Parasitoid wasps lay their eggs inside another arthropod, whose body is physically consumed by the parasitoid larvae. Phylogenetic inference suggests that Parasitoida are monophyletic, and that this clade underwent a strong radiation shortly after branching off from the Apocrita stem, some 236 million years ago (Peters et al. 2017). The increase in taxonomic diversity during evolutionary radiations is usually concurrent with an increase in genetic/genomic diversity, and is often associated with an increase in phenotypic diversity. Gene (or genome) duplication provides the evolutionary potential for such increase of genomic diversity by neo/subfunctionalisation of one of the gene paralogs, and is often proposed to be related to evolutionary radiations (Ohno 1970; Francino 2005).

In their recent preprint, Dominique Colinet and coworkers have explored the genetic and functional diversity of a Rho GTPase activating protein (RhoGAP) multigene family in two very divergent wasp clades within Parasitoida, namely Leptopilina (Figitidae) and Venturia (Ichneumonidae) (Colinet et al. 2024). Some members of the RhoGAP family are present in the venom of the parasitoid wasp Leptopilina boulardi as well as in other Leptopilina species, and are probably involved in the parasitic lifestyle by binding and inactivating host’s Rho GTPases, thereby interfering with the host’s immune response (Colinet et al. 2007).

Venom protein composition is highly variable, even between very closely related species, and is subject to rapid evolutionary changes. Although gene duplication and subsequent neo/subfunctionalisation have been frequently proposed as the main mechanism underlying this evolutionary diversification, observations are often compatible with alternative explanations, such as horizontal gene transfer, gene co-option or multifunctionalisation (Martinson et al. 2017; Alvarado et al. 2020; Huang et al. 2021; Undheim and Jenner 2021). Furthermore, high mutation rates in venom protein-encoding genes hinder phylogenetic hypothesis testing, and venom proteomics can be needed to verify transcriptomic predictions (Smith and Undheim 2018; von Reumont et al. 2022).

Colinet and coworkers (2024) have applied a combined transcriptomic, proteomic and functional approach to i) identify potential transcripts of the RhoGAP family in Leptopilina species using experimental and bioinformatic approaches; ii) experimentally identify proteins of the RhoGAP family in the venom of three Leptopilina species; iii) identify transcripts and proteins of the RhoGAP family in the ovarian calyx of Venturia canescens; and iv) perform phylogenetic and selection analyses on the extant sequences of these RhoGAP family genes to propose an evolutionary scenario for their origin and diversification. The most striking results are first the large diversity of RhoGAP sequences retrieved in the transcriptomes and proteomes of Leptopilina and of V. canescens, and second the high number of branches and positions identified to have evolved under positive selection. All the retrieved hits share a RhoGAP domain, either alone or in tandem, preceded in the case of Leptopilina RhoGAPs by a signal peptide that may be responsible for protein vehiculation for venom secretion. Further, for some of the protein positions identified to have evolved under positive selection, the authors have experimentally verified the functional impact of the changes by reverse genetic engineering.

The authors propose an evolutionary scenario to interpret the phylogenetic relationships among extant RhoGAP diversity in the clades under study. They posit that two independent, incomplete duplication events from the respectively ancestral RacGAP gene, followed by subsequent, lineage- and paralog-specific duplication events, lie at the origin of the wealth of diversity of in the Leptopilina venom RhoGAPs and of V. canescens ovarian calyx RhoGAPs. Notwithstanding, the global relationships presented in the work are not systematically consistent with this interpretation, e.g. regarding the absence of monophyly for Leptopilina RhoGAPs and Leptopilina RacGAP, and the same holds true for the respective V. canescens sequences. It may very well be that the high evolutionary rate of these genes has eroded the phylogenetic signal and prevented proper reconstruction, as the large differences between codon-based and amino acid-based phylogenies and the low support suggest. Explicit hypothesis testing, together with additional data from other taxa, may shed light onto the evolution of this gene family.

The work by Colinet and coworkers communicates sound, novel transcriptomic, proteomic and functional data from complex gene targets, consolidated from an important amount of experimental and bioinformatic work, and related to evolutionarily intriguing and complex phenotypes. These results, and the evolutionary hypothesis proposed to account for them, will be instrumental for our understanding of the evolution and diversity of vesicle-associated RhoGAPs in divergent parasitoid wasps.

