Novel model‐based clustering reveals ecologically differentiated bacterial genomes across a large climate gradient

Abstract

A pervasive challenge in microbial ecology is understanding the genetic level where ecological units can be differentiated. Ecological differentiation often occurs at fine genomic levels, yet it is unclear how to utilise ecological information to define ecotypes given the breadth of environmental variation among microbial taxa. Here, we present an analytical framework that infers clusters along genome‐based microbial phylogenies according to shared environmental responses. The advantage of our approach is the ability to identify genomic clusters that best fit complex environmental information whilst characterising cluster niches through model predictions. We apply our method to determine climate‐associated ecotypes in populations of nitrogen‐fixing symbionts using whole genomes, explicitly sampled to detect climate differentiation across a heterogeneous landscape. Although soil and plant host characteristics strongly influence distribution patterns of inferred ecotypes, our flexible statistical method enabled us to identify climate‐associated genomic clusters using environmental data, providing solid support for ecological specialisation in soil symbionts.

Groundwater Microbial Communities in Times of Climate Change

“…In the future, new bioinformatics approaches will help to refine ecological traits of microorganism and their interplay with climate change (Simonsen et al, 2019). A comprehensive and standardized study and sampling design followed by the evaluation of genomic and metadata is asked for to allow meta-analysis (Field et al, 2008).…”

“…A comprehensive and standardized study and sampling design followed by the evaluation of genomic and metadata is asked for to allow meta-analysis (Field et al, 2008). These methods need to deal with the environmental complexity and interspecies interactions that often interfere with climate-associated patterns which become apparent on the large scale (Simonsen et al, 2019)…”

Four Complete Genome Sequences for Bradyrhizobium sp. Strains Isolated from an Endemic Australian Acacia Legume Reveal Structural Variation

“…genome sequences originally isolated from Acacia acuminata (a host endemic to southwest Australia, a global biodiversity hot spot). These genome sequences were completed to provide preliminary insight into the chromosome structural variation; the strains sequenced were selected to maximize genetic variability from a larger population genomic study comprising 375 closely related yet genetically diverse Bradyrhizobium strains sampled along a large climate gradient in the same region ( 5 ). All 4 Bradyrhizobium strains cluster within a single species, having >99.5% 16S rRNA sequence identity, and phylogenetically cluster with Bradyrhizobium diazoefficiens ( 5 ).…”

“…All strains were grown on yeast extract mannitol plates from frozen stock cultures (70% glycerol, −80°C), previously isolated from root nodules of Acacia acuminata ( 3 , 5 ). DNA was extracted from a single colony using a modified MoBio Ultraclean microbial isolation protocol, where cells were heat treated (60°C, 5 min) in lysis buffer prior to mechanical lysing.…”

“…For genome assembly, we used a hybrid approach using short Illumina reads and long reads from either the PacBio or Nanopore sequencing platform. Short-read whole-genome paired-end 150-bp Illumina data were generated on two HiSeq 2000 lanes using Illumina Nextera XT library kits, following standard Illumina protocols ( 5 ), and trimmed using Trimmomatic v0.36 ( 6 ) (ILLUMINACLIP:adapters.fasta:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:20 MINLEN:100). In 3 of 4 strains, long-read data were generated on a PacBio RS II system at the Macrogen sequencing facilities in South Korea; SMRTbell libraries were created using the protocol “Procedure and Checklist—10 kb Template Preparation and Sequencing (with Low-Input DNA)” ( 7 ), and each strain was sequenced on two single-molecule real-time (SMRT) cells.…”