By sensing changes in one or few environmental factors biological systems can anticipate future changes in multiple factors over a wide range of time scales (daily to seasonal). This anticipatory behavior is important to the fitness of diverse species, and in context of the diurnal cycle it is overall typical of eukaryotes and some photoautotrophic bacteria but is yet to be observed in archaea. Here, we report the first observation of light-dark (LD)-entrained diurnal oscillatory transcription in up to 12% of all genes of a halophilic archaeon Halobacterium salinarum NRC-1. Significantly, the diurnally entrained transcription was observed under constant darkness after removal of the LD stimulus (free-running rhythms). The memory of diurnal entrainment was also associated with the synchronization of oxic and anoxic physiologies to the LD cycle. Our results suggest that under nutrient limited conditions halophilic archaea take advantage of the causal influence of sunlight (via temperature) on O2 diffusivity in a closed hypersaline environment to streamline their physiology and operate oxically during nighttime and anoxically during daytime.
Citation: Whitehead K, Pan M, Masumura K-i, Bonneau R, Baliga NS (2009) Diurnally Entrained Anticipatory Behavior in Archaea. PLoS ONE4(5): e5485. https://doi.org/10.1371/journal.pone.0005485
Editor: Francisco Rodriguez-Valera, Universidad Miguel Hernandez, Spain
Received: March 16, 2009; Accepted: April 11, 2009; Published: May 8, 2009
Copyright: © 2009 Whitehead et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from NIH (P50GM076547 and 1R01GM077398-01A2), DoE (MAGGIE: DE-FG02-07ER64327 and DE-FG02-07ER64327), NSF (EF-0313754, EIA-0220153, MCB-0425825, DBI-0640950) and NASA (NNG05GN58G) to NSB and an NSF OPP Postdoctoral Fellowship to KW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The ability to anticipate impending environmental change(s) and mount a preparative response is crucial to the fitness of all organisms , . Such preparatory behavior has been observed over a wide range of time scales (e.g. daily or seasonal variations) and is mediated via sensing, internalizing and subsequently recalling fluctuation patterns in the specific environmental factor(s) (EFs). Interestingly, such behavior can also result from the ability of biological systems (even microorganisms) to internalize and use reproducible interrelationships among EFs such that by sensing a change in one or few EFs (i.e. proxy variables) they are informed of impending changes in other EFs . In other words, anticipatory or preparative behavior is a manifestation of gene regulatory networks that are appropriately structured to reproduce the cyclic nature and interrelatedness of EFs that have constrained their evolution .
In context of the diurnal cycle, anticipatory behavior appears widely throughout the eukaryotes and has been observed in some bacteria and is typical of organisms possessing circadian clocks . However, photoresponsive anticipatory behavior is yet to be observed in archaea. The halophilic archaeaon Halobacterium salinarum was considered a prime candidate for LD entrainment of transcription owing to the presence in its genome of genes for four opsins, one putative cryptochrome and an ortholog of the bacterial clock component KaiC . H. salinarum NRC-1 uses light as a source of information for physical relocation towards favorable wavelengths of light or away from damaging radiation , , , , . Under anoxic conditions it can use light-driven ion pumping by bacteriorhodopsin (bR) as means for producing ATP phototrophically , , . Taken together with substantial evidence for light-mediated global gene regulation in this organism , , , these observations make a compelling case for investigating the feasibility of entraining global expression changes in Halobacterium salinarum NRC-1 by prolonged culturing under diurnal 12h∶12h light∶dark (LD) cycles.
Here we present results of these experiments in which we detected free-running rhythms for at least 72 hours in up to 12% of all genes in H. salinarum NRC-1 under constant darkness post-entrainment with 3 days of LD cycles. Remarkably, we observe that despite maintaining constant O2 during this experiment, a significant fraction of cycling genes are those that are also independently regulated by changes in O2 concentration . This is interesting because O2 is another EF that has dominant influence on haloarchaeal physiology as a result of poor gas solubility in hypersaline environments. As such, we have previously demonstrated that a significant number of genes (at least 10%) in H. salinarum NRC-1 are differentially regulated as a direct consequence of changes in O2 availability . We conclude that H. salinarum can take advantage of coupled changes in sunlight and O2 such that it can use the L∶D cycle to anticipate higher levels of O2 during nighttime and lower levels during daytime. Given that entrainment of halobacterial physiology was best accomplished under nutrient limited condition we discuss this finding as a possible mechanism for maximizing resource utilization.
