A separation-of-function ZIP4 wheat mutant allows crossover between related chromosomes and is meiotically stable

Many species, including most flowering plants, are polyploid, possessing multiple genomes. During polyploidisation, fertility is preserved via the evolution of mechanisms to control the behaviour of these multiple genomes during meiosis. On the polyploidisation of wheat, the major meiotic gene ZIP4 duplicated and diverged, with the resulting new gene TaZIP4-B2 being inserted into chromosome 5B. Previous studies showed that this TaZIP4-B2 promotes pairing and synapsis between wheat homologous chromosomes, whilst suppressing crossover between related (homoeologous) chromosomes. Moreover, in wheat, the presence of TaZIP4-B2 preserves up to 50% of grain number. The present study exploits a ‘separation-of-function’ wheat Tazip4-B2 mutant named zip4-ph1d, in which the Tazip4-B2 copy still promotes correct pairing and synapsis between homologues (resulting in the same pollen profile and fertility normally found in wild type wheat), but which also allows crossover between the related chromosomes in wheat haploids of this mutant. This suggests an improved utility for the new zip4-ph1d mutant line during wheat breeding, compared to the previously described CRISPR Tazip4-B2 and ph1 mutant lines. The results also reveal that loss of suppression of homoeologous crossover between wheat chromosomes does not in itself reduce wheat fertility when promotion of homologous pairing and synapsis by TaZIP4-B2 is preserved.

A separation-of-function ZIP4 wheat mutant allows crossover between related chromosomes and is meiotically stable

ABSTRACTMany species, including most flowering plants, are polyploid, possessing multiple genomes. During polyploidisation, fertility is preserved via the evolution of mechanisms to control the behaviour of these multiple genomes during meiosis. On the polyploidisation of wheat, the major meiotic gene ZIP4 duplicated and diverged, with the resulting new gene TaZIP4-B2 being inserted into chromosome 5B. Previous studies showed that this TaZIP4-B2 promotes pairing and synapsis between wheat homologous chromosomes, whilst suppressing crossover between related (homoeologous) chromosomes. Moreover, in wheat, the presence of TaZIP4-B2 preserves up to 50% of grain number. The present study exploits a ‘separation-of-function’ wheat Tazip4-B2 mutant named zip4-ph1d, in which the Tazip4-B2 copy still promotes correct pairing and synapsis between homologues (resulting in the same pollen profile and fertility normally found in wild type wheat), but which also allows crossover between the related chromosomes in wheat haploids of this mutant. This suggests an improved utility for the new zip4-ph1d mutant line during wheat breeding exploitation, compared to the previously described CRISPR Tazip4-B2 and ph1 mutant lines. The results also reveal that loss of suppression of homoeologous crossover between wheat chromosomes does not in itself reduce wheat fertility when promotion of homologous pairing and synapsis by TaZIP4-B2 is preserved.

StrainFLAIR: strain-level profiling of metagenomic samples using variation graphs

Current studies are shifting from the use of single linear references to representation of multiple genomes organised in pangenome graphs or variation graphs. Meanwhile, in metagenomic samples, resolving strain-level abundances is a major step in microbiome studies, as associations between strain variants and phenotype are of great interest for diagnostic and therapeutic purposes. We developed StrainFLAIR with the aim of showing the feasibility of using variation graphs for indexing highly similar genomic sequences up to the strain level, and for characterizing a set of unknown sequenced genomes by querying this graph. On simulated data composed of mixtures of strains from the same bacterial species Escherichia coli, results show that StrainFLAIR was able to distinguish and estimate the abundances of close strains, as well as to highlight the presence of a new strain close to a referenced one and to estimate its abundance. On a real dataset composed of a mix of several bacterial species and several strains for the same species, results show that in a more complex configuration StrainFLAIR correctly estimates the abundance of each strain. Hence, results demonstrated how graph representation of multiple close genomes can be used as a reference to characterize a sample at the strain level.

