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2021-07-20
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Genetic mapping of sex and self-incompatibility determinants in the androdioecious plant Phillyrea angustifolia

Recommended by and based on reviews by 2 anonymous reviewers

Identification of distinct YX-like loci for sex determination and self-incompatibility in an androdioecious shrub

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

2021-07-19
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TransPi - a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly

Recommended by based on reviews by Juan Daniel Montenegro Cabrera and Gustavo Sanchez

TransPI: A balancing act between transcriptome assemblers

Ever since the introduction of the first widely usable assemblers for transcriptomic reads (Huang and Madan 1999; Schulz et al. 2012; Simpson et al. 2009; Trapnell et al. 2010, and many more), it has been a technical challenge to compare different methods and to choose the “right” or “best” assembly. It took years until the first widely accepted set of benchmarks beyond raw statistical evaluation became available (e.g., Parra, Bradnam, and Korf 2007; Simão et al. 2015)⁠⁠. However, an approach to find the right balance between the number of transcripts or isoforms vs. evolutionary completeness measures has been lacking. This has been particularly pronounced in the field of non-model organisms (i.e., wild species that lack a genomic reference). Often, studies in this area employed only one set of assembly tools (the most often used to this day being Trinity, Haas et al. 2013; Grabherr et al. 2011)⁠. While it was relatively straightforward to obtain an initial assembly, its validation, annotation, as well its application to the particular purpose that the study was designed for (phylogenetics, differential gene expression, etc) lacked a clear workflow. This led to many studies using a custom set of tools with ensuing various degrees of reproducibility.

TransPi (Rivera-Vicéns et al. 2021)⁠ fills this gap by first employing a meta approach using several available transcriptome assemblers and algorithms to produce a combined and reduced transcriptome assembly, then validating and annotating the resulting transcriptome. Notably, TransPI performs an extensive analysis/detection of chimeric transcripts, the results of which show that this new tool often produces fewer misassemblies compared to Trinity. TransPI not only generates a final report that includes the most important plots (in clickable/zoomable format) but also stores all relevant intermediate files, allowing advanced users to take a deeper look and/or experiment with different settings. As running TransPi is largely automated (including its installation via several popular package managers), it is very user-friendly and is likely to become the new "gold standard" for transcriptome analyses, especially of non-model organisms.  

References

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29, 644–652. https://doi.org/10.1038/nbt.1883

Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, MacManes MD, Ott M, Orvis J, Pochet N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R, LeDuc RD, Friedman N, Regev A (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature Protocols, 8, 1494–1512. https://doi.org/10.1038/nprot.2013.084

Huang X, Madan A (1999) CAP3: A DNA Sequence Assembly Program. Genome Research, 9, 868–877. https://doi.org/10.1101/gr.9.9.868

Parra G, Bradnam K, Korf I (2007) CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics, 23, 1061–1067. https://doi.org/10.1093/bioinformatics/btm071

Rivera-Vicéns RE, Garcia-Escudero CA, Conci N, Eitel M, Wörheide G (2021) TransPi – a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly. bioRxiv, 2021.02.18.431773, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.02.18.431773

Schulz MH, Zerbino DR, Vingron M, Birney E (2012) Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics, 28, 1086–1092. https://doi.org/10.1093/bioinformatics/bts094

Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics, 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351

Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJM, Birol İ (2009) ABySS: A parallel assembler for short read sequence data. Genome Research, 19, 1117–1123. https://doi.org/10.1101/gr.089532.108

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 28, 511–515. https://doi.org/10.1038/nbt.1621

2021-07-06
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A pipeline to detect the relationship between transposable elements and adjacent genes in host genomes

Recommended by based on reviews by 2 anonymous reviewers

A new tool to cross and analyze TE and gene annotations

Transposable elements (TEs) are important components of genomes. Indeed, they are now recognized as having a major role in gene and genome evolution (Biémont 2010). In particular, several examples have shown that the presence of TEs near genes may influence their functioning, either by recruiting particular epigenetic modifications (Guio et al. 2018) or by directly providing new regulatory sequences allowing new expression patterns (Chung et al. 2007; Sundaram et al. 2014). Therefore, the study of the interaction between TