Genome-Wide Transcriptional Variation within and between Steady States for Continuous Growth of the Hyperthermophile Thermotoga Maritima

Maltose-limited, continuous growth of the hyperthermophile Thermotogamaritima at different temperatures and dilution rates (80°C/0.25h^–1, 80°C/0.17 h^–1, and 85°C/0.25 h^–1)showed that transcriptome-wide variation in gene expressionwithin mechanical steady states was minimal compared to thatbetween steady states, supporting the efficacy of chemostat-basedapproaches for functional genomics studies.

INTRODUCTION

Top

Introduction

Continuous culture can be an effective tool for determiningglobal transcriptional patterns in functional genomics studies(5). It may also be useful for examining the fluctuation ingene expression that arises within a specific environmentalcontext or growth condition. It is not clear yet whether biovariabilityin gene expression in unperturbed cells impacts the interpretationof transcriptional response to intended perturbations. Thus,some of the variation between experimental conditions couldreflect the variation within an experimental condition. Thisissue was examined by using a whole-genome cDNA microarray forThermotoga maritima, an obligately anaerobic, hyperthermophilic,heterotrophic bacterium growing optimally at 80°C (2, 4,8). Chemostat-based transcriptional response experiments withwhole-genome cDNA microarrays were used to investigate sourcesof variance that contribute to the observed patterns of differentialgene expression both within and between mechanical steady states(temperature and dilution rate) by using three common proceduresused to assign differential gene expression.

Experimental overview.

Top

Experimental overview.

A full-genome Thermotoga maritima DNA microarray constructedfrom PCR products (3) was used to measure transcriptional variationduring continuous cultivation (6). Three mechanical steady stateschosen to reflect perturbations within the normal growth rangeof the organism were examined: 80°C at a dilution rate of0.25 h^–1 (six samples taken at 103.7, 109.3, 127.7, 133.4,151.8, and 157.5 h [19.4 generations]), 85°C at a dilutionrate of 0.25 h^–1 (four samples taken at 435.4, 459.6,483.4, and 501.7 h [23.9 generations]), and 80°C at a dilutionrate of 0.17 h^–1 (three samples taken at 879.0, 958.5,and 1,000.0 h [29.7 generations]) (Fig. 1). Isolation of totalRNA, hybridizations and washes were performed as described previously(9). Slides were scanned with a Scanarray 4000 scanner (PerkinElmer, Fremont, CA) and the raw intensity data were processedas described previously (1). The hybridization scheme was arrangedaccording to a loop design strategy, which allows statisticallyefficient comparisons between samples (11).

View larger version (18K):
[in this window]
[in a new window]
FIG. 1. Growth of Thermotoga maritima in continuous culture. Six samples (at different time points) were taken at 80°C at a dilution rate of 0.25 h^–1; four samples (at different time points) were taken at 85°C at a dilution rate of 0.25 h^–1; and three samples (at different time points) were taken at 80°C at a dilution rate of 0.17 h^–1. Sample points used in the study are indicated with the filled triangles. Optical densities of samples collected from the chemostat outflow were measured every hour for each steady state, and cell densities (squares) were determined by epifluorescent cell count enumeration. An attempt to stabilize the chemostat at a dilution rate of 0.25 h^–1 and 90°C (the reported upper temperature limit of growth for T. maritima [2]), prior to shifting to a lower dilution rate (0.17 h^–1) was made; however, washout occurred under these conditions. The solid line indicates temperature.

Simple "fold change" criteria and estimate-based probabilities.

Top

Simple "fold change" criteria...

Differential gene expression calls based on simple fold changecriteria were evaluated by pairwise t tests derived from least-squaresmean estimates from log₂-transformed raw signal intensity dataand mixed model analyses (1). The log₂-transformed analysisis referred to as "unnormalized" because systematic global variationremained confounded with growth state effects; important sourcesof variation were taken into account through mixed model analyses.Over 36% of the open reading frames (ORFs) on the array werechanged by the "twofold change rule" (Fig. 2A), but most ofthese simple fold change calls were not significant by statisticalcriteria. Even fold changes greater than 20-fold did not ensurethat changes were statistically significant (Fig. 2B and C).

View larger version (17K):
[in this window]
[in a new window]
FIG. 2. Gene expression calls for three different methods. (A) Shown is the distribution of simple fold change calls. Simple fold change was calculated by dividing the mean signal intensities from one sample by the mean signal intensities from a comparison sample, after balancing Cy3/Cy5 signal intensity channels across arrays. (B) Unnormalized log₂-transformed significance measures are shown as a function of simple fold changes. (C) Mixed model significance versus simple fold changes. A total of 148,368 comparisons were made in the experiment. The dotted lines in panels B and C correspond to a Bonferroni corrected significance level ( ${alpha}$ ) of 3.37 x 10^–7, representing a conservative cutoff that bounds the experiment-wise false-positive error rate at 0.05. Statistical tests based on unnormalized estimate differences were in close agreement with simple fold change methods for fold changes greater than 10-fold (data not shown), but estimates resulting from mixed model analyses did not depend strongly on degree of fold change for any fold change threshold.