Results and Discussion
We investigated possible diurnal entrainment of gene expression in H. salinarum NRC-1 by subjecting cultures at various cell densities (Supplementary Table S1) to 72 hours of light∶dark (L∶D) changes on a 12∶12 hour cyclic schedule. Cells were harvested over 3 or 4 hour intervals for up to 75 hours in continuous darkness post-entrainment (Fig 1a and Supplementary Table S1). The cell pellets were flash frozen and subsequently processed for transcriptome analysis using whole genome microarray hybridization , . The duration of each experiment, sampling frequency and cell densities over which the experiments were conducted (as estimated by optical density (OD) at 600 nm) are reported in Supplementary Table S1.
a, H. salinarum NRC-1 cells were entrained with 3 days of 12∶12 LD and subsequently released into constant darkness. Total RNA was prepared from samples collected immediately post-entrainment (t = 0 hrs), every four hours until t = 60 hours. Two additional samples were collected at t = 64.5 hrs and t = 68.5 hrs. Culture conditions during sampling were frequently monitored and controlled (Supplementary Table S1). b, Frequency histogram of genes detected with periodic transcriptional changes (binned at intervals of 0.001 hr−1, p<0.2) using Lomb-Scargle analysis. c, Spectral density (black line) of the histogram in (a) shows two dominant frequencies of ∼12.5 and ∼21 hours; the blue swath shows data distribution of normally distributed gene expression changes. d, Five k-means clusters of periodic transcriptional changes of the 290 genes (from Experiment A) in (a, b) are visualized as a heatmap [average period is shown in parentheses and overrepresented GO or KEGG physiological functions (p<0.01) are also indicated]. The phasing of the diurnal L∶D cycle is indicated at the top of the heatmap with alternating white and shaded rectangles, respectively. e, Phase alignment of periodic gene expression changes shows co-induction of related cellular functions according to the diurnal cycle. DNA replication, tyrosine metabolism and ion-coupled transporters were upregulated during the middle of the light and dark phase with a period of 13.6 hours. Transcription of genes encoding components of NADH dehydrogenase (ndhG3 and ndhG4), cytochrome oxidase (coxB), the urea cycle and glutamine-glutamate metabolism peaked at the transitions from one phase to the next. Finally, nucleotide sugar metabolism, general sugar metabolism, and DNA integration were maximally induced during the latter half of the dark phase.
The resulting microarray data (Geo accession number: GSE15282) were analyzed for periodic expression patterns using the Lomb-Scargle (LS) method  (see methods for details). The LS analysis makes use of a least squares fit of sinusoidal curves to a given time series, and thus does not require evenly spaced data and is tolerant to missing data points . The null distribution for the periodogram was also derived to determine statistical significance (p-value) of detecting oscillatory gene expression patterns . The application of this analysis to 5 extended time courses (3 experimental and 2 controls with durations up to 75 hours with a 3–4 hour sampling frequency) allowed us to investigate oscillatory expression patterns with periods ranging from 6 hrs to >30 hrs. A gene was considered to have oscillatory behavior if a periodic pattern was detected in its expression with a p-value<0.2 in its respective LS periodogram (Figure 1b and Supplemental Figure S1). Consistent with the 3-day 12∶12 L∶D entrainment regimen, statistically significant periodic expression patterns with dominant periods of ∼13.0 hrs or ∼21 hrs were detected in a total of 290 genes (∼12.1% of the genome) in Experiment A and 230 genes (9.6% of the genome) in Experiment B (Fig 1c, Supplementary Figure S1). When expanded to include transcriptionally-linked genes within operons  (Koide et al., submitted to Mol Sys Bio) this represents potential periodic transcription of up to 636 genes in Expt A (27%) and 460 genes in Expt B (19%). An overlap of 167 genes between these gene-sets demonstrated significant reproducibility across the two experiments (p<10−8). Significantly, periodic gene expression with either of the two dominant frequencies was not observed at a lower cell density (OD600<0.4) (this is discussed further below), in control cultures that received no entrainment but were otherwise processed identically; or after shuffling/randomization of the expression-matrices (Supplementary Figure S1).
Genes with significant periodic expression patterns were further investigated in context of cellular physiology. This identified several classes of expression profiles, each with a distinct period and phasing and several with significant over-representation of diverse function categories (Fig 1d–e). This preliminary integrated analysis demonstrated the diurnal synchronization of a large number of linked enzymatic steps including key steps in the synthesis of nucleotides (Figure 2, Supplementary Figure S1B and Supplementary Table S2). Moreover, it was possible to phase-align several classes of oscillatory gene expression changes with the L∶D cycle (Fig 1e). This revealed that related cellular processes align well with respect to patterns of co-induction within the entrained transcriptional program.
Integrated analysis of transcriptional changes from a physiological context identified periodic expression of genes encoding key steps in energy production (TCA cycle and arginine metabolism), C- and N- assimilation (glutamate and arginine metabolism) and nucleotide biosynthesis. The inset graphs show transcriptional profiles (log10 ratios) of gene with a specific period.