Whose trait is it anyways? Coevolution of joint phenotypes and genetic architecture in mutualisms

Evolutionary biologists typically envision a trait’s genetic basis and fitness effects occurring within a single species. However, traits can be determined by and have fitness consequences for interacting species, thus evolving in multiple genomes. This is especially likely in mutualisms, where species exchange fitness benefits and can associate over long periods of time. Partners may experience evolutionary conflict over the value of a multi-genomic trait, but such conflicts may be ameliorated by mutualism’s positive fitness feedbacks. Here, we develop a simulation model of a host–microbe mutualism to explore the evolution of a multi-genomic trait. Coevolutionary outcomes depend on whether hosts and microbes have similar or different optimal trait values, strengths of selection and fitness feedbacks. We show that genome-wide association studies can map joint traits to loci in multiple genomes and describe how fitness conflict and fitness feedback generate different multi-genomic architectures with distinct signals around segregating loci. Partner fitnesses can be positively correlated even when partners are in conflict over the value of a multi-genomic trait, and conflict can generate strong mutualistic dependency. While fitness alignment facilitates rapid adaptation to a new optimum, conflict maintains genetic variation and evolvability, with implications for applied microbiome science.

Correction to: Processing and Analyzing Multiple Genomes Alignments with MafFilter

Chapter 2, “Processing and Analyzing Multiple Genomes Alignments with MafFilter,” was previously published without including the Electronic Supplementary Material. This has now been included in the revised version of this book.

The Bacteria Genome Pipeline (BAGEP): an automated, scalable workflow for bacteria genomes with Snakemake

Next generation sequencing technologies are becoming more accessible and affordable over the years, with entire genome sequences of several pathogens being deciphered in few hours. However, there is the need to analyze multiple genomes within a short time, in order to provide critical information about a pathogen of interest such as drug resistance, mutations and genetic relationship of isolates in an outbreak setting. Many pipelines that currently do this are stand-alone workflows and require huge computational requirements to analyze multiple genomes. We present an automated and scalable pipeline called BAGEP for monomorphic bacteria that performs quality control on FASTQ paired end files, scan reads for contaminants using a taxonomic classifier, maps reads to a reference genome of choice for variant detection, detects antimicrobial resistant (AMR) genes, constructs a phylogenetic tree from core genome alignments and provide interactive short nucleotide polymorphism (SNP) visualization across core genomes in the data set. The objective of our research was to create an easy-to-use pipeline from existing bioinformatics tools that can be deployed on a personal computer. The pipeline was built on the Snakemake framework and utilizes existing tools for each processing step: fastp for quality trimming, snippy for variant calling, Centrifuge for taxonomic classification, Abricate for AMR gene detection, snippy-core for generating whole and core genome alignments, IQ-TREE for phylogenetic tree construction and vcfR for an interactive heatmap visualization which shows SNPs at specific locations across the genomes. BAGEP was successfully tested and validated with Mycobacterium tuberculosis (n = 20) and Salmonella enterica serovar Typhi (n = 20) genomes which are about 4.4 million and 4.8 million base pairs, respectively. Running these test data on a 8 GB RAM, 2.5 GHz quad core laptop took 122 and 61 minutes on respective data sets to complete the analysis. BAGEP is a fast, calls accurate SNPs and an easy to run pipeline that can be executed on a mid-range laptop; it is freely available on: https://github.com/idolawoye/BAGEP.

The design and construction of reference pangenome graphs with minigraph

The recent advances in sequencing technologies enable the assembly of individual genomes to the quality of the reference genome. How to integrate multiple genomes from the same species and make the integrated representation accessible to biologists remains an open challenge. Here, we propose a graph-based data model and associated formats to represent multiple genomes while preserving the coordinate of the linear reference genome. We implement our ideas in the minigraph toolkit and demonstrate that we can efficiently construct a pangenome graph and compactly encode tens of thousands of structural variants missing from the current reference genome.