Genome-Wide Comparative Analysis of Phylogenetic Trees: The Prokaryotic Forest of Life

OrthoPhyl-streamlining large-scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales

There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.

A spectrum of verticality across genes

Lateral gene transfer (LGT) has impacted prokaryotic genome evolution, yet the extent to which LGT compromises vertical evolution across individual genes and individual phyla is unknown, as are the factors that govern LGT frequency across genes. Estimating LGT frequency from tree comparisons is problematic when thousands of genomes are compared, because LGT becomes difficult to distinguish from phylogenetic artefacts. Here we report quantitative estimates for verticality across all genes and genomes, leveraging a well-known property of phylogenetic inference: phylogeny works best at the tips of trees. From terminal (tip) phylum level relationships, we calculate the verticality for 19,050,992 genes from 101,422 clusters in 5,655 prokaryotic genomes and rank them by their verticality. Among functional classes, translation, followed by nucleotide and cofactor biosynthesis, and DNA replication and repair are the most vertical. The most vertically evolving lineages are those rich in ecological specialists such as Acidithiobacilli, Chlamydiae, Chlorobi and Methanococcales. Lineages most affected by LGT are the α-, β-, γ-, and δ- classes of Proteobacteria and the Firmicutes. The 2,587 eukaryotic clusters in our sample having prokaryotic homologues fail to reject eukaryotic monophyly using the likelihood ratio test. The low verticality of α-proteobacterial and cyanobacterial genomes requires only three partners-an archaeal host, a mitochondrial symbiont, and a plastid ancestor-each with mosaic chromosomes, to directly account for the prokaryotic origin of eukaryotic genes. In terms of phylogeny, the 100 most vertically evolving prokaryotic genes are neither representative nor predictive for the remaining 97% of an average genome. In search of factors that govern LGT frequency, we find a simple but natural principle: Verticality correlates strongly with gene distribution density, LGT being least likely for intruding genes that must replace a preexisting homologue in recipient chromosomes. LGT is most likely for novel genetic material, intruding genes that encounter no competing copy.

Taxonomic classification method for metagenomics based on core protein families with Core-Kaiju

Characterizing species diversity and composition of bacteria hosted by biota is revolutionizing our understanding of the role of symbiotic interactions in ecosystems. Determining microbiomes diversity implies the assignment of individual reads to taxa by comparison to reference databases. Although computational methods aimed at identifying the microbe(s) taxa are available, it is well known that inferences using different methods can vary widely depending on various biases. In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and shotgun sequencing to three mock communities of bacteria, of which the compositions are known. We show that none of these methods can infer both the true number of taxa and their abundances. We thus propose a novel approach, named Core-Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method similar to 16S, but based on emergent statistics of core protein domain families. We thus test the proposed method on various mock communities and we show that Core-Kaiju reliably predicts both number of taxa and abundances. Finally, we apply our method on human gut samples, showing how Core-Kaiju may give more accurate ecological characterization and a fresh view on real microbiomes.

A New Phylogenomic Approach For Quantifying Horizontal Gene Transfer Trends in Prokaryotes

It is well established nowadays that among prokaryotes, various families of orthologous genes exhibit conflicting evolutionary history. A prime factor for this conflict is horizontal gene transfer (HGT) - the transfer of genetic material not via vertical descent. Thus, the prevalence of HGT is challenging the meaningfulness of the classical Tree of Life concept. Here we present a comprehensive study of HGT representing the entire prokaryotic world. We mainly rely on a novel analytic approach for analyzing an aggregate of gene histories, by means of the quartet plurality distribution (QPD) that we develop. Through the analysis of real and simulated data, QPD is used to reveal evidence of a barrier against HGT, separating the archaea from the bacteria and making HGT between the two domains, in general, quite rare. In contrast, bacteria's confined HGT is substantially more frequent than archaea's. Our approach also reveals that despite intensive HGT, a strong tree-like signal can be extracted, corroborating several previous works. Thus, QPD, which enables one to analytically combine information from an aggregate of gene trees, can be used for understanding patterns and rates of HGT in prokaryotes, as well as for validating or refuting models of horizontal genetic transfers and evolution in general.