to FCR?
HRFI LRFI
LRFI HRFI
?
Experiment 1
Can we identify a microbial contribution to FCR?
prop
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lirub
roba
cter
sol
iBu
tyric
icoc
cus
pullic
aeco
rum
Bact
eroi
des
salye
rsia
eBa
cter
oide
s co
prop
hilu
s D
SM 1
8228
Anae
rost
ipes
cac
cae
DSM
146
62An
aero
stip
es b
utyr
atic
usAl
istip
es fi
nego
ldii
Slac
kia
pirif
orm
isD
orea
long
icat
ena
Col
linse
lla a
erof
acie
nsBl
autia
hyd
roge
notro
phic
a D
SM 1
0507
Alis
tipes
sha
hii
Oxa
loba
cter
form
igen
esO
lsen
ella
uli
Lach
nosp
ira m
ultip
ara
Bact
eroi
des
vulg
atus
ATC
C 8
482
Bact
eroi
des
vulg
atus
Bact
eroi
des
nord
iiBa
cter
oide
s eg
gerth
iiAn
aero
filum
agi
leVi
ctiva
llis v
aden
sis
Met
hylo
baci
llus
flage
llatu
sC
ollin
sella
ste
rcor
is D
SM 1
3279
Bact
eroi
des
egge
rthii
DSM
206
97An
aero
trunc
us c
olih
omin
is D
SM 1
7241
Sutte
rella
wad
swor
then
sis
LRFI
Taxa found only in HRFI flock
Taxa found only in LRFI flock
Can we identify a microbial contribution to FCR?
LRFI HRFI
LRFI HRFI
Experiment 2: Genetics vs. microflora
Reciprocal transplant of fecal material from adults to chicks:
Plus uninoculated controls...
Inoculum improves weight gain
050
010
0015
0020
00
week
body
wt (
g)
0 1 2 3 4 5 6
050
010
0015
0020
00
week
body
wt (
g)
0 1 2 3 4 5 6
inoculated Minoculated Funinoculated Muninoculated F
7.7%/135g
6.7%/103g
inoculated uninoculated
0200
400
600
RFI
!3022
Females
Inoculum improves FCR
!! Different cecal communities at 6 weeks
Can we manage the microbiota?
−1.0 −0.5 0.0 0.5 1.0
−3−2
−10
12
CA1
CA2
Low to Low
High to Low
Low to High
Low to High
High to High
High to High
High Male
High Male
High FemaleLow to High
High to High
Low to Low
High to Low
Low to Low
High to Low
High to Low
Low Male
Low Male
Low Female
High to Low
Low Male
Low to Low
High to Low
Low Female
High to Low
Low to Low
Low Female
Low Male
Low Female
Low Female
Low Male
Low Female
Low Female
Low Male
No inoculumDay-of-hatch inoculum
Two groups have significantly different microflora after 6 weeks:
!! Different cecal communities at 6 weeks pr
op o
f seq
s
0.00
0.10
0.20
0.30
inoculated
prop
of s
eqs
0.00
0.10
0.20
0.30 uninoculated
Strain Identification
Can FMT ‘lay the golden egg’?
14 d 1 d
5x
•! Community composition •! Metagenome •! Host Transcriptome •! Immune response (M1 vs. M2) •! AME •! Pathogen load •! Growth
FMT+
FMT-
Can FMT ‘lay the golden egg’?
14 d 1 d
5x
−2 −1 0 1
−10
12
CA1
CA2
1_NA_M34_D10
1_NA_M35_D11
1_NA_M36_D12
1_NA_M37_D1
1_NA_M38_D2
2_NA_M39_D3
2_NA_M41_D52_NA_M42_D6
2_NA_M43_D7
4_NA_M44_D8
4_NA_M45_D9
4_NA_M46_D10
4_NA_M47_D11
4_NA_M48_D12
5_NA_M49_D1
5_NA_M50_D2
5_NA_M51_D3
5_NA_M52_D4
5_NA_M53_D5
5_NA_M54_D65_NA_M55_D7
5_NA_M56_D8
NA_NA_M57_D9
5_NA_M58_D10
5_NA_M59_D11
5_NA_M60_D12
6_NA_M63_D3
6_NA_M64_D4
6_NA_M65_D5
6_NA_M66_D6
6_NA_M67_D7
6_NA_M68_D86_NA_M69_D9
