Integrated genome-based studies of Shewanella ecophysiology
This project was a component of the Shewanella Federation and, as such, contributed to the overall goal of applying the genomic tools to better understand eco-physiology and speciation of respiratory-versatile members of Shewanella genus. Our role at Boston University was to perform bioreactor and high throughput gene expression microarrays, and combine dynamic flux balance modeling with experimentally obtained transcriptional and gene expression datasets from different growth conditions. In the first part of project, we designed the S. oneidensis microarray probes for Affymetrix Inc. (based in California), then we identified the pathways of carbon utilization in the metal-reducing marine bacterium Shewanella oneidensis MR-1, using our newly designed high-density oligonucleotide Affymetrix microarray on Shewanella cells grown with various carbon sources. Next, using a combination of experimental and computational approaches, we built algorithm and methods to integrate the transcriptional and metabolic regulatory networks of S. oneidensis. Specifically, we combined mRNA microarray and metabolite measurements with statistical inference and dynamic flux balance analysis (dFBA) to study the transcriptional response of S. oneidensis MR-1 as it passes through exponential, stationary, and transition phases. By measuring time-dependent mRNA expression levels during batch growth of S. oneidensis MR-1 under two radically different nutrient compositions (minimal lactate and nutritionally rich LB medium), we obtain detailed snapshots of the regulatory strategies used by this bacterium to cope with gradually changing nutrient availability. In addition to traditional clustering, which provides a first indication of major regulatory trends and transcription factors activities, we developed and implemented a new computational approach for Dynamic Detection of Transcriptional Triggers (D2T2). This new method allows us to infer a putative topology of transcriptional dependencies, with special emphasis on the nodes at which external stimuli are expected to affect the internal dynamics. In parallel, we addressed the question of how to compare transcriptional profiles across different time-course experiments. Our growth derivative mapping (GDM) method makes it possible to relate with each other points that correspond to the same relative growth rate in different media sets. This mapping allowed us to discriminate between genes that display an environment-independent behavior, and genes whose transcription seems to be tuned by specific environmental factors. Our analysis highlighted the importance of some specific pathways, whose metabolic relevance was confirmed by dynamic flux balance analysis (dFBA) calculations. In particular, we found that oxygen limitation potentially triggers the activation of genes previously shown to be relevant for anaerobic respiration, and that nitrogen limitation is coupled to storage of glycogen. Both observations have been corroborated by measurement of relevant intracellular and extracellular metabolites, as well as by complementary analyses of literature information and competitive fitness assay data. The pipeline of experimental and computational approaches applied and developed for this work could be extended to other microbes and additional conditions.
- Research Organization:
- Daniel Segre (Boston University) [Transferred in April 2009 from initial PI, Timothy Gardner (Boston University)]
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- DOE Contract Number:
- FG02-07ER64388
- OSTI ID:
- 1034753
- Report Number(s):
- DOE/ER/64388-F; TRN: US201216%%314
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
59 BASIC BIOLOGICAL SCIENCES
60 APPLIED LIFE SCIENCES
54 ENVIRONMENTAL SCIENCES
ALGORITHMS
BIOREACTORS
CARBON
CARBON SOURCES
DETECTION
GENES
GLYCOGEN
LACTATES
METABOLITES
NITROGEN
NUTRIENTS
OLIGONUCLEOTIDES
OXYGEN
PIPELINES
PROBES
RESPIRATION
STIMULI
STORAGE
TOPOLOGY
TRANSCRIPTION
TRANSCRIPTION FACTORS
Metabolic Networks
Gene expression
Shewanella
Bioenergy
Bioremediation
Systems Biology
Genome-scale mathematical models