Selected discrepancies between methods are shown in Table 1.The effect of growth rate on transcription response has yetto be characterized in T. maritima, but comparison with a previousstudy describing the dynamic expression of temperature-inducibleCIRCE-related genes (7) in the organism provided biologicalsupport for the utility of the mixed model procedure. Here,increased expression of the CIRCE-related genes were calledmore often using significance criteria from the mixed modelapproach than from the unnormalized method or twofold simplefold change cutoffs for growth at 85°C compared to growthat 80°C.

View this table:
[in this window]
[in a new window]
TABLE 1. Selected discrepant genes up-regulated due to temperature or growth rate effects^a

Biological trends in expression based on mixed model analyses.

Top

Biological trends in expression...

As visualized in the overall least-squares mean heat plot, expressionof most ORFs within a steady state was relatively consistentover time (Fig. 3). Such consistency within and between growthconditions was expected, given the relatively mild levels ofperturbation studied here. However, the mixed model approachled to a total of 386 differentially expressed ORFs (or about20% of the 1,907 ORFs examined; the full set of differentiallyexpressed genes will be published online at http://www.che.ncsu.edu/extremophiles/)in spite of the overall stability in expression (Fig. 4). Ofthis number, 102 transcripts were differentially regulated atthe elevated temperature and 320 transcripts were significantlydifferent at the reduced dilution rate (57 ORFs were modulatedin response to both perturbations). Most ORFs differentiallyexpressed between steady states (301/386 ORFs or about 88% ofthe final gene list) were not differentially expressed withina steady state, but those that were called indicate the needfor multiple samplings within a treatment condition.

View larger version (41K):
[in this window]
[in a new window]
FIG. 3. Hierarchical clusters constructed by using least-squares mean estimates. Harvest times (hours since reactor inoculation) and steady-state regions are indicated. Each row represents a different gene in the organism.

View larger version (23K):
[in this window]
[in a new window]
FIG. 4. Summary of transcriptional responses within and between steady states. Significant findings (adjusted P, <0.05) are reported for global transcriptional responses within and between the mechanical steady states at 80°C (dilution rates of 0.25 h^–1 and 0.17 h^–1) and 85°C (dilution rate of 0.25 h^–1). Differentially expressed ORFs within a given steady state for all three mechanical steady states are summed together and reported as "Steady State." Significant ORFs in each pie chart are mutually exclusive to the stated category.

Experiments designed to study the influence of growth temperatureon transcription in prokaryotes have usually been conductedin batch cultures, in which an increase in growth temperatureleads to a corresponding increase in cell growth rate. However,the continuous culture presented a means to investigate theeffects of temperature apart from growth rate considerations.While heat shock genes were induced in T. maritima at 85°Cand 80°C (Table 1), about 75% of the ORFs with significanttranscriptional differences due to growth temperature were down-regulatedat the higher growth temperature. These included genes frommost annotated prokaryotic clusters of functional categoriesof orthologous groups of proteins (COGs) (10). No COG functionalcategories were up-regulated due to growth at the higher temperature,but ORFs belonging to COG functional categories E (amino acidtransport and metabolism), G (carbohydrate transport and metabolism),K (transcription), P (inorganic ion transport and metabolism),and M (cell envelope biogenesis and lipid metabolism) were down-regulatedat the increased temperature. This downshift of major functionalgroups may indicate that at 85°C (5 degrees higher thanthe reported growth temperature optimum for this organism),T. maritima is devoting cellular resources to rescue and recoveryefforts.

Transcriptional adjustments also arose from a change in thedilution rate. Among the ORFs showing the most significant changeswere genes encoding a putative ammonium transporter, variousribosomal proteins, and enzymes involved in the processing ofthe growth substrate (maltose), which were all up-regulatedat the lower dilution rate (Table 1). Nearly one-third of allthe transcriptional differences due to dilution rate were ORFsencoding genes involved in the transport and metabolism of aminoacids and carbohydrates.

In conclusion, repeated samplings of three different mechanicalsteady states demonstrated that transcriptional variation wasremarkably constant in spite of possibly influential mechanismsoperating within and upon the vessel. Thus, the key findingof this study is that continuous culture used with appropriatestatistical models is an excellent tool for functional genomicsinvestigations.

ACKNOWLEDGMENTS

This work was supported in part by grants from the Departmentof Energy (Energy Biosciences Program) and the National ScienceFoundation (Biotechnology Program). K.R.S. was supported bya Department of Education GAANN Fellowship. S.B.C. was supportedby a National Institute of Environmental Health Sciences traineeshipin Bioinformatics.

FOOTNOTES

* Corresponding author. Mailing address: Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695-7905. Phone: (919) 515-6396. Fax: (919) 515-3465. E-mail: rmkelly{at}eos.ncsu.edu.

Present address: The Jackson Laboratory, 600 Main Street, BarHarbor, ME 04609.

Present address: Roswell Park Cancer Institute, Department ofPharmacology and Therapeutics, Elm and Carlton Streets, Buffalo,NY 14263.

REFERENCES

Top