Phylodynamic approaches enable viral genetic variation to be tracked over time, providing insight into pathogen phylogenetic relationships and epidemiological dynamics. These are important methods for monitoring viral spread, and identifying important parameters such as transmission rate, geographic origin and duration of infection [1]. This knowledge makes it possible to adjust public health measures in real-time and was important in the case of the COVID-19 pandemic [2]. However, these approaches can be complicated to use when combining a very large number of sequences. This was particularly true during the COVID-19 pandemic, when sequencing data representing millions of entire viral genomes was generated, with associated metadata enabling their precise identification.

Danesh et al. [3] present a bioinformatics pipeline, CovFlow, for selecting relevant sequences according to user-defined criteria to produce files that can be used directly for phylodynamic analyses. The selection of sequences first involves a quality filter on the size of the sequences and the absence of unresolved bases before being able to make choices based on the associated metadata. Once the sequences are selected, they are aligned and a time-scaled phylogenetic tree is inferred. An output file in a format directly usable by BEAST 2 [4] is finally generated.

To illustrate the use of the pipeline, Danesh et al. [3] present an analysis of the Delta variant in two regions of France. They observed a delay in the start of the epidemic depending on the region. In addition, they identified genetic variation linked to the start of the school year and the extension of vaccination, as well as the arrival of a new variant. This tool will be of major interest to researchers analysing SARS-CoV-2 sequencing data, and a number of future developments are planned by the authors.

References

[1] Baele G, Dellicour S, Suchard MA, Lemey P, Vrancken B. 2018. Recent advances in computational phylodynamics. Curr Opin Virol. 31:24-32. https://doi.org/10.1016/j.coviro.2018.08.009

[2] Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. 2022. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet. 23:547-562. https://doi.org/10.1038/s41576-022-00483-8

[3] Danesh G, Boennec C, Verdurme L, Roussel M, Trombert-Paolantoni S, Visseaux B, Haim-Boukobza S, Alizon S. 2023. COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences. bioRxiv, ver. 7 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.06.17.496544

[4] Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. 2014. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 10: e1003537. https://doi.org/10.1371/journal.pcbi.1003537

Contamination, the presence of foreign DNA sequences in a sample of interest, is currently a major problem in genomics. Because contamination is often unavoidable at the experimental stage, it is increasingly recognized that the processing of high-throughput sequencing data must include a decontamination step. This is usually performed after the many sequence reads have been assembled into a relatively small number of contigs. Dubious contigs are then discarded based on their composition (e.g. GC-content) or because they are highly similar to a known piece of DNA from a foreign species.

Here [1], Mathieu Gautier explores a novel strategy consisting in decontaminating reads, not contigs. Why is this promising? Assembly programs and algorithms are complex, and it is not easy to predict, or monitor, how they handle contaminant reads. Ideally, contaminant reads will be assembled into obvious contaminant contigs. However, there might be more complex situations, such as chimeric contigs with alternating genuine and contaminant segments. Decontaminating at the read level, if possible, should eliminate such unfavorable situations where sequence information from contaminant and target samples are intimately intertwined by an assembler.

To achieve this aim, Gautier proposes to use methods initially designed for the analysis of metagenomic data. This is pertinent since the decontamination process involves considering a sample as a mixture of different sources of DNA. The programs used here, CLARK and CLARK-L, are based on so-called k-mer analysis, meaning that the similarity between a read to annotate and a reference sequence is measured by how many sub-sequences (of length 31 base pairs for CLARK and 27 base pairs for CLARK-L) they share. This is notoriously more efficient than traditional sequence alignment algorithms when it comes to comparing a very large number of (most often unrelated) sequences. This is, therefore, a reference-based approach, in which the reads from a sample are assigned to previously sequenced genomes based on k-mer content.

This original approach is here specifically applied to the case of Drosophila suzukii, an invasive pest damaging fruit production in Europe and America. Fortunately, Drosophila is a genus of insects with abundant genomic resources, including high-quality reference genomes in dozens of species. Having calibrated and validated his pipeline using data sets of known origins, Gautier quantifies in each of 258 presumed D. suzukii samples the proportion of reads that likely belong to other species of fruit flies, or to fruit fly-associated microbes. This proportion is close to one in 16 samples, which clearly correspond to mis-labelled individuals. It is non-negligible in another ~10 samples, which really correspond to D. suzukii individuals. Most of these reads of unexpected origin are contaminants and should be filtered out. Interestingly, one D. suzukii sample contains a substantial proportion of reads from the closely related D. subpulchera, which might instead reflect a recent episode of gene flow between these two species. The approach, therefore, not only serves as a crucial technical step, but also has the potential to reveal biological processes.