Interestingly, diurnal entrainment of gene expression was maximally observed above a cell density (OD600>0.4) (Supplementary Figure S1) at which H. salinarum NRC-1 is known to undergo a large physiological transition that involves the differential regulation of over 63% of all genes (Facciotti et al., submitted to J. Bact) through diverse mechanisms including activation of a large number of alternate promoters, terminators and putative ncRNAs (Koide et al., submitted). Not surprisingly, transcription of a significant fraction of cycling genes is also independently induced at this growth phase (Experiment A: p = 7×10−5; Experiment B: p = 3.7×10−7). This growth-phase dependent phenomenon results from exhausted nutritional resources including decreased oxygen carrying capacity in the medium - conditions akin to those in the natural environment of H. salinarum NRC-1  (Facciotti et al., submitted). Consistent with this observation, the 135 transcripts that are both periodically induced upon diurnal entrainment (including 70 genes with peak expression during daytime) and also independently upregulated at high cell density are significantly enriched for anoxic functions  (Figure 3, Supplementary Table S3) . Surprisingly, the converse was also true - 45 transcripts that are typically downregulated at this growth phase and also independently repressed by a decrease in oxygen availability were also diurnally entrained with maximal expression during nighttime  (Fig 3; Supplementary Table S3). Remarkably, the distinct partitioning of periodic transcriptional changes in oxic and anoxic genes continues for at least 72 hours post-entrainment (Fig 3A). This clear split in oscillatory behavior of genes associated with oxic and anoxic functions strongly suggests synchronization and entrainment of oxygen-responsive physiologies according to the L∶D phase. Taken together these results demonstrate that nutrient and oxygen-limited conditions are the most conducive to entrainment with L∶D cycles – indicating perhaps the importance of synchronizing gene expression for efficient resource utilization under such conditions. Again, this periodic switching between oxic and anoxic physiologies was observed post-entrainment with the L∶D cycle, in constant darkness, and in culture conditions that were controlled to maintain constant dissolved oxygen (Supplementary Table S1). However, one could argue that natural oxygen consumption during aerobic growth and the subsequent adaptive shift to anaerobic physiology might have induced spontaneous cycling of oxic and anoxic gene expression similar to a phenomenon observed during continuous culturing of yeast . We can rule out such a phenomenon because control experiments that were conducted simultaneously and at the same cell density did not result in oscillatory expression of oxic and anoxic physiology genes. Thus, we conclude that the 12∶12 L∶D entrainment indirectly induced cycling of oxygen-related physiologies and speculate that this might be an outcome of a natural relationship between changes in light and oxygen that has been internalized by H. salinarum NRC-1. This was initially intriguing because in most aquatic environments the direct physical coupling between light and oxygen via temperature is often confounded by diverse hydrological (river inflow, tides, rainfall, etc.) and biological (e.g. the balance between photosynthesis and respiration) phenomena , . Further investigation into the physical characteristics of the natural hypersaline environment of halophilic archaea provided clues into the potential implication of light-mediated entrainment of oxygen-associated physiologies.
Three classes of average mRNA profiles for 180 of the 290 genes detected as cyclers in Experiment A. Expression profiles in all three panels are color-matched to indicate transcript profiles for the same three sets of genes over the LD cycle (A), in response to oxygen (B) and during growth in a batch culture (C). In panel A The period of oscillations in transcription upon entrainments is indicated as is the L∶D cycle (open∶grey boxes). Average transcript level changes in the same three groups of genes are plotted over the course of the growth curve for H. salinarum NRC-1 (data from Facciotii et al. submitted). (C) The transcriptional response of these genes to sudden inflow of O2 after >6 hrs of anoxia [O2 levels are shown as a magenta dotted line (see secondary y-axis)] (Schmid et al. 2007).
Extreme haloarchaea such as H. salinarum NRC-1 thrive in closed ponds or terminal lake systems (such as the Great Salt Lake or the Dead Sea) with salinities in excess of 100–150 g salt L−1 . Oxygen solubility is extremely poor at such high salinities and, not surprisingly, in addition to aerobic respiration most halophilic organisms also require alternate means of energy production such as phototrophy, denitrification and other dissimilatory processes , , , . Adaptive responses that enable efficient conditional switching between these varied modes of energy production are critical for the energetically expensive lifestyle of halophilic organisms , , , , , . For instance, in these environments, temperature and salinity are generally considered to be the dominant parameters influencing dissolved oxygen content  as biological primary production (photosynthesis) is greatly reduced , . The physicochemical dependence of O2 concentration on temperature and salinity is well known , . Notably, concentration of dissolved O2 in water drops as its temperature goes up; the solubility of O2 at 0°C is about twice its solubility at 30°C. Furthermore, there is evidence for an average diurnal cycle of 1–2°C in surface temperature of Great Salt Lake, a prototypically closed hypersaline ecosystem, with lower temperatures at nighttime . Hence, higher oxygen levels are generally expected at nighttime relative to the warmer daytime period. Our data suggests that this physicochemical relationship between light and oxygen in the natural closed hypersaline environment has been imprinted onto the regulatory architecture of indigenous organisms such as H. salinarum NRC-1. In other words, under nutrient limited conditions halophilic archaea take advantage of this relationship to streamline their physiology by anticipating present and future linked changes in oxygen availability and operate oxically during nighttime and anoxically during daytime.
