The allometry of cellular DNA and ribosomal gene content among microbes and its use for the assessment of microbiome community structure

Abstract

Background The determination of taxon-specific composition of microbiomes by combining high-throughput sequencing of ribosomal genes with phyloinformatic analyses has become routine in microbiology and allied sciences. Systematic biases to this approach based on the demonstrable variability of ribosomal operon copy number per genome were recognized early. The more recent realization that polyploidy is probably the norm, rather than the exception, among microbes from all domains of life, points to an even larger source bias. Results We found that the number of 16S or 18S RNA genes per cell, a combined result of the number of RNA gene loci per genome and ploidy level, follows an allometric power law of cell volume with an exponent of 2/3 across 6 orders of magnitude in small subunit copy number per cell and 9 orders of magnitude in cell size. This stands in contrast to cell DNA content, which follows a power law with an exponent of ¾. Conclusion In practical terms, that relationship allows for a single, simple correction for variations in both copy number per genome and ploidy level in ribosomal gene analyses of taxa-specific abundance. In biological terms, it points to the uniqueness of ribosomal gene content among microbial properties that scale with size.

Rainfall pulse regime drives biomass and community composition in biological soil crusts

“…This approach has been shown to be appropriate whenever total community size is affected (Fernandes et al, 2018). A recent study that analyzed cell size, cell volume and number of 16S rRNA copies (Gonzalez‐de‐Salceda & Garcia‐Pichel, 2021) also points to the advantage of combining these two approaches instead of presenting each separately.…”

Towards quantitative metabarcoding of eukaryotic plankton: an approach to improve 18S rRNA gene copy number bias

“…For instance, GCN per cell will change according to the growth phase and physiological status of the cell. Indeed, Gonzalez-de-Salceda and Garcia-Pichel (2021) found that the number of 18S RNA genes per cell follows an allometric power law of cell volume with an exponent of 2/3. In addition, information regarding ribosome number variation would help to anticipate potential biases that can occur in environmental 18S rRNA metabarcoding dataset from marine environments.…”

Solving the grand challenge of phenotypic integration: allometry across scales

“…Indeed, individuals with large genomes are expected to produce more RNA (Kozłowski et al 2003b ), which is phosphorus consuming and could explain the positive relationship between growth rate and RNA/protein as well as phosphorus/protein ratios observed empirically (Elser et al 2003 ). Consistently, a strong relationship has been observed between the number of ribosomal genes per cell and cell size across a large diversity of prokaryote and eukaryote organisms (Gonzalez-de-Salceda and Garcia-Pichel 2021 ). Collectively, these findings suggest that the number of expressed genes in the genome could be the key piece to link allometric relationships at different levels of phenotypic integration, as well as to reconcile allometry with other disciplines such as biological stoichiometry, quantitative genetics and physiology.…”

“…They typically take the form of power-law functions Y = α X β , where X is a size-related trait and Y a morphological or physiological trait (Niklas 1994 ). Strikingly, the same equations are used to scale up from genome size to cell size (Kozłowski et al 2003b ; Beaulieu et al 2008 ; Šímová and Herben 2011 ; Gonzalez-de-Salceda and Garcia-Pichel 2021 ), cell size to organ size (Gregory et al 2000 ; Tisné et al 2008 ; John et al 2013 ), organ size to organism size (Stahl 1965 ; Gould 1971 ; Stevenson et al 1995 ; Lindstedt and Schaeffer 2002 ; Shingleton et al 2007 ; Poorter et al 2015 ), body size to energy consumption (Huxley 1932 ; Kleiber 1932 , 1947 ; DeLong et al 2010 ; Capellini et al 2010 ), as well as from organism size to population density (Enquist et al 1998 ; Malerba and Marshall 2019 ) (see Fig. 2 for examples of allometric relationships at different organizational levels).…”

“…D Relationship between organism’s basal metabolic rate ( B ) and body mass ( M ) (West et al 2002 ). E Relationship between the number of 16S/18S ribosomal genes per cell ( R C ) and cell volume ( V C ) (Gonzalez-de-Salceda and Garcia-Pichel 2021 ). F Relationship between xylem flux ( F ) and plant mass ( M ) (Enquist and Niklas 2001 ).…”