Gautier's thorough, well-documented work will clearly benefit the ongoing and future research on D. suzuki, and Drosophila genomics in general. The author and reviewers rightfully note that, like any reference-based approach, this method is heavily dependent on the availability and quality of reference genomes - Drosophila being a favorable case. Building the reference database is a key step, and the interpretation of the output can only be made in the light of its content and gaps, as illustrated by Gautier's careful and detailed discussion of his numerous results. 

This pioneering study is a striking demonstration of the potential of metagenomic methods for the decontamination of high-throughput sequence data at the read level. The pipeline requires remarkably few computing resources, ensuring low carbon emission. I am looking forward to seeing it applied to a wide range of taxa and samples.

Reference

[1] Gautier Mathieu. Efficient k-mer based curation of raw sequence data: application in Drosophila suzukii. bioRxiv, 2023.04.18.537389​, ver. 2, peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.04.18.537389​

Bacteria and phages have coexisted and coevolved for a long time. Phages are bacteria-infecting viruses, with a symbiotic status sensu lato, meaning they can be pathogenic, commensal or mutualistic. Thus, the association between bacteria phages has probably played a key role in the high adaptability of bacteria to most - if not all – of Earth’s ecosystems, including other living organisms (such as eukaryotes), and also regulate bacterial community size (for instance during bacterial blooms). 

As genetic entities, phages are submitted to mutations and natural selection, which changes their DNA sequence. Therefore, comparative genomic analyses of contemporary phages can be useful to understand their evolutionary dynamics. International initiatives such as SEA-PHAGES have started to tackle the issue of history of phage-bacteria interactions and to describe the dynamics of the co-evolution between bacterial hosts and their associated viruses. Indeed, the understanding of this cross-talk has many potential implications in terms of health and agriculture, among others.

The work of Koert et al. (2021) deals with one of the largest groups of bacteria (Actinobacteria), which are Gram-positive bacteria mainly found in soil and water. Some soil-born Actinobacteria develop filamentous structures reminiscent of the mycelium of eukaryotic fungi. In this study, the authors focused on the Streptomyces clade, a large genus of Actinobacteria colonized by phages known for their high level of genetic diversity.

The authors tested the hypothesis that large exchanges of genetic material occurred between Streptomyces and diverse phages associated with bacterial hosts. Using public datasets, their comparative phylogenomic analyses identified a new cluster among Actinobacteria–infecting phages closely related to phages of Firmicutes. Moreover, the GC content and codon-usage biases of this group of phages of Actinobacteria are similar to those of Firmicutes. 

This work demonstrates for the first time the transfer of a bacteriophage lineage from one bacterial phylum to another one. The results presented here suggest that the age of the described transfer is probably recent since several genomic characteristics of the phage are not fully adapted to their new hosts. However, the frequency of such transfer events remains an open question. If frequent, such exchanges would mean that pools of bacteriophages are regularly fueled by genetic material coming from external sources, which would have important implications for the co-evolutionary dynamics of phages and bacteria.

References

Koert, M., López-Pérez, J., Courtney Mattson, C., Caruso, S. and Erill, I. (2021) Evidence for shared ancestry between Actinobacteria and Firmicutes bacteriophages. bioRxiv, 842583, version 5 peer-reviewed and recommended by Peer community in Genomics. doi: https://doi.org/10.1101/842583 

The role of GC-biased gene conversion (gBGC) in molecular evolution has interested scientists for the last two decades since its discovery in 1999 (Eyre-Walker 1999; Galtier et al. 2001). gBGC is a process that is associated with meiotic recombination, and is characterized by a transmission distortion in favor of G and C over A and T alleles at GC/AT heterozygous sites that occur in the vicinity of recombination-inducing double-strand breaks (Duret and Galtier 2009; Mugal et al. 2015). This transmission distortion results in a fixation bias of G and C alleles, equivalent to directional selection for G and C (Nagylaki 1983). The fixation bias subsequently leads to a correlation between recombination rate and GC content across the genome, which has served as indirect evidence for the prevalence of gBGC in many organisms. The fixation bias also produces shifts in the allele frequency spectrum (AFS) towards higher frequencies of G and C alleles.

These molecular signatures of gBGC provide a means to quantify the strength of gBGC and study its variation among species and across the genome. Following this idea, first Lartillot (2013) and Capra et al. (2013) developed phylogenetic methodology to quantify gBGC based on substitutions, and De Maio et al. (2013) combined information on polymorphism into a phylogenetic setting. Complementary to the phylogenetic methods, later Glemin et al. (2015) developed a method that draws information solely from polymorphism data and the shape of the AFS. Application of these methods to primates (Capra et al. 2013; De Maio et al. 2013; Glemin et al. 2015) and mammals (Lartillot 2013) supported the notion that variation in the strength of gBGC across the genome reflects the dynamics of the recombination landscape, while variation among species correlates with proxies of the effective population size. However, application of the polymorphism-based method by Glemin et al. (2015) to distantly related Metazoa did not confirm the correlation with effective population size (Galtier et al. 2018).