While such anticipatory behavior has been observed over shorter time scales , this study shows sustained oscillations in oxic/anoxic transitions over longer time scales through several cell divisions even after the L∶D stimulus is removed and the cells are maintained under constant conditions. Large families of haloarchaeal regulatory proteins (signal transducers and TFs) with physically linked domains for sensing light and oxygen are further evidence of tight coupling between these environmental factors and the biological architecture of the gene regulatory networks , , . Finally, the discovery of diurnal entrainment of gene expression in an archaeon also raises important questions regarding the origin of light-responsive clock mechanisms. This is because archaeal information processing machinery is assembled from components that share ancestry with eukaryotic (general transcription factors and RNA polymerase) and bacterial (sequence-specific transcription regulators) counterparts . Furthermore, components of both bacterial ,  and eukaryotic  clocks are encoded in its genome , . Indeed, further detailed experimentation is necessary to ascertain precise phasing, temperature compensation, adaptability to different periods of entrainment etc. to ascertain the mechanistic underpinnings of this diurnal entrainment and its physiological implications. Nonetheless, our results demonstrate that even extremophilic archaea can use the diurnal day/night cycle to their advantage by anticipating future physicochemically linked changes in other EFs.
Materials and Methods
Culturing, sampling and RNA extraction
Wild type Halobacterium salinarium NRC-1 was cultured from a single colony in Complete Medium (CM) ; at 37°C with shaking at 125 rpm (Innova Waterbath, NewBrunswick Scientific, Edison, NJ). Cells were incubated under entrainment conditions (12∶12 L∶D cycle; daylight was simulated with full spectrum light at 150 µE/m2/s) or in continuous darkness (control) for three to four days prior to sampling. Post-entrainment Samples (2 ml) were collected periodically (every 3–4 hours) for up to 72 hours in continuous darkness, under constant cell density. The cell density was maintained by replacing a fixed volume in the culture (typically 30 mls) with equivalent of fresh CM every 3–4 hours . Comparative analysis with a similarly processed control culture discounted any unaccounted perturbations that were introduced by this periodic dilution. Cell pellets were harvested by centrifugation at 1600 rcf for 2 min, decanted, flash-frozen in liquid N2 and stored at −80° until RNA extraction. Total RNA was prepared using the Absolutely RNA kit (Qiagen, Foster City, CA, USA). RNA quality was examined by spectrophotometry and BioAnalyzer (Agilent, Santa Clara, CA, USA) analyses and DNA contamination was ruled out by PCR with 16S rDNA primers.
H. salinarum NRC-1 microarrays were fabricated at the Institute for Systems Biology Microarray Facility. Each microarray slide contains a unique 70mer oligonucleotide for each of the 2400 genes spotted in quadruplicate at two spatially distinct locations. Labeling, hybridization and washing have been previously described . Statistical significance of differential gene expression was determined using the maximum likelihood method . All microarray data reported in the manuscript is described in accordance with MIAME guidelines.
To examine the relative periodicity of genes in the constant light and constant dark experiments we used the Lomb normalized periodogram to estimate the spectral power as a function of angular frequency , , , . This method can be used to evaluate whether a given gene is periodic or is the result of noise or some other non-periodic process (a p-value associated with the significance of the each peak in the periodogram can be easily calculated). There are other methods that would allow us to calculate periodograms and statistically evaluate whether a signal was truly periodic , , ; we chose the Lomb periodogram in part because it does not require evenly sampled data. Further, obeying the Nyquist limit, the highest frequency allowed was 0.167 hr−1 (period = 6 hrs). For our analysis the lowest frequency detected was 0.033 hr−1 (period = 30 hrs). This allowed for detection of a 24 hour signal and also for the experiment duration to contain two full periods over which to detect.
The Lomb periodogram, PN(ω), is calculated as follows:where mean and variance are calculated as per usual:Tau is an offset that makes PN(ω) invariant to shifts in all time-points by a constant; tau is defined by the relation:This offset removes phase from the calculation. The Lomb periodogram is analogous to least squares fitting of sins and cosines to our signal in the time domain.
The significance of periodicity of expression changes for each gene is then calculated as the probability that a peak in the periodogram with intensity greater than z is due to a random or non-periodic process :Where M is the effective number of independent frequencies sampled, which in our case is well approximated by N, the number of samples . Thus, for each of the 2400 unique genes the analysis of a single time series resulted in a spectrogram and the significance of the maximum peak in that spectrogram.
A. Results from Lomb-Scargle analysis are presented as periodograms for each experiment described in Supplementary Table 1. Only genes with p<0.2 were considered to be cyclic in their expression pattern. Note that a strong banding pattern with p<0.2 is only observed in experiments A and B. B. Reproducibility of periodic transcriptional changes in 12 genes of diverse functions post-entrainment with three days of 12∶12 LD. Transcriptional changes over 48 hours of “memory” phase are shown along with putative functions.
