Analysis of genetic information from soil samples provides insights into bacteria that help to protect crops from fungal diseases by producing chemicals called phenazines.
“Imagine walking out in the countryside and not being able to tell a snake from a cow from a mouse from a blade of grass” (Carl Woese, in?Yardley, 2012). A similar problem confronted researchers trying to identify individual microbes within complex communities – ecosystems that can contain thousands of different microbial species – before DNA sequencing made it easier to distinguish different microorganisms (Woese and Fox, 1977). Part of the problem was that microbes are wildly diverse, spanning the three domains of life. Moreover, two microbes can be as dissimilar as mushrooms and humans, yet difficult to tell apart – even with the help of a microscope.
Complex microbial communities are particularly important in agriculture. Most of the food crops we grow have been meticulously selected and modified to increase yields, among other things, but our understanding of how these crops interact with the wild microbes that live in soil is far from complete (Graham et al., 2016; Badri et al., 2009). Now, in eLife, Dianne Newman (California Institute of Technology) and colleagues – including Daniel Dar (CalTech), Linda Thomashow and David Weller (both from the USDA Agricultural Research Service) – report how studying metagenomes can shed light on the bacteria responsible for making phenazines, a class of chemicals that protects major food crops from fungal disease (Dar et al., 2020).
Until now, identifying the bacteria that produced phenazines was a slow process that relied on analyzing individual bacteria from different plant samples independently, or on comparing samples with mixed DNA and reporting on the relative proportions of bacteria (Mavrodi et al., 2013). Moreover, it was difficult to compare different samples using these methods. The new metagenomic technique – which involves analyzing genetic material collected from soil samples – does not suffer from these shortcomings.
Dar et al. started by connecting specific bacteria found in the immediate vicinity of plant roots – a region of soil called the rhizosphere – to the production of phenazine. They searched for the genes that allow bacteria to make phenazine in agricultural soil samples, and assumed that any bacteria carrying these genes were indeed true phenazine producers. However, simply counting the number of these 'phz+' bacteria in each sample was not sufficient as no two grams of soil contain the same number of bacteria. Dar et al. allowed for this by dividing the number of phz+ bacteria by the number of individual bacteria in each sample (which can be estimated by counting certain genes that are found in all bacteria in single copy; Parks et al., 2018). The value of this ratio can be compared across multiple samples from different environments.
After confirming that their pipeline worked by testing it on computationally-generated data, Dar et al. applied their approach to a real meta- genomic dataset from the rhizosphere of wheat. This revealed that phenazines were produced by two groups of bacteria. The bacteria in one of these groups belong to the Pseudomonas genus, and were already known to produce phenazine based on traditional culture based studies. However, the discovery of a second group, Streptomyces bacteria, came as a surprise as there are no previous reports of any members of this diverse group of bacteria being phenazine producers relevant to agricultural crops. This discovery is agriculturally relevant because different bacteria can produce different phenazine compounds, which interact with roots in different ways.
Based on these results, Dar et al. expanded their search to 799 more datasets and found that phz+ bacteria comprised between 0% and 2.7% of the total bacteria in the samples. Some crops harbor more phz+ bacteria than others and, on average, the rhizosphere contained 1.9 times more phz+ bacteria than 'open' soil. Some strains of phz+ bacteria were also plant-specific, while others inhabited the rhizospheres of multiple plants as well as open soils (Figure 1). The new analysis also provided insights into what bacterial species are important phenazine producers. The phz+ Streptomyces detected initially comprised a large portion of phenazine producers. In addition, a clade of bacteria previously unknown to colonize major crops, Xanthomonadales, was often found associated with root ecosystems highly enriched in phz+ bacteria.
Finally, to confirm that the analysis could identify genes within phz+ bacteria that produce specific phenazines, Dar?et al. cultured different genetically modified versions of one Xanthomonadales species in the laboratory. When the genes predicted to be involved in phenazine production were removed from the different versions of this bacterium, the bacteria stopped producing phenazine.
Researchers currently have access to a wide range of metagenomic datasets, and the approach developed by Dar?et al. provides new ways to analyze these and find out more about the interactions between bacterial microbes and plants. Normalizing bacterial counts across samples allows scientists to uncover global microbial interactions, and potentially predict the chemical environment of an ecosystem. The ability of microbes to shape their own chemical environment is an emerging area of research?(Guthrie et al., 2019;?Hooper et al., 2002;?Reese et al., 2018) that is likely relevant across many microbiomes, from the soil to the human body.
Rhizosphere chemical dialogues: plant-microbe interactionsCurrent Opinion in Biotechnology 20:642–650.https://doi.org/10.1016/j.copbio.2009.09.014
How host-microbial interactions shape the nutrient environment of the mammalian intestineAnnual Review of Nutrition 22:283–307.https://doi.org/10.1146/annurev.nutr.22.011602.092259
Carl Woese dies at 84; Discovered life’s ‘Third Domain’The New York Times. Accessed October 23, 2020.
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Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioral traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the 'estrus-mediated variability hypothesis'); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the 'greater male variability hypothesis'. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex-bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.
Vulturine guineafowl range over larger areas, explore more new places and are more likely to reproduce when they live in groups of intermediate size.