References

Alvarado, G., Holland, S., R., DePerez-Rasmussen, J., Jarvis, B., A., Telander, T., Wagner, N., Waring, A., L., Anast, A., Davis, B., Frank, A., et al. (2020). Bioinformatic analysis suggests potential mechanisms underlying parasitoid venom evolution and function. Genomics 112(2), 1096–1104. https://doi.org/10.1016/j.ygeno.2019.06.022

Colinet, D., Cavigliasso, F., Leobold, M., Pichon, A., Urbach, S., Cazes, D., Poullet, M., Belghazi, M., Volkoff, A-N., Drezen, J-M., Gatti, J-L., and Poirié, M. (2024). Convergent origin and accelerated evolution of vesicle-associated RhoGAP proteins in two unrelated parasitoid wasps. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.06.05.543686

Colinet, D., Schmitz, A., Depoix, D., Crochard, D., and Poirié, M. (2007). Convergent Use of RhoGAP Toxins by eukaryotic parasites and bacterial pathogens. PLoS Pathogens 3(12), e203. https://doi.org/10.1371/journal.ppat.0030203

Francino, M.P. (2005). An adaptive radiation model for the origin of new gene functions. Nature Genetics 37, 573–577. https://doi.org/10.1038/ng1579

Huang, J., Chen, J., Fang, G., Pang, L., Zhou, S., Zhou, Y., Pan, Z., Zhang, Q., Sheng, Y., Lu, Y., et al. (2021). Two novel venom proteins underlie divergent parasitic strategies between a generalist and a specialist parasite. Nature Communications 12, 234. https://doi.org/10.1038/s41467-020-20332-8

Martinson, E., O., Mrinalini, Kelkar, Y. D., Chang, C-H., and Werren, J., H. 2017. The evolution of venom by co-option of single-copy genes. Current Biololgy 27(13), 2007-2013.e8. https://doi.org/10.1016/j.cub.2017.05.032

Ohno, S. (1970). Evolution by gene duplication. New-York: Springer-Verlag.

Peters, R., S., Krogmann, L., Mayer, C., Donath, A., Gunkel, S., Meusemann, K., Kozlov, A., Podsiadlowski, L., Petersen, M., Lanfear, R., et al. (2017). Evolutionary history of the Hymenoptera. Current Biology 27(7), 1013–1018. https://doi.org/10.1016/j.cub.2017.01.027

von Reumont, B., M., Anderluh, G., Antunes, A., Ayvazyan, N., Beis, D., Caliskan, F., Crnković, A., Damm, M., Dutertre, S., Ellgaard, L., et al. (2022). Modern venomics—Current insights, novel methods, and future perspectives in biological and applied animal venom research. GigaScience 11, giac048. https://doi.org/10.1093/gigascience/giac048

Smith, J., J., and Undheim, E., A., B. (2018). True lies: using proteomics to assess the accuracy of transcriptome-based venomics in centipedes uncovers false positives and reveals startling intraspecific variation in Scolopendra subspinipes. Toxins 10(3), 96. https://doi.org/10.3390/toxins10030096

Undheim, E., A., B., and Jenner, R., A. (2021). Phylogenetic analyses suggest centipede venom arsenals were repeatedly stocked by horizontal gene transfer. Nature Communications 12, 818. https://doi.org/10.1038/s41467-021-21093-8