Here, Galtier (2021) introduces a novel phylogenetic approach applicable to the study of closely related species. Specifically, Galtier introduces a statistical framework that enables the systematic study of variation in the strength of gBGC among species and among genes. In addition, Galtier assesses fine-scale variation of gBGC across the genome by means of spatial autocorrelation analysis. This puts Galtier in a position to study variation in the strength of gBGC at three different scales, i) among species, ii) among genes, and iii) within genes. Galtier applies his method to four families of mammals, Hominidae, Cercopithecidae, Bovidae, and Muridae and provides a thorough discussion of his findings and methodology.

Galtier found that the strength of gBGC correlates with proxies of the effective population size (Ne), but that the slope of the relationship differs among the four families of mammals. Given the relationship between the population-scaled strength of gBGC B = 4Neb, this finding suggests that the conversion bias (b) could vary among mammalian species. Variation in b could either result from differences in the strength of the transmission distortion (Galtier et al. 2018) or evolutionary changes in the rate of recombination (Boman et al. 2021). Alternatively, Galtier suggests that also systematic variation in proxies of Ne could lead to similar observations. Finally, the present study reports intriguing inter-species differences between the extent of variation in the strength of gBGC among and within genes, which are interpreted in consideration of the recombination dynamics in mammals.

References

Boman J, Mugal CF, Backström N (2021) The Effects of GC-Biased Gene Conversion on Patterns of Genetic Diversity among and across Butterfly Genomes. Genome Biology and Evolution, 13. https://doi.org/10.1093/gbe/evab064

Capra JA, Hubisz MJ, Kostka D, Pollard KS, Siepel A (2013) A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes. PLOS Genetics, 9, e1003684. https://doi.org/10.1371/journal.pgen.1003684

De Maio N, Schlötterer C, Kosiol C (2013) Linking Great Apes Genome Evolution across Time Scales Using Polymorphism-Aware Phylogenetic Models. Molecular Biology and Evolution, 30, 2249–2262. https://doi.org/10.1093/molbev/mst131

Duret L, Galtier N (2009) Biased Gene Conversion and the Evolution of Mammalian Genomic Landscapes. Annual Review of Genomics and Human Genetics, 10, 285–311. https://doi.org/10.1146/annurev-genom-082908-150001

Eyre-Walker A (1999) Evidence of Selection on Silent Site Base Composition in Mammals: Potential Implications for the Evolution of Isochores and Junk DNA. Genetics, 152, 675–683. https://doi.org/10.1093/genetics/152.2.675

Galtier N (2021) Fine-scale quantification of GC-biased gene conversion intensity in mammals. bioRxiv, 2021.05.05.442789, ver. 5 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.05.05.442789

Galtier N, Piganeau G, Mouchiroud D, Duret L (2001) GC-Content Evolution in Mammalian Genomes: The Biased Gene Conversion Hypothesis. Genetics, 159, 907–911. https://doi.org/10.1093/genetics/159.2.907

Galtier N, Roux C, Rousselle M, Romiguier J, Figuet E, Glémin S, Bierne N, Duret L (2018) Codon Usage Bias in Animals: Disentangling the Effects of Natural Selection, Effective Population Size, and GC-Biased Gene Conversion. Molecular Biology and Evolution, 35, 1092–1103. https://doi.org/10.1093/molbev/msy015

Glémin S, Arndt PF, Messer PW, Petrov D, Galtier N, Duret L (2015) Quantification of GC-biased gene conversion in the human genome. Genome Research, 25, 1215–1228. https://doi.org/10.1101/gr.185488.114

Lartillot N (2013) Phylogenetic Patterns of GC-Biased Gene Conversion in Placental Mammals and the Evolutionary Dynamics of Recombination Landscapes. Molecular Biology and Evolution, 30, 489–502. https://doi.org/10.1093/molbev/mss239

Mugal CF, Weber CC, Ellegren H (2015) GC-biased gene conversion links the recombination landscape and demography to genomic base composition. BioEssays, 37, 1317–1326. https://doi.org/10.1002/bies.201500058

Nagylaki T (1983) Evolution of a finite population under gene conversion. Proceedings of the National Academy of Sciences, 80, 6278–6281. https://doi.org/10.1073/pnas.80.20.6278

