Environmental Microbiology Reports · 2013 · 27 citations · 25 references
Life SciencesBiologyMicrobiologyEnvironmental MicrobiologyMicrobial EcologyMicrobial Genetics16S Ribosomal Ribonucleic Acid AnalysisBacterial EcologyMicrobial Growth16S Ribosomal Ribonucleic Acid (Rrna) Sequencing16S Ribosomal Ribonucleic Acid Gene Profiling16S Ribosomal Rna Gene AnalysisArchaeal EcologyLinear RegressionMeltingReal-time Polymerase Chain Reaction (Pcr)
Prokaryote growth temperatures in environmental samples are difficult to measure because it is hard to culture viable prokaryotes in natural environments. We comprehensively surveyed growth temperatures and 16S rRNA sequences of prokaryotes to estimate their growth temperatures based on guanine-plus-cytosine contents (P(GC)) of their 16S rRNA sequences. We focused on archaea because of the wide range of growth temperatures within this group. Their minimum (Tmin), optimum (Topt) and maximum (Tmax) growth temperatures correlated strongly with PGC of their 16S rRNA genes. Linear regression equations were established to approximate Tmin, Topt and Tmax from P(GC). We also established a linear regression equation for calculating P(GC) of 16S rRNA genes based on the melting temperatures (Tm) of PCR fragments, without using a clone library or sequencing. Environmental samples were obtained from a wide variety of microbial natural habitats. Tm of archaeal 16S rRNA genes amplified by real-time PCR were determined by melting curve analysis. Based on those values, P(GC) of 16S rRNA genes and mean Tmin, Topt and Tmax were calculated using the linear regression equations. These temperatures correlated strongly with the in situ temperatures. Tmax agreed particularly well with these temperatures, suggesting many archaea live at their maximum growth temperatures.
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