Developing new sequencing and bioinformatic strategies for non-model species is of great interest in many applications, such as phylogenetic studies of diverse related species, but also for studies in population genomics, where a relatively large number of individuals is necessary. Different approaches have been developed and used in these last two decades, such as RAD-Seq (e.g., Miller et al. 2007), exome sequencing (e.g., Teer and Mullikin 2010) and other genome reduced representation methods that avoid the use of a good reference and well annotated genome (reviewed at Davey et al. 2011). However, population genomics studies require the analysis of numerous individuals, which makes the studies still expensive. Pooling samples was thought as an inexpensive strategy to obtain estimates of variability and other related to the frequency spectrum, thus allowing the study of variability at population level (e.g., Van Tassell et al. 2008), although the major drawback was the loss of information related to the linkage of the variants. In addition, population analysis using all these sequencing strategies require statistical and empirical validations that are not always fully performed. A number of studies aiming to obtain unbiased estimates of variability using reduced representation libraries and/or with pooled data have been performed (e.g., Futschik and Schlötterer 2010, Gautier et al. 2013, Ferretti et al. 2013, Lynch et al. 2014), as well as validation of new sequencing methods for population genetic analyses (e.g., Gautier et al. 2013, Nevado et al. 2014). Nevertheless, empirical validation using both pooled and individual experimental approaches combined with different bioinformatic methods has not been always performed.
Here, Deleury et al. (2020) proposed an efficient and elegant way of quantifying the single-nucleotide polymorphisms (SNPs) of exon-derived sequences in a non-model species (i.e. for which no reference genome sequence is available) at the population level scale. They also designed a new procedure to capture exon-derived sequences based on a reference transcriptome. In addition, they were able to make predictions of intron-exon boundaries for de novo transcripts based on the decay of read depth at the ends of the coding regions.
Based on theoretical predictions (Gautier et al. 2013), Deleury et al. (2020) designed a procedure to test the accuracy of variant allele frequencies (AFs) with pooled samples, in a reduced genome-sequence library made with transcriptome regions, and additionally testing the effects of new bioinformatic methods in contrast to standardized methods. They applied their strategy on the non-model species Asian ladybird (Harmonia axyridis), for which a draft genome is available, thereby allowing them to benchmark their method with regard to a traditional mapping-based approach. Based on species-specific de novo transcriptomes, they designed capture probes which are then used to call SNPx and then compared the resulting SNP AFs at the individual (multiplexed) versus population (pooled) levels. Interestingly, they showed that SNP AFs in the pool sequencing strategy nicely correlate with the individual ones but obviously in a cost-effective way. Studies of population genomics for non-model species have usually limited budgets. The number of individuals required for population genomics analysis multiply the costs of the project, making pooling samples an interesting option. Furthermore, the use of pool sequencing is not always a choice, as many organisms are too small and/or individuals are too sticked each other to be individually sequenced (e.g., Choquet et al. 2019, Kurland et al. 2019). In addition, the study of a reduced section of the genome is cheaper and often sufficient for a number of population genetic questions, such as the understanding of general demographic events, or the estimation of the effects of positive and/or negative selection at functional coding regions. Studies on population genomics of non-model species have many applications in related fields, such as conservation genetics, control of invasive species, etc. The work of Deleury et al. (2020) is an elegant contribution to the assessment and validation of new methodologies used for the analysis of genome variations at the intra-population variability level, highlighting straight bioinformatic and reliable sequencing methods for population genomics studies.

References

[1] Choquet et al. (2019). Towards population genomics in non-model species with large genomes: a case study of the marine zooplankton Calanus finmarchicus. Royal Society open science, 6(2), 180608. doi: https://doi.org/10.1098/rsos.180608
[2] Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M. and Blaxter, M. L. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499-510. doi: https://doi.org/10.1038/nrg3012
[3] Deleury, E., Guillemaud, T., Blin, A. and Lombaert, E. (2020) An evaluation of pool-sequencing transcriptome-based exon capture for population genomics in non-model species. bioRxiv, 10.1101/583534, ver. 7 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/583534
[4] Ferretti, L., Ramos‐Onsins, S. E. and Pérez‐Enciso, M. (2013). Population genomics from pool sequencing. Molecular ecology, 22(22), 5561-5576. doi: https://doi.org/10.1111/mec.12522
[5] Futschik, A. and Schlötterer, C. (2010). Massively parallel sequencing of pooled DNA samples—the next generation of molecular markers. Genetics, 186 (1), 207-218. doi: https://doi.org/10.1534/genetics.110.114397
[6] Gautier et al. (2013). Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping. Molecular Ecology, 22(14), 3766-3779. doi: https://doi.org/10.1111/mec.12360
[7] Kurland et al. (2019). Exploring a Pool‐seq‐only approach for gaining population genomic insights in nonmodel species. Ecology and evolution, 9(19), 11448-11463. doi: https://doi.org/10.1002/ece3.5646
[8] Lynch, M., Bost, D., Wilson, S., Maruki, T. and Harrison, S. (2014). Population-genetic inference from pooled-sequencing data. Genome biology and evolution, 6(5), 1210-1218. doi: https://doi.org/10.1093/gbe/evu085
[9] Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. and Johnson, E. A. (2007). Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome research, 17(2), 240-248. doi: https://doi.org/10.1101%2Fgr.5681207
[10] Nevado, B., Ramos‐Onsins, S. E. and Perez‐Enciso, M. (2014). Resequencing studies of nonmodel organisms using closely related reference genomes: optimal experimental designs and bioinformatics approaches for population genomics. Molecular ecology, 23(7), 1764-1779. doi: https://doi.org/10.1111/mec.12693
[11] Teer, J. K. and Mullikin, J. C. (2010). Exome sequencing: the sweet spot before whole genomes. Human molecular genetics, 19(R2), R145-R151. doi: https://doi.org/10.1093/hmg/ddq333
[12] Van Tassell et al. (2008). SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nature methods, 5(3), 247-252. doi: https://doi.org/10.1038/nmeth.1185

