Plant-Microbe Interactions: Finding phenazine

Analysis of genetic information from soil samples provides insights into bacteria that help to protect crops from fungal diseases by producing chemicals called phenazines.
  1. Sarah J Wolfson
  2. Libusha Kelly  Is a corresponding author
  1. Department of Systems and Computational Biology, Albert Einstein College of Medicine, United States
  2. Department of Microbiology and Immunology, Albert Einstein College of Medicine, United States

“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.

The rhizomes of different food crops host distinct communities of phenazine-producing bacteria.

Root microbiomes are enriched in bacteria that produce phenazines, a group of compounds that protects plants from fungal diseases. Dar et al. have developed a method that allows them to compare which phenazine-producing bacteria are present in different soil and crop samples. This allows them to identify which phenazine-producing bacteria may be important for different food crops. Some of the phenazine-producing microbes are associated with specific plants: on the left, tomatoes are shown with specific microbes in blue, while on the right wheat is shown with specific microbes in green. Other microbes are common across different ecosystems: yellow and peach-colored coccoid microbes are shown with both crops. Under each crop, the chemical structures of different types of phenazines show the diversity of these compounds, which depends on the bacteria producing them.

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., 2019Hooper et al., 2002Reese et al., 2018) that is likely relevant across many microbiomes, from the soil to the human body.


Article and author information

Author details

  1. Sarah J Wolfson

    Sarah J Wolfson is in the Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, United States

    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9774-0977
  2. Libusha Kelly

    Libusha Kelly is in the Department of Systems and Computational Biology, and Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, United States

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7303-1022

Publication history

  1. Version of Record published: October 27, 2020 (version 1)


© 2020, Wolfson and Kelly

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


  • 880
    Page views
  • 114
  • 0

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Sarah J Wolfson
  2. Libusha Kelly
Plant-Microbe Interactions: Finding phenazine
eLife 9:e62983.

Further reading

    1. Ecology
    2. Evolutionary Biology
    Julian Melgar, Mads F Schou ... Charlie K Cornwallis
    Research Article

    Cooperative breeding allows the costs of parental care to be shared, but as groups become larger, such benefits often decline as competition increases and group cohesion breaks down. The counteracting forces of cooperation and competition are predicted to select for an optimal group size, but variation in groups is ubiquitous across cooperative breeding animals. Here, we experimentally test if group sizes vary because of sex differences in the costs and benefits of cooperative breeding in captive ostriches, Struthio camelus, and compare this to the distribution of group sizes in the wild. We established 96 groups with different numbers of males (1 or 3) and females (1, 3, 4, or 6) and manipulated opportunities for cooperation over incubation. There was a clear optimal group size for males (one male with four or more females) that was explained by high costs of competition and negligible benefits of cooperation. Conversely, female reproductive success was maximised across a range of group sizes due to the benefits of cooperation with male and female group members. Reproductive success in intermediate sized groups was low for both males and females due to sexual conflict over the timing of mating and incubation. Our experiments show that sex differences in cooperation and competition can explain group size variation in cooperative breeders.

    1. Ecology
    2. Evolutionary Biology
    Nicholas M Grebe, Jean Paul Hirwa ... Stacy Rosenbaum
    Research Article Updated

    Evolutionary theories predict that sibling relationships will reflect a complex balance of cooperative and competitive dynamics. In most mammals, dispersal and death patterns mean that sibling relationships occur in a relatively narrow window during development and/or only with same-sex individuals. Besides humans, one notable exception is mountain gorillas, in which non-sex-biased dispersal, relatively stable group composition, and the long reproductive tenures of alpha males mean that animals routinely reside with both maternally and paternally related siblings, of the same and opposite sex, throughout their lives. Using nearly 40,000 hr of behavioral data collected over 14 years on 699 sibling and 1235 non-sibling pairs of wild mountain gorillas, we demonstrate that individuals have strong affiliative preferences for full and maternal siblings over paternal siblings or unrelated animals, consistent with an inability to discriminate paternal kin. Intriguingly, however, aggression data imply the opposite. Aggression rates were statistically indistinguishable among all types of dyads except one: in mixed-sex dyads, non-siblings engaged in substantially more aggression than siblings of any type. This pattern suggests mountain gorillas may be capable of distinguishing paternal kin but nonetheless choose not to affiliate with them over non-kin. We observe a preference for maternal kin in a species with a high reproductive skew (i.e. high relatedness certainty), even though low reproductive skew (i.e. low relatedness certainty) is believed to underlie such biases in other non-human primates. Our results call into question reasons for strong maternal kin biases when paternal kin are identifiable, familiar, and similarly likely to be long-term groupmates, and they may also suggest behavioral mismatches at play during a transitional period in mountain gorilla society.