A wide variety of systems have evolved to control mating compatibility in sexual organisms. Their genetic determinism and the factors controlling their evolution represent fascinating questions in evolutionary biology and genomics. The plant Phillyrea angustifolia (Oleaeceae family) represents an exciting model organism, as it displays two distinct and rare mating compatibility systems [1]: 1) males and hermaphrodites co-occur in populations of this shrub (a rare system called androdioecy), while the evolution and maintenance of purely hermaphroditic plants or mixtures of females and hermaphrodites (a system called gynodioecy) are easier to explain [2]; 2) a homomorphic diallelic self-incompatibility system acts in hermaphrodites, while such systems are usually multi-allelic, as rare alleles are advantageous, being compatible with all other alleles. Previous analyses of crosses brought some interesting answers to these puzzles, showing that males benefit from the ability to mate with all hermaphrodites regardless of their allele at the self-incompatibility system, and suggesting that both sex and self incompatibility are determined by XY-like genetic systems, i.e. with each a dominant allele; homozygotes for a single allele and heterozygotes therefore co-occur in natural populations at both sex and self-incompatibility loci [3].

Here, Carré et al. used genotyping-by-sequencing to build a genome linkage map of P. angustifolia [4]. The elegant and original use of a probabilistic model of segregating alleles (implemented in the SEX-DETector method) allowed to identify both the sex and self-incompatibility loci [4], while this tool was initially developed for detecting sex-linked genes in species with strictly separated sexes (dioecy) [5]. Carré et al. [4] confirmed that the sex and self-incompatibility loci are located in two distinct linkage groups and correspond to XY-like systems. A comparison with the genome of the closely related Olive tree indicated that their self-incompatibility systems were homologous. Such a XY-like system represents a rare genetic determination mechanism for self-incompatibility and has also been recently found to control mating types in oomycetes [6].

This study [4] paves the way for identifying the genes controlling the sex and self-incompatibility phenotypes and for understanding why and how self-incompatibility is only expressed in hermaphrodites and not in males. It will also be fascinating to study more finely the degree and extent of genomic differentiation at these two loci and to assess whether recombination suppression has extended stepwise away from the sex and self-incompatibility loci, as can be expected under some hypotheses, such as the sheltering of deleterious alleles near permanently heterozygous alleles [7]. Furthermore, the co-occurrence in P. angustifolia of sex and mating types can contribute to our understanding of the factor controlling their evolution [8].

References

[1] Saumitou-Laprade P, Vernet P, Vassiliadis C, Hoareau Y, Magny G de, Dommée B, Lepart J (2010) A Self-Incompatibility System Explains High Male Frequencies in an Androdioecious Plant. Science, 327, 1648–1650. https://doi.org/10.1126/science.1186687

[2] Pannell JR, Voillemot M (2015) Plant Mating Systems: Female Sterility in the Driver’s Seat. Current Biology, 25, R511–R514. https://doi.org/10.1016/j.cub.2015.04.044

[3] Billiard S, Husse L, Lepercq P, Godé C, Bourceaux A, Lepart J, Vernet P, Saumitou-Laprade P (2015) Selfish male-determining element favors the transition from hermaphroditism to androdioecy. Evolution, 69, 683–693. https://doi.org/10.1111/evo.12613

[4] Carre A, Gallina S, Santoni S, Vernet P, Gode C, Castric V, Saumitou-Laprade P (2021) Genetic mapping of sex and self-incompatibility determinants in the androdioecious plant Phillyrea angustifolia. bioRxiv, 2021.04.15.439943, ver. 7 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.04.15.439943

[5] Muyle A, Käfer J, Zemp N, Mousset S, Picard F, Marais GA (2016) SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms. Genome Biology and Evolution, 8, 2530–2543. https://doi.org/10.1093/gbe/evw172

[6] Dussert Y, Legrand L, Mazet ID, Couture C, Piron M-C, Serre R-F, Bouchez O, Mestre P, Toffolatti SL, Giraud T, Delmotte F (2020) Identification of the First Oomycete Mating-type Locus Sequence in the Grapevine Downy Mildew Pathogen, Plasmopara viticola. Current Biology, 30, 3897-3907.e4. https://doi.org/10.1016/j.cub.2020.07.057

[7] Jay P, Tezenas E, Giraud T (2021) A deleterious mutation-sheltering theory for the evolution of sex chromosomes and supergenes. bioRxiv, 2021.05.17.444504. https://doi.org/10.1101/2021.05.17.444504

[8] Billiard S, López-Villavicencio M, Devier B, Hood ME, Fairhead C, Giraud T (2011) Having sex, yes, but with whom? Inferences from fungi on the evolution of anisogamy and mating types. Biological Reviews, 86, 421–442. https://doi.org/10.1111/j.1469-185X.2010.00153.x