Repetitive extragenic palindromic sequences (REPs) are common repetitive elements in bacterial genomes (Gilson et al., 1984; Stern et al., 1984). In 2011, Bertels and Rainey identified that REPs are overrepresented in pairs of inverted repeats, which likely form hairpin structures, that they referred to as “REP doublets forming hairpins” (REPINs). Based on bioinformatics analyses, they argued that REPINs are likely selfish elements that evolved from REPs flanking particular transposes (Bertels and Rainey, 2011). These transposases, so-called REP-associated tyrosine transposases (RAYTs), were known to be highly associated with the REP content in a genome and to have characteristic upstream and downstream flanking REPs (Nunvar et al., 2010). The flanking REPs likely enable RAYT transposition, and their horizontal replication is physically linked to this process. In contrast, Bertels and Rainey hypothesized that REPINs are selfish elements that are highly replicated due to the similarity in arrangement to these RAYT-flanking REPs, but independent of RAYT transposition and generally with no impact on bacterial fitness (Bertels and Rainey, 2011).

This last point was especially contentious, as REPINs are highly conserved within species (Bertels and Rainey, 2023), which is unusual for non-beneficial bacterial DNA (Mira et al., 2001). Bertels and Rainey have since refined their argument to be that REPINs must provide benefits to host cells, but that there are nonetheless signatures of intragenomic conflict in genomes associated with these elements (Bertels and Rainey, 2023). These signatures reflect the divergent levels of selections driving REPIN distribution: selection at the level of each DNA element and selection on each individual bacterium. I found this observation particularly interesting as I and my colleague recently argued that these divergent levels of selection, and the interaction between them, is key to understanding bacterial pangenome diversity (Douglas and Shapiro, 2021). REPINs could be an excellent system for investigating these levels of selection across bacteria more generally.

The problem is that REPINs have not been widely characterized in bacterial genomes, partially because no bioinformatic workflow has been available for this purpose. To address this problem, Fortmann-Grote et al. (2023) developed RAREFAN, which is a web server for identifying RAYTs and associated REPINs in a set of input genomes. The authors showcase their tool by applying it to 49 Stenotrophomonas maltophilia genomes and providing examples of how to identify and assess RAYT-REPIN hits. The workflow requires several manual steps, but nonetheless represents a straightforward and standardized approach. Overall, this workflow should enable RAYTs and REPINs to be identified across diverse bacterial species, which will facilitate further investigation into the mechanisms driving their maintenance and spread.

References

Bertels F, Rainey PB (2023) Ancient Darwinian replicators nested within eubacterial genomes. BioEssays, 45, 2200085. https://doi.org/10.1002/bies.202200085

Bertels F, Rainey PB (2011) Within-Genome Evolution of REPINs: a New Family of Miniature Mobile DNA in Bacteria. PLOS Genetics, 7, e1002132. https://doi.org/10.1371/journal.pgen.1002132

Douglas GM, Shapiro BJ (2021) Genic Selection Within Prokaryotic Pangenomes. Genome Biology and Evolution, 13, evab234. https://doi.org/10.1093/gbe/evab234

Fortmann-Grote C, Irmer J von, Bertels F (2023) RAREFAN: A webservice to identify REPINs and RAYTs in bacterial genomes. bioRxiv, 2022.05.22.493013, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.05.22.493013

Gilson E, Clément J m., Brutlag D, Hofnung M (1984) A family of dispersed repetitive extragenic palindromic DNA sequences in E. coli. The EMBO Journal, 3, 1417–1421. https://doi.org/10.1002/j.1460-2075.1984.tb01986.x

Mira A, Ochman H, Moran NA (2001) Deletional bias and the evolution of bacterial genomes. Trends in Genetics, 17, 589–596. https://doi.org/10.1016/S0168-9525(01)02447-7

Nunvar J, Huckova T, Licha I (2010) Identification and characterization of repetitive extragenic palindromes (REP)-associated tyrosine transposases: implications for REP evolution and dynamics in bacterial genomes. BMC Genomics, 11, 44. https://doi.org/10.1186/1471-2164-11-44

Stern MJ, Ames GF-L, Smith NH, Clare Robinson E, Higgins CF (1984) Repetitive extragenic palindromic sequences: A major component of the bacterial genome. Cell, 37, 1015–1026. https://doi.org/10.1016/0092-8674(84)90436-7

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​

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