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Experiment design, culturing parameters and sampling schedule.
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Genes with oscillatory gene expression profiles in Experiments A and B, period of oscilattion and significance.
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We thank Monica Orellana for insightful comments and her expert advice on O2 solubility in marine and hypersaline waters. We also thank Carl Johnson for guidance in setting up the diurnal entrainment experiments and analyzing data.
Conceived and designed the experiments: KW KiM RB NSB. Performed the experiments: KW KiM MP NSB. Analyzed the data: KW KiM RB NSB. Contributed reagents/materials/analysis tools: KW RB NSB. Wrote the paper: KW RB NSB.
- 1. Johnson CH, Mori T, Xu Y (2008) A cyanobacterial circadian clockwork. Curr Biol 18: R816–R825.CH JohnsonT. MoriY. Xu2008A cyanobacterial circadian clockwork.Curr Biol18R816R825
- 2. Dodd AN, Salathia N, Hall A, Kevei E, Toth R, et al. (2005) Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science 309: 630–633.AN DoddN. SalathiaA. HallE. KeveiR. Toth2005Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage.Science309630633
- 3. Tagkopoulos I, Liu Y-C, Tavazoie S (2008) Predictive Behavior Within Microbial Genetic Networks. Science 1154456.I. TagkopoulosY-C LiuS. Tavazoie2008Predictive Behavior Within Microbial Genetic Networks.Science1154456
- 4. Baliga NS (2008) The scale of prediction. Science 320: 1297–1298.NS Baliga2008The scale of prediction.Science32012971298
- 5. Bell-Pedersen D, Cassone VM, Earnest DJ, Golden SS, Hardin PE, et al. (2005) Circadian rhythms from multiple oscillators: lessons from diverse organisms. Nat Rev Genet 6: 544–556.D. Bell-PedersenVM CassoneDJ EarnestSS GoldenPE Hardin2005Circadian rhythms from multiple oscillators: lessons from diverse organisms.Nat Rev Genet6544556
- 6. DasSarma S, Kennedy SP, Berquist B, Ng W-LV, Baliga NS, Spudich JL, Krebs MP, Eisen JA, Johnson CH, Hood L (2002) Genomic perspective on the photobiology of Halobacterium species NRC-1, a phototrophic, phototactic, and UV-tolerant haloarchaeon. Photosynthesis Research 70: 3–17.S. DasSarmaSP KennedyB. BerquistW-LV NgNS BaligaJL SpudichMP KrebsJA EisenCH JohnsonL. Hood2002Genomic perspective on the photobiology of Halobacterium species NRC-1, a phototrophic, phototactic, and UV-tolerant haloarchaeon.Photosynthesis Research70317
- 7. Hoff WD, Jung KH, Spudich JL (1997) Molecular mechanism of photosignaling by archaeal sensory rhodopsins. Annu Rev Biophys Biomol Struct 26: 223–258.WD HoffKH JungJL Spudich1997Molecular mechanism of photosignaling by archaeal sensory rhodopsins.Annu Rev Biophys Biomol Struct26223258
- 8. Spudich EN, Takahashi T, Spudich JL (1989) Sensory rhodopsins I and II modulate a methylation/demethylation system in Halobacterium halobium phototaxis. Proc Natl Acad Sci U S A 86: 7746–7750.EN SpudichT. TakahashiJL Spudich1989Sensory rhodopsins I and II modulate a methylation/demethylation system in Halobacterium halobium phototaxis.Proc Natl Acad Sci U S A8677467750
- 9. Spudich JL (1993) Color sensing in the Archaea: a eukaryotic-like receptor coupled to a prokaryotic transducer. J Bacteriol 175: 7755–7761.JL Spudich1993Color sensing in the Archaea: a eukaryotic-like receptor coupled to a prokaryotic transducer.J Bacteriol17577557761
- 10. Spudich JL, Bogomolni RA (1984) Mechanism of colour discrimination by a bacterial sensory rhodopsin. Nature 312: 509–513.JL SpudichRA Bogomolni1984Mechanism of colour discrimination by a bacterial sensory rhodopsin.Nature312509513
- 11. Spudich JL, Luecke H (2002) Sensory rhodopsin II: functional insights from structure. Curr Opin Struct Biol 12: 540–546.JL SpudichH. Luecke2002Sensory rhodopsin II: functional insights from structure.Curr Opin Struct Biol12540546
- 12. Krebs MP, Khorana HG (1993) Mechanism of light-dependent proton translocation by bacteriorhodopsin. J Bacteriol 175: 1555–1560.MP KrebsHG Khorana1993Mechanism of light-dependent proton translocation by bacteriorhodopsin.J Bacteriol17515551560
- 13. Sumper M, Reitmeier H, Oesterhelt D (1976) Biosynthesis of the purple membrane of halobacteria. Angew Chem Int Ed Engl 15: 187–194.M. SumperH. ReitmeierD. Oesterhelt1976Biosynthesis of the purple membrane of halobacteria.Angew Chem Int Ed Engl15187194