3_NA_M70_D10
3_NA_M71_D11
3_NA_M72_D12
3_NA_M73_D13_NA_M74_D2
3_NA_M75_D3
5_NA_M76_D4
5_NA_M80_D8
1_NA_M34_D10
1_NA_M35_D11
1_NA_M36_D12
1_NA_M37_D1
1_NA_M38_D2
0 1
2_NA_M39_D3
2_NA_M41_D52_NA_M42_D62_NA_M42_D62_NA_M41_D52_NA_M42_D62_NA_M41_D5
2_NA_M43_D7
4_NA_M44_D8
4_NA_M45_D94_NA_M45_D9
4_NA_M46_D102_NA_M41_D5
4_NA_M46_D102_NA_M41_D5
4_NA_M47_D114_NA_M47_D11
4_NA_M48_D124_NA_M44_D8
4_NA_M48_D124_NA_M44_D8
5_NA_M49_D1
5_NA_M50_D22_NA_M43_D75_NA_M50_D22_NA_M43_D7
5_NA_M51_D35_NA_M51_D34_NA_M45_D95_NA_M51_D34_NA_M45_D9
5_NA_M52_D45_NA_M52_D4
5_NA_M53_D5
5_NA_M54_D65_NA_M55_D7
5_NA_M56_D8
NA_NA_M57_D9
5_NA_M58_D105_NA_M52_D45_NA_M58_D105_NA_M52_D4
5_NA_M59_D115_NA_M59_D11
5_NA_M60_D125_NA_M60_D125_NA_M60_D122_NA_M43_D75_NA_M60_D122_NA_M43_D75_NA_M50_D25_NA_M60_D125_NA_M50_D22_NA_M43_D75_NA_M50_D22_NA_M43_D75_NA_M60_D122_NA_M43_D75_NA_M50_D22_NA_M43_D7
6_NA_M63_D35_NA_M52_D4
6_NA_M63_D35_NA_M52_D4
6_NA_M64_D4
6_NA_M65_D5
6_NA_M66_D6
6_NA_M67_D7
6_NA_M68_D86_NA_M68_D85_NA_M59_D11
6_NA_M68_D85_NA_M59_D115_NA_M59_D11
6_NA_M68_D85_NA_M59_D116_NA_M69_D95_NA_M59_D116_NA_M69_D95_NA_M59_D11
6_NA_M68_D86_NA_M69_D96_NA_M68_D85_NA_M59_D11
6_NA_M68_D85_NA_M59_D116_NA_M69_D95_NA_M59_D11
6_NA_M68_D85_NA_M59_D11
3_NA_M70_D10
3_NA_M71_D113_NA_M71_D113_NA_M71_D115_NA_M55_D73_NA_M71_D115_NA_M55_D76_NA_M63_D33_NA_M71_D116_NA_M63_D3
3_NA_M72_D125_NA_M51_D33_NA_M72_D125_NA_M51_D34_NA_M45_D95_NA_M51_D34_NA_M45_D93_NA_M72_D12
4_NA_M45_D95_NA_M51_D34_NA_M45_D9
3_NA_M73_D13_NA_M74_D23_NA_M74_D23_NA_M73_D13_NA_M74_D23_NA_M73_D1
3_NA_M75_D3
5_NA_M76_D44_NA_M47_D115_NA_M76_D44_NA_M47_D11
5_NA_M80_D85_NA_M80_D83_NA_M70_D105_NA_M80_D83_NA_M70_D10
Initial inoculum
Can FMT ‘lay the golden egg’?
14 d 1 d
5x
M34_D
10−1
M35_D
11−1
M36_D
12−1
M37_D
1−1
M38_D
2−1
M39_D
3−2
M41_D
5−2
M42_D
6−2
M43_D
7−2
M70_D
10−3
M71_D
11−3
M72_D
12−3
M73_D
1−3
M74_D
2−3
M75_D
3−3
M44_D
8−4
M45_D
9−4
M46_D
10−4
M47_D
11−4
M48_D
12−4
M49_D
1−5
M50_D
2−5
M51_D
3−5
M52_D
4−5
M53_D
5−5
M54_D
6−5
M55_D
7−5
M56_D
8−5
M58_D
10−5
M59_D
11−5
M60_D
12−5
M76_D
4−5
M80_D
8−5
M63_D
3−6
M64_D
4−6
M65_D
5−6
M66_D
6−6
M67_D
7−6
M68_D
8−6
M69_D
9−6
M57_D
9−NA
Pro
p. of sequences
0.0
0.2
0.4
0.6
0.8
1.0
Lactobacillus
Ruminiclostridium
Blautia
Lachnospiraceae
Eubacterium
Dehalobacter
Bacteroides
Subdoligranulum
Olsenella
Anaerobacterium
Faecalicoccus
Erysipelatoclostridium
Gracilibacter
AcholeplasmaRuminococcus
Spiroplasma
Faecalibacterium
AlistipesPapillibacter
AnaerocolumnaParabacteroides
Intestinimonas
Barnesiella
Acetivibrio
AlkaliphilusPeptococcus
Microbacter
Sporobacter
Clostridium
Anaerostipes
Can FMT ‘lay the golden egg’?
14 d 1 d
5x
1 2 3 4 5 6
0.0
0.1
0.2
0.3
0.4
0.5
Bacteroides
0.000215809557482717
Can FMT ‘lay the golden egg’?
FMT+
FMT-
!!!
!!!
!!! !
!!
0
30
60
90
2 4 6day post challenge day post challenge
body
wei
ght (
g)
body
wei
ght (
g)Treatment!
