Transcript Slide 1

Integration of
Prokaryotic Genomics into the
Unknown Microbe ID Lab
Bert Eardley – Penn State, Berks
&
Dan Golemboski – Bellarmine University
Background
• Add-on to traditional introductory microbiology unknown
identification lab; >6000 genome sequences available
• Many faculty use their unsequenced research organisms as
unknowns
• Prerequisites:
– Concurrent with microbiology lecture
– Understanding of basic cellular metabolism
• Identification of unknown bacterium, related to faculty/student
research interests, using traditional biochemical analysis and
subsequent genomic correlation to observed phenotypic traits
– Examples: Carbohydrate utilization, antibiotic resistance, motility,
anaerobic/aerobic, symbiotic capabilities, amino acid
requirements, BioLog/API, etc.
– Student develops hypothesis on identity of organism
Background (cont’d)
• Determine consistency of phenotypic
analysis with genotype
– Retrieve genomic sequence of each
identified organism
• Perform automated annotation (Rapid
Annotation Using Subsystem Technology, RAST)
• Compare computationally derived characteristics
to observed
• Rationalize inconsistencies between phenotype
and genotype
RAST
Student Learning Goals
• Predict which genes/subsystems should or should not
be present
• Integrate the annotated gene products into
subsystems that can be used to identify pathways
used to transform energy during growth
• Illustrate the interdisciplinary nature of genomics
• Correlate observed genotypes and phenotypes with
ecological niche
• Use sequence data to illustrate evolutionary
relatedness by construction of phylogenetic trees
Vision and Change Core
Competencies
• #1: Students design and perform experiments, make
observations, formulate hypothesis about identity of
unknowns, and predict gene content
• #2: Statistical analysis, such as bootstrapping in
phylogenetic tree construction; requires quantitative
reasoning
• #3: Compare phylogenetic trees with those generated
by other students; metabolic modeling with RAST
• #6: Use of sequence related technology to facilitate
identification of organisms of clinical, commercial, and
agricultural significance
GCAT-SEEK Requirements
• No sequencing will be required if publically
available sequences are sufficient.
• However, if the genome of an organism of
interest has not been sequenced then
appropriate technology will be utilized (i.e.,
MiSeq, Ion Torrent, 454)
Computer/Program
Requirements
• Internet access, RAST account, MEGA
Time Line
• Pre-lab
– Instructor selects strains of related genera as
student unknowns
• Students register for access to RAST (Rapid Annotation
Using Subsystem Technology; http://rast.nmpdr.org/ )
• Lab 1
– Phenotypic identification of unknown
• Traditional biochemical analysis in typical laboratory timeframe: 4 -6 lab periods; dependent on level of automation
available - could be shorter (i.e., API)
Time Line (cont’d)
• Post-identification Lab 1
– Prior to lab
• Retrieve genome sequence of proposed unknown type-strain
– Submit sequence to RAST for automated annotation
• Post-identification Lab 2
–
–
–
–
Identify subsystems associated with phenotypic traits
Determine gene common to all identified organisms
Using RAST, obtain selected gene sequence
BLAST sequence and select orthologs of species
identified by other students.
Time Line (cont’d)
• Post-identification Lab 3
– Use MEGA to align sequences
from the BLAST search
– Construct phylogenetic
tree using MEGA
– Discuss significance of
bootstrap values
– Discuss sequence
divergence and how
this is reflected in
phylogenetic trees
Bacillus indicus SJS
86
Bacillus subtilis
91
Exiguobacterium undae
100
Staphylococcus aureus
Lactococcus lactis
37
100
Streptococcus pyogenes
13
Prochlorococcus marinus
Geovibrio ferrireducens
25
Nitrospira moscoviensis
Aquifex pyrophilus
80
35
Thermomicrobium roseum
62
49
Chloroflexus aurantiacus
Corynebacterium callunae
Streptomyces coelicolor
100
30
Oerskovia jenensis
48
66
Arthrobacter aurescens
Neisseria gonorrhoeae
90
Aquaspirillum sinuosum
37
Pseudomonas aeruginosa
96
Acinetobacter johnsonii
65
75
Escherichia coli
Helicobacter pylori
Blastopirellula marina
90
Bdellovibrio bacteriovorus
Chryseobacterium indologenes
Pedobacter sandarakinus SJS
100
56
Cytophaga hutchinsonii
0.05
89
Lecture and Discussion Topics
• Relationships between phenotypes,
pathways, and genes
• How many changes to the genome are
necessary to define a species?
• What role does gene expression play in the
recognition of an observable phenotype?
Assessment
• Determine ability to identify organisms on the basis of phenotypic
analysis using established reference manual(s)
• Demonstrate ability to access database tools and perform RAST
annotation of a genomic sequence
• Determine ability to correlate genes to the particular phenotype
• Determine ability to use BLAST to obtain orthologous sequences
• Explain how genes diverge at the molecular level through the process
of evolution
• Determine students’ confidence in ability to construct phylogenetic
tree showing relationships among a group of bacteria
References
• Tamura K, Peterson D, Peterson N, Stecher G, Nei M, and Kumar
S (2011) MEGA5: Molecular Evolutionary Genetics Analysis
using Maximum Likelihood, Evolutionary Distance, and
Maximum Parsimony Methods. Molecular Biology and
Evolution 28: 2731-2739.
• Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA,
Formsma K, Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ,
Olson R, Osterman AL, Overbeek RA, McNeil LK, Paarmann D,
Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O,
Vonstein V, Wilke A, Zagnitko O., BMC Genomics, 2008.
• Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J.
(1990) "Basic local alignment search tool." J. Mol. Biol. 215:403410. PubMed