Comparative Methods for Reconstructing Ancient Genome Organization

Genomes of Meniocus linifolius and Tetracme quadricornis reveal the ancestral karyotype and genomic features of core Brassicaceae

Brassicaceae represents an important plant family from both a scientific and economic perspective. However, genomic features related to the early diversification of this family have not been fully characterized, especially upon the uplift of the Tibetan Plateau, which was followed by increasing aridity in the Asian interior, intensifying monsoons in Eastern Asia, and significantly fluctuating daily temperatures. Here, we reveal the genomic architecture that accompanied early Brassicaceae diversification by analyzing two high-quality chromosome-level genomes for Meniocus linifolius (Arabodae; clade D) and Tetracme quadricornis (Hesperodae; clade E), together with genomes representing all major Brassicaceae clades and the basal Aethionemeae. We reconstructed an ancestral core Brassicaceae karyotype (CBK) containing 9 pseudochromosomes with 65 conserved syntenic genomic blocks and identified 9702 conserved genes in Brassicaceae. We detected pervasive conflicting phylogenomic signals accompanied by widespread ancient hybridization events, which correlate well with the early divergence of core Brassicaceae. We identified a successive Brassicaceae-specific expansion of the class I TREHALOSE-6-PHOSPHATE SYNTHASE 1 (TPS1) gene family, which encodes enzymes with essential regulatory roles in flowering time and embryo development. The TPS1s were mainly randomly amplified, followed by expression divergence. Our results provide fresh insights into historical genomic features coupled with Brassicaceae evolution and offer a potential model for broad-scale studies of adaptive radiation under an ever-changing environment.

AGO, a Framework for the Reconstruction of Ancestral Syntenies and Gene Orders

Reconstructing ancestral gene orders from the genome data of extant species is an important problem in comparative and evolutionary genomics. In a phylogenomics setting that accounts for gene family evolution through gene duplication and gene loss, the reconstruction of ancestral gene orders involves several steps, including multiple sequence alignment, the inference of reconciled gene trees, and the inference of ancestral syntenies and gene adjacencies. For each of the steps of such a process, several methods can be used and implemented using a growing corpus of, often parameterized, tools; in practice, interfacing such tools into an ancestral gene order reconstruction pipeline is far from trivial. This chapter introduces AGO, a Python-based framework aimed at creating ancestral gene order reconstruction pipelines allowing to interface and parameterize different bioinformatics tools. The authors illustrate the features of AGO by reconstructing ancestral gene orders for the X chromosome of three ancestral Anopheles species using three different pipelines. AGO is freely available at https://github.com/cchauve/AGO-pipeline .

From comparative gene content and gene order to ancestral contigs, chromosomes and karyotypes

To reconstruct the ancestral genome of a set of phylogenetically related descendant species, we use the RACCROCHE pipeline for organizing a large number of generalized gene adjacencies into contigs and then into chromosomes. Separate reconstructions are carried out for each ancestral node of the phylogenetic tree for focal taxa. The ancestral reconstructions are monoploids; they each contain at most one member of each gene family constructed from descendants, ordered along the chromosomes. We design and implement a new computational technique for solving the problem of estimating the ancestral monoploid number of chromosomes x. This involves a "g-mer" analysis to resolve a bias due long contigs, and gap statistics to estimate x. We find that the monoploid number of all the rosid and asterid orders is [Formula: see text]. We show that this is not an artifact of our method by deriving [Formula: see text] for the metazoan ancestor.

IAGS: Inferring Ancestor Genome Structure under a wide range of evolutionary scenarios

Significant improvements in genome sequencing and assembly technology have led to increasing numbers of high-quality genomes, revealing complex evolutionary scenarios such as multiple whole-genome duplication events, which hinders ancestral genome reconstruction via the currently available computational frameworks. Here, we present the Inferring Ancestor Genome Structure (IAGS) framework, a novel block/endpoint matching optimization strategy with single-cut-or-join distance, to allow ancestral genome reconstruction under both simple (single-copy ancestor) and complex (multicopy ancestor) scenarios. We evaluated IAGS with two simulated data sets and applied it to four different real evolutionary scenarios to demonstrate its performance and general applicability. IAGS is available at https://github.com/xjtu-omics/IAGS.

Ancestral Flowering Plant Chromosomes and Gene Orders Based on Generalized Adjacencies and Chromosomal Gene Co-Occurrences

Recurrent whole genome duplication and the ensuing loss of redundant genes-fractionation-complicate efforts to reconstruct the gene orders and chromosomes of the ancestors associated with the nodes of a phylogeny. Loss of genes disrupts the gene adjacencies key to current techniques. With our RACCROCHE pipeline, instead of starting with the inference of short ancestral segments, we suggest delaying the choice of gene adjacencies while we accumulate many more syntenically validated generalized (gapped) adjacencies. We obtain longer ancestral contigs using maximum weight matching (MWM). Similarly, we do not construct chromosomes by successively piecing together contigs into larger segments, but rather compile counts of pairwise contig co-occurrences on the set of extant genomes and use these to cluster the contigs. Chromosome-level contig assemblies for a monoploid genome emerge naturally at each node of the phylogeny and the contigs then can be ordered along the chromosome. Sampling alternative MWM solutions, visualizing heat maps, and applying gap statistics allow us to estimate the number of chromosomes in the reconstruction. We introduce several measures of quality: length of contigs, continuity of contig structure on successive ancestors, coverage of the extant genome by the reconstruction, and rearrangement relations among the inferred chromosomes. The reconstructed ancestors are visualized by painting the ancestral projections on the descendant genomes. We submit genomes drawn from a broad range of monocot orders to our pipeline, confirming the tetraploidization event "tau" in the stem lineage between the alismatids and the lilioids. We show additional applications to the Solanaceae and to four Brassica genomes, producing evidence about the monoploid ancestor in each case.

Three chromosome-scale Papaver genomes reveal punctuated patchwork evolution of the morphinan and noscapine biosynthesis pathway

For millions of years, plants evolve plenty of structurally diverse secondary metabolites (SM) to support their sessile lifestyles through continuous biochemical pathway innovation. While new genes commonly drive the evolution of plant SM pathway, how a full biosynthetic pathway evolves remains poorly understood. The evolution of pathway involves recruiting new genes along the reaction cascade forwardly, backwardly, or in a patchwork manner. With three chromosome-scale Papaver genome assemblies, we here reveal whole-genome duplications (WGDs) apparently accelerate chromosomal rearrangements with a nonrandom distribution towards SM optimization. A burst of structural variants involving fusions, translocations and duplications within 7.7 million years have assembled nine genes into the benzylisoquinoline alkaloids gene cluster, following a punctuated patchwork model. Biosynthetic gene copies and their total expression matter to morphinan production. Our results demonstrate how new genes have been recruited from a WGD-induced repertoire of unregulated enzymes with promiscuous reactivities to innovate efficient metabolic pathways with spatiotemporal constraint.