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Integration of Prokaryotic Genomics into the Unknown Microbe ID Lab Bert Eardley – Penn State, Berks & Dan Golemboski – Bellarmine University.

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Presentation on theme: "Integration of Prokaryotic Genomics into the Unknown Microbe ID Lab Bert Eardley – Penn State, Berks & Dan Golemboski – Bellarmine University."— Presentation transcript:

1 Integration of Prokaryotic Genomics into the Unknown Microbe ID Lab Bert Eardley – Penn State, Berks & Dan Golemboski – Bellarmine University

2 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

3 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


5 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

6 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

7 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)

8 Computer/Program Requirements Internet access, RAST account, MEGA

9 Time Line Pre-lab – Instructor selects strains of related genera as student unknowns Students register for access to RAST (Rapid Annotation Using Subsystem Technology; ) Lab 1 – Phenotypic identification of unknown Traditional biochemical analysis in typical laboratory time- frame: 4 -6 lab periods; dependent on level of automation available - could be shorter (i.e., API)

10 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.

11 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

12 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?

13 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

14 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. 215:403- 410. PubMed

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