Does the growth temperature of a prokaryote influence the purine content of its mRNAs?

Mahale, Kiran N.; Kempraj, Vivek; Dasgupta, Debjani

doi:10.1016/j.gene.2012.01.040

Cited by 5 publications

(7 citation statements)

References 45 publications

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“…2A). Similar to the previous reports in prokaryotes (Lao and Forsdyke 2000;Mahale et al 2012), AG contents are negatively correlated with GC contents among all species (R 2 =0.497, Fig. 2B).…”

Section: / 16supporting

confidence: 90%

“…Forsdyke claimed that Thermophiles strictly obey Szybalski'r rule and raised a Politeness Hypothesis, assuming that mRNA with higher purine content are "polite" to avoid undesired interactions, and mRNA of thermophiles need to be even more polite, because the entropy-driven reactions are more prone to happen under high temperature (Lao and Forsdyke 2000). However, the results of further studies turned out to be paradoxical (Mahale et al 2012;Paz et al 2004). So far, the applicability of Szybalski's rule has not been proved.…”

Section: Main Textmentioning

confidence: 99%

“…The influence of genome GC content (Kagawa et al 1984;Galtier and Lobry 1997;Musto et al 2004;Wang et al 2006) or CDS purine content (Lao and Forsdyke 2000;Mahale et al 2012;Paz et al 2004) on the thermophilicity in prokaryotes has long been debated and remained unclear. Fig.…”

Section: / 16mentioning

confidence: 99%

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An overview on the DNA nucleotide compositions across kingdoms

Guo

2016

Preprint

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Abstract:The DNA nucleotide compositions vary among species. This fascinating phenomenon has been studied for decades with some interesting questions remaining unclear.Recent years, thousands of genomes have been sequenced, but general evaluations on the nucleotide compositions across different phylogenetic groups are still absent. In this letter, I analyzed 371 genomes from different kingdoms and provided an overview on DNA nucleotide compositions. A number of important topics were discussed, including GC content, DNA strand symmetricity, CDS purine content, codon usage, thermophilicity in prokaryotes and non-coding RNA genes. I also gave explanations to two long debated questions: 1) both genome GC content and CDS purine content are correlated with the thermophilicity in archaea, but not in bacteria; 2) the purine rich pattern of CDS in most species is mainly a consequence of coding requirement, but not mRNA interaction dynamics. This study provides valuable information and ideas for future investigations.

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confidence: 90%

Section: Main Textmentioning

confidence: 99%

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An overview on the DNA nucleotide compositions across kingdoms

Guo

2016

Preprint

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“…the broad range of temperatures where the bacterium has optimal growth. From the literature, unfortunately there appear to be no single standard for how the optimal temperature (OT) range of any thermophilicity class is defined [2-5], and many authors do not explicitly define which definitions they use [6-8]. For this study, the four included classes were defined as: hyperthermophiles (OT > 80°C), thermophiles (OT 50°C-80°C), mesophiles (OT 15-50°C) and psychrophiles (OT < 15°C).…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, many have looked for such a correlation, with varying conclusions; some have reported an increase in genomic GC-content correlated with an increase in optimal growth temperature, and others have shown the same for higher GC content of coding, as well as non-coding regions of specific prokaryotic genes [3,5]. Different groups, however, have reported that no such correlation could be seen for prokaryotic DNA [2,7], and a plot of GC content vs. OT shows no clear correlation. Perhaps most interestingly, in a study where a mesophile Escherichia coli strain was experimentally evolved into a facultative thermophile, Blaby et al reported 31 point mutations to have taken place [11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian prediction of bacterial growth temperature range based on genome sequences

et al. 2012

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BackgroundThe preferred habitat of a given bacterium can provide a hint of which types of enzymes of potential industrial interest it might produce. These might include enzymes that are stable and active at very high or very low temperatures. Being able to accurately predict this based on a genomic sequence, would thus allow for an efficient and targeted search for production organisms, reducing the need for culturing experiments.ResultsThis study found a total of 40 protein families useful for distinction between three thermophilicity classes (thermophiles, mesophiles and psychrophiles). The predictive performance of these protein families were compared to those of 87 basic sequence features (relative use of amino acids and codons, genomic and 16S rDNA AT content and genome size). When using naïve Bayesian inference, it was possible to correctly predict the optimal temperature range with a Matthews correlation coefficient of up to 0.68. The best predictive performance was always achieved by including protein families as well as structural features, compared to either of these alone. A dedicated computer program was created to perform these predictions.ConclusionsThis study shows that protein families associated with specific thermophilicity classes can provide effective input data for thermophilicity prediction, and that the naïve Bayesian approach is effective for such a task. The program created for this study is able to efficiently distinguish between thermophilic, mesophilic and psychrophilic adapted bacterial genomes.

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An overview on the DNA nucleotide compositions across kingdoms

Guo

2017

Gene Reports

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Does the growth temperature of a prokaryote influence the purine content of its mRNAs?

Cited by 5 publications

References 45 publications

An overview on the DNA nucleotide compositions across kingdoms

An overview on the DNA nucleotide compositions across kingdoms

Bayesian prediction of bacterial growth temperature range based on genome sequences

An overview on the DNA nucleotide compositions across kingdoms

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