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Inter-cluster competition and resource partitioning may govern the ecology of Frankia

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Abstract

Microbes live in a complex communal ecosystem. The structural complexity of microbial community reflects diversity, functionality, as well as habitat type. Delineation of ecologically important microbial populations along with exploration of their roles in environmental adaptation or host–microbe interaction has a crucial role in modern microbiology. In this scenario, reverse ecology (the use of genomics to study ecology) plays a pivotal role. Since the co-existence of two different genera in one small niche should maintain a strict direct interaction, it will be interesting to utilize the concept of reverse ecology in this scenario. Here, we exploited an ‘R’ package, the RevEcoR, to resolve the issue of co-existing microbes which are proven to be a crucial tool for identifying the nature of their relationship (competition or complementation) persisting among them. Our target organism here is Frankia, a nitrogen-fixing actinobacterium popular for its genetic and host-specific nature. According to their plant host, Frankia has already been sub-divided into four clusters C-I, C-II, C-III, and C-IV. Our results revealed a strong competing nature of CI Frankia. Among the clusters of Frankia studied, the competition index between C-I and C-III was the largest. The other interesting result was the co-occurrence of C-II and C-IV groups. It was revealed that these two groups follow the theory of resource partitioning in their lifestyle. Metabolic analysis along with their differential transporter machinery validated our hypothesis of resource partitioning among C-II and C-IV groups.

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Acknowledgements

We acknowledge S Das and C Sarkar for initially standardizing the software. We also acknowledge the Biswa Bangla Genome Center and the University of North Bengal for supporting this work.

Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Bioinformatics Facility, University of North Bengal, Siliguri, West Bengal, India

    I. Sarkar, G. Sen & A. Sen

  2. Biswa Bangla Genome Centre, Univ. of North Bengal, Siliguri, West Bengal, India

    S. Bhattacharyya & A. Sen

  3. Department of Botany, University of North Bengal, Siliguri, West Bengal, India

    I. Sarkar & A. Sen

  4. Unité de Bactériologie Moléculaire and Génomique, Département de Génie Biologique and Chimique, Institut National Des Sciences Appliquéeset de Technologie, Université de Carthage, Carthage, Tunisia

    M. Gtari

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  1. I. Sarkar
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Contributions

AS and MG: conceived and developed the idea. IS, GS, and AS: designed the experiment and did the bioinformatics work. SB, IS, and AS: contributed to analyzing the data and wrote the final draft of the manuscript. All authors have read and approved the manuscript.

Corresponding author

Correspondence to A. Sen.

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The authors declare that the research paper was written in the absence of any commercial or financial relationships that could be construed as a real or potential conflict of interest. Thus authors declare no conflict of interest.

Animal issue

No animal or human was treated as the sample in this work.

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Communicated by Erko Stackebrandt.

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Supplementary Information

Below is the link to the electronic supplementary material.

203_2022_2910_MOESM1_ESM.tif

Supplementary file1 Supplementary Figure 1: Taxonomic position of Actinorhizal genera in the Magnoliopsida according to Cronquist (1988) (ref 47). Subclasses are shown in bold uppercase and orders in bold lowercase, families in lowercase and genera in italics. (TIF 691 KB)

203_2022_2910_MOESM2_ESM.pdf

Supplementary file2 Supplementary Figure 2: A high resolution figure on the whole genome based Frankia phylogeny. Whole genome based phylogeny of Frankia. Type (Strain) Genome Server was used for generating the tree. TYGS sub-divided the whole genome based phylogeny generation into pair-wise comparison of genome sequences using Genome Blast Distance Phylogeny (GBDP) and accurate intergenomic distances calculation by 'trimming' and distance formula. Hundred distance replicates were used for that analysis. Digital DNA-DNA Hybridization (DDH) scores and confidence intervals were calculated through GGDC2.1 server. Intergenomic distance based phylogeny was generated through a balanced minimum evolution tree with branch support via FASTME 2.1.6.1. Branch point support was inferred from 100 pseudo-bootstrap replicates per branch. The phylogeny was generated using the default parameters. Heatmaps with species cluster, sub-species cluster, G+C amount, delta statistics, the genome size (bp) and protein content were generated through the TYGS server using default parameters. (PDF 626 KB)

203_2022_2910_MOESM3_ESM.pdf

Supplementary file3 Supplementary Figure 3 (a): Competition among C-II and C-IV Frankia strains. Values were calculated through RevEcoR which is based on R software. C-II members are written in blue color and C-IV members are in red color. The value ranges from 0-1. The intra-cluster competition seemed to be lesser than inter-cluster competition. (b): Complementation among C-II and C-IV Frankia strains. Values were calculated through RevEcoR which is based on R software. C-II members are written in blue color and C-IV members are in red color. The value ranges from 0-1. Inter-cluster complementation was lesser than intra cluster complementation. (PDF 105 KB)

203_2022_2910_MOESM4_ESM.xlsx

Supplementary file4 Supplementary File 1: Overall competition (sheet 1) and complementation (sheet 2) among selected Frankia strains. (XLSX 70 KB)

Supplementary file5 Supplementary File 2: Metabolic pathway analysis among four different Frankia cluster (XLSX 21 KB)

203_2022_2910_MOESM6_ESM.docx

Supplementary file6 Supplementary Table 1: Genomic details of Frankia strains considered for reverse ecology analysis (DOCX 21 KB)

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Sarkar, I., Sen, G., Bhattacharyya, S. et al. Inter-cluster competition and resource partitioning may govern the ecology of Frankia. Arch Microbiol 204, 326 (2022). https://doi.org/10.1007/s00203-022-02910-0

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