- 14. Hartmann R, Sickinger HD, Oesterhelt D (1980) Anaerobic growth of halobacteria. Proc Natl Acad Sci U S A 77: 3821–3825.R. HartmannHD SickingerD. Oesterhelt1980Anaerobic growth of halobacteria.Proc Natl Acad Sci U S A7738213825
- 15. Baliga NS, Kennedy SP, Ng WV, Hood L, DasSarma S (2001) Genomic and genetic dissection of an archaeal regulon. Proc Natl Acad Sci U S A 98: 2521–2525.NS BaligaSP KennedyWV NgL. HoodS. DasSarma2001Genomic and genetic dissection of an archaeal regulon.Proc Natl Acad Sci U S A9825212525
- 16. Baliga NS, Pan M, Goo YA, Yi EC, Goodlett DR, et al. (2002) Coordinate regulation of energy transduction modules in Halobacterium sp. analyzed by a global systems approach. Proc Natl Acad Sci U S A 99: 14913–14918.NS BaligaM. PanYA GooEC YiDR Goodlett2002Coordinate regulation of energy transduction modules in Halobacterium sp. analyzed by a global systems approach.Proc Natl Acad Sci U S A991491314918
- 17. Twellmeyer J, Wende A, Wolfertz J, Pfeiffer F, Panhuysen M, et al. (2007) Microarray Analysis in the Archaeon Halobacterium salinarum Strain R1. PLoS ONE 2: e1064.J. TwellmeyerA. WendeJ. WolfertzF. PfeifferM. Panhuysen2007Microarray Analysis in the Archaeon Halobacterium salinarum Strain R1.PLoS ONE2e1064
- 18. Schmid AK, Reiss DJ, Kaur A, Pan M, King N, et al. (2007) The anatomy of microbial cell state transitions in response to oxygen. Genome Res 17: 1399–1413.AK SchmidDJ ReissA. KaurM. PanN. King2007The anatomy of microbial cell state transitions in response to oxygen.Genome Res1713991413
- 19. Ideker T, Thorsson V, Siegel AF, Hood LE (2000) Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. J Comput Biol 7: 805–817.T. IdekerV. ThorssonAF SiegelLE Hood2000Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.J Comput Biol7805817
- 20. Glynn EF, Chen J, Mushegian AR (2006) Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms. Bioinformatics 22: 310–316.EF GlynnJ. ChenAR Mushegian2006Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms.Bioinformatics22310316
- 21. Lomb NR (1976) Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science 39: 447–462.NR Lomb1976Least-squares frequency analysis of unequally spaced data.Astrophysics and Space Science39447462
- 22. Scargle JD (1982) Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data. Astrophysical Journal 263: 835–853.JD Scargle1982Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data.Astrophysical Journal263835853
- 23. Moreno-Hagelsieb G, Collado-Vides J (2002) A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics 18: Suppl 1S329–336.G. Moreno-HagelsiebJ. Collado-Vides2002A powerful non-homology method for the prediction of operons in prokaryotes.Bioinformatics18Suppl 1S329336
- 24. Oren A (2001) The bioenergetic basis for the decrease in metabolic diversity at increasing salt concentrations: implications for the functioning of salt lake ecosystems. Hydrobiolgia 466: 61–72.A. Oren2001The bioenergetic basis for the decrease in metabolic diversity at increasing salt concentrations: implications for the functioning of salt lake ecosystems.Hydrobiolgia4666172
- 25. Tu BP, Kudlicki A, Rowicka M, McKnight SL (2005) Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science 310: 1152–1158.BP TuA. KudlickiM. RowickaSL McKnight2005Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes.Science31011521158
- 26. Saenger C, Miller M, Smittenberg RH, Sachs JP (2006) A physico-chemical survey of inland lakes and saline ponds: Christmas Island (Kiritimati) and Washington (Teraina) Islands, Republic of Kiribati. Saline Systems 2: 8.C. SaengerM. MillerRH SmittenbergJP Sachs2006A physico-chemical survey of inland lakes and saline ponds: Christmas Island (Kiritimati) and Washington (Teraina) Islands, Republic of Kiribati.Saline Systems28
- 27. Valdez-Holguin JE (1994) Daily Variations Of Temperature, Salinity, Dissolved Oxygen And Chlorophyll a, In A Hypersaline Lagoon Of The Gulf Of California. Ciencias Marinas 20: 123–137.JE Valdez-Holguin1994Daily Variations Of Temperature, Salinity, Dissolved Oxygen And Chlorophyll a, In A Hypersaline Lagoon Of The Gulf Of California.Ciencias Marinas20123137
- 28. Oren A (2008) Microbial life at high salt concentrations: phylogenetic and metabolic diversity. Saline Systems 4: 2.A. Oren2008Microbial life at high salt concentrations: phylogenetic and metabolic diversity.Saline Systems42
- 29. Baliga NS, Bonneau R, Facciotti MT, Pan M, Glusman G, et al. (2004) Genome sequence of Haloarcula marismortui: A halophilic archaeon from the Dead Sea. Genome Res 14: 2221–2234.NS BaligaR. BonneauMT FacciottiM. PanG. Glusman2004Genome sequence of Haloarcula marismortui: A halophilic archaeon from the Dead Sea.Genome Res1422212234