!
!
Clean
Dirty
PBS
!!!
!!!
!
!! !
!
!
0
30
60
90
2 4 6
Treatment!
!
!
Clean
Dirty
PBS
A) No challenge B) Challenge
!
(15)
m20−4, 96.7
mg20−4, 99.6c2−6−12, 96.9m1−10−15, 97.6
m2−9, 99.2
m20−1, 99.6c20−1, 99.5
m20−5, 99.6
c1−3c, 99.8c10−3, 99.7c1−4a, 100
c10−4, 98.5c20−4, 98.8
c20−3−3, 96.3m20−2, 96.5
mg20−2, 92.9
mg10−1, 99.2
c1−9a, 99.1c20−2, 98.6
c1−8, 95.4m1−9, 95.5
Ab7Poly2, Anaerobacter polyendosporusCloInte3, Clostridium intestinale
CloAlg18, Clostridium algidicarnisCloPutr3, Clostridium putrefaciens
CloMesop, Clostridium mesophilum
CloCaven, Clostridium cavendishiiCloSart2, Clostridium sartagoforme
CloButy9, Clostridium butyricumCloSacc9, Clostridium saccharoperbutylacetonicum N1−4(HMT)CloPunic, Clostridium puniceumCloPerf4, Clostridium perfringens
CloSpo17, Clostridium sporogenes
CloHisto, Clostridium histolyticumCloProte, Clostridium proteolyticum
CloLimos, Clostridium limosum
CloDiff4, [Clostridium] difficileCloDiff3, [Clostridium] difficile
CloGlyc8, [Clostridium] glycolicumCloMayom, Clostridium mayombei
RumFaeci, Ruminococcus faecisRumTorq3, Ruminococcus torques ATCC 27756
BauHydro, Blautia hydrogenotrophica DSM 10507
CloAero2, Clostridium aerotolerans
CloXyla8, Clostridium xylanolyticum
FlfPlau3, Flavonifractor plautiiCisMinut, Christensenella minuta
Bacillus
0.10
CloIrre2, [Clostridium] irregulare
Microbiome datasets can guide targeted cultivation of potentially novel strains from chicken G-I tract:
Novel strains shown in red genome sequenced
Strain Identification
Outer ring = consensus of cut sites Fragment sizes and order reflect true arrangement in genome Provides true barcode of genome Provides template for assembly of genome sequence reads
Characterization of new isolates "! Genome Sequencing "! Optical Mapping
Strain characterization
Characterization of new isolates "! Genome Sequencing "! Optical Mapping
Strain characterization
Identifying Differentially Abundant COGs between Cluster N and Cluster W)
Image adapted from: doi: 10.1128/mBio.00889-14 22 April 2014 mBio vol. 5 no. 2 e00889-14
Butyrate Synthesis Pathways of new strains !"#$%!
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4-Aminobutyrate Pathway
Y9! ZAD! Y9! Y9! Y9! Y9! Y9! ZAD! Y9! Y9! Y9! Y9! Y9! ZAD! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9!
Acetyl-CoA Pathway
ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! Y9! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! Y9! ZAD! ZAD! ZAD!
Glutarate Pathway Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9!
Lysine Pathway Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9! Y9!
Common Ending Pathway
Y9! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! Y9! ZAD! ZAD! ZAD! ZAD! ZAD! Y9! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! Y9! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! ZAD! Y9! ZAD! ZAD! ZAD! Y9! ZAD!
*Color Represents Presence or Absence of Pathway based on KEGG EC numbers found in Genome (allowing for 1 missing enzyme)
ZAD!!34565785!9:!3;<=>;?!Y9! !'@65785!9:!3;<=>;?!
A Majority of Uploaded Genomes can synthesize Butyrate from Acetyl-CoA according to their functional profile
CONCLUSIONS
#!Managing the microbiota can improve performance and food safety
#!Next-generation sequencing is a transformative tool
#!Proper management starts with understanding natural communities
#! ‘Efficacy-first’ inversion of traditional strain-centric approach may be valuable
Acknowledgements
USDA ARS Athens •! Bruce Seal •! Johnna Garrish •! Mark Berrang •! Nelson Cox •! Eric Line •! Jeff Buhr •! Seung-Chul Yoon •! Rick Meinersmann •! Cesar Morales •! Susan Brooks •! Jessica Johnson •! Raja Chalghoumi (Fulbright fellow)
CDC Atlanta •! Vladimir Loparev •! Rebecca Lindsey
UGA •! Sammy Aggrey •! Steve Collett •! Eldin Talundzic
SEPRL •! Laszlo Zak •! Michael Day
U.S. Poultry & Egg Association USDA 1433 Formula Funds USDA NIFA competitive grants
University of illinois •! Franck Carbonero •! Rex Gaskins
ARS College Station •! Mike Kogut
Western University •! Dr. Dominique Griffon •! Dr. Santiago Aguilar •! Ella Richardson •! Kim Calloway •! Jory Clark