- 30. Bolhuis H, Palm P, Wende A, Falb M, Rampp M, et al. (2006) The genome of the square archaeon Haloquadratum walsbyi : life at the limits of water activity. BMC Genomics 7: 169.H. BolhuisP. PalmA. WendeM. FalbM. Rampp2006The genome of the square archaeon Haloquadratum walsbyi : life at the limits of water activity.BMC Genomics7169
- 31. Falb M, Pfeiffer F, Palm P, Rodewald K, Hickmann V, et al. (2005) Living with two extremes: conclusions from the genome sequence of Natronomonas pharaonis. Genome Res 15: 1336–1343.M. FalbF. PfeifferP. PalmK. RodewaldV. Hickmann2005Living with two extremes: conclusions from the genome sequence of Natronomonas pharaonis.Genome Res1513361343
- 32. Ng WV, Kennedy SP, Mahairas GG, Berquist B, Pan M, et al. (2000) From the cover: genome sequence of halobacterium species NRC-1. Proc Natl Acad Sci U S A 97: 12176–12181.WV NgSP KennedyGG MahairasB. BerquistM. Pan2000From the cover: genome sequence of halobacterium species NRC-1.Proc Natl Acad Sci U S A971217612181
- 33. Betlach MC, Shand RF, Leong DM (1989) Regulation of the bacterio-opsin gene of a halophilic archaebacterium. Can J Microbiol 35: 134–140.MC BetlachRF ShandDM Leong1989Regulation of the bacterio-opsin gene of a halophilic archaebacterium.Can J Microbiol35134140
- 34. Muller JA, DasSarma S (2005) Genomic analysis of anaerobic respiration in the archaeon Halobacterium sp. strain NRC-1: dimethyl sulfoxide and trimethylamine N-oxide as terminal electron acceptors. J Bacteriol 187: 1659–1667.JA MullerS. DasSarma2005Genomic analysis of anaerobic respiration in the archaeon Halobacterium sp. strain NRC-1: dimethyl sulfoxide and trimethylamine N-oxide as terminal electron acceptors.J Bacteriol18716591667
- 35. Ruepp A, Soppa J (1996) Fermentative arginine degradation in Halobacterium salinarium (formerly Halobacterium halobium): genes, gene products, and transcripts of the arcRACB gene cluster. J Bacteriol 178: 4942–4947.A. RueppJ. Soppa1996Fermentative arginine degradation in Halobacterium salinarium (formerly Halobacterium halobium): genes, gene products, and transcripts of the arcRACB gene cluster.J Bacteriol17849424947
- 36. Shand RF, Betlach MC (1991) Expression of the bop gene cluster of Halobacterium halobium is induced by low oxygen tension and by light. J Bacteriol 173: 4692–4699.RF ShandMC Betlach1991Expression of the bop gene cluster of Halobacterium halobium is induced by low oxygen tension and by light.J Bacteriol17346924699
- 37. Ashton PJ, Schoeman FR (1983) Limnological studies on the Pretoria Salt Pan, a hypersaline maar lake. Hydrobiologia 99: 61–73.PJ AshtonFR Schoeman1983Limnological studies on the Pretoria Salt Pan, a hypersaline maar lake.Hydrobiologia996173
- 38. Pinckney J, Paerl HW, Bebout BM (1995) Salinity control of benthic microbial mat community production in a Bahamian hypersaline lagoon. Journal of Experimental Marine Biology and Ecology 187: 223–237.J. PinckneyHW PaerlBM Bebout1995Salinity control of benthic microbial mat community production in a Bahamian hypersaline lagoon.Journal of Experimental Marine Biology and Ecology187223237
- 39. Wieland A, Kuhl M (2006) Regulation of photosynthesis and oxygen consumption in a hypersaline cyanobacterial mat (Camargue, France) by irradiance, temperature and salinity. FEMS Microbiol Ecol 55: 195–210.A. WielandM. Kuhl2006Regulation of photosynthesis and oxygen consumption in a hypersaline cyanobacterial mat (Camargue, France) by irradiance, temperature and salinity.FEMS Microbiol Ecol55195210
- 40. Stumm W, Morgan JJ (1981) Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibra In Natural Waters. New York: John Wiley & Sons. W. StummJJ Morgan1981Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibra In Natural WatersNew YorkJohn Wiley & Sons780
- 41. Han P, Bartels DM (1996) Temperature dependence of oxygen diffusion in H2O and D2O. J Phys Chem 100: 5597–5602.P. HanDM Bartels1996Temperature dependence of oxygen diffusion in H2O and D2O.J Phys Chem10055975602
- 42. Department of Meteorology UoU Annual temperature cycle obtained from AVHRR (1981–2004) http://www.met.utah.edu/research/saltlake/remote/climo_html/. Department of Meteorology UoU Annual temperature cycle obtained from AVHRR (1981–2004) http://www.met.utah.edu/research/saltlake/remote/climo_html/.
- 43. Mascher T, Helmann JD, Unden G (2006) Stimulus Perception in Bacterial Signal-Transducing Histidine Kinases. Microbiol Mol Biol Rev 70: 910–938.T. MascherJD HelmannG. Unden2006Stimulus Perception in Bacterial Signal-Transducing Histidine Kinases.Microbiol Mol Biol Rev70910938
- 44. Geiduschek EP, Ouhammouch M (2005) Archaeal transcription and its regulators. Mol Microbiol 56: 1397–1407.EP GeiduschekM. Ouhammouch2005Archaeal transcription and its regulators.Mol Microbiol5613971407
- 45. Dvornyk V, Vinogradova O, Nevo E (2003) Origin and evolution of circadian clock genes in prokaryotes. Proc Natl Acad Sci U S A 100: 2495–2500.V. DvornykO. VinogradovaE. Nevo2003Origin and evolution of circadian clock genes in prokaryotes.Proc Natl Acad Sci U S A10024952500
- 46. Taniguchi Y, Katayama M, Ito R, Takai N, Kondo T, et al. (2007) labA: a novel gene required for negative feedback regulation of the cyanobacterial circadian clock protein KaiC. Genes Dev 21: 60–70.Y. TaniguchiM. KatayamaR. ItoN. TakaiT. Kondo2007labA: a novel gene required for negative feedback regulation of the cyanobacterial circadian clock protein KaiC.Genes Dev216070
- 47. Lin C, Todo T (2005) The cryptochromes. Genome Biol 6: 220.C. LinT. Todo2005The cryptochromes.Genome Biol6220
- 48. DasSarma S, Arora P (1999) Halophiles. Encyclopedia of Life Sciences: Macmillan Press. London: Nature Publishing Group. pp. 458–466.S. DasSarmaP. Arora1999Halophiles. Encyclopedia of Life Sciences: Macmillan PressLondonNature Publishing Group458466
- 49. Mori T, Binder B, Johnson CH (1996) Circadian gating of cell division in cyanobacteria growing with average doubling times of less than 24 hours. Proc Natl Acad Sci U S A 93: 10183–10188.T. MoriB. BinderCH Johnson1996Circadian gating of cell division in cyanobacteria growing with average doubling times of less than 24 hours.Proc Natl Acad Sci U S A931018310188
- 50. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical Recipes in C: the art of scientific computing. Cambridge: Cambridge University Press. WH PressBP FlannerySA TeukolskyWT Vetterling1992Numerical Recipes in C: the art of scientific computingCambridgeCambridge University Press
- 51. Vityazev VV (1997) Time series analysis of unevenly spaced data: Intercomparison between estimators of the power spectrum. Astronomical data analysis, software and systems VI 125: 166–169.VV Vityazev1997Time series analysis of unevenly spaced data: Intercomparison between estimators of the power spectrum.Astronomical data analysis, software and systems VI125166169
- 52. Lomb NR (1976) Astrophysics and Space Science 39: 447–462.NR Lomb1976Astrophysics and Space Science39447462
- 53. Wichert S, Fokianos K, Strimmer K (2004) Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20: 5–20.S. WichertK. FokianosK. Strimmer2004Identifying periodically expressed transcripts in microarray time series data.Bioinformatics20520
- 54. Scargle JD (1982) Astrophysics Journal 263: 835–853.JD Scargle1982Astrophysics Journal263835853
- 55. Horne JH, Baliunas SL (1986) Astrophysics Journal 302: 757–763.JH HorneSL Baliunas1986Astrophysics Journal302757763
- 56. Bare JC, Shannon PT, Schmid AK, Baliga NS (2007) The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications. BMC Bioinformatics 8: 456.JC BarePT ShannonAK SchmidNS Baliga2007The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications.BMC Bioinformatics8456
- 57. Shannon P, Reiss DJ, Bonneau R, Baliga NS (2006) Gaggle: An open-source software system for integrating bioinformatics software and data sources. BMC Bioinformatics 7: 176.P. ShannonDJ ReissR. BonneauNS Baliga2006Gaggle: An open-source software system for integrating bioinformatics software and data sources.BMC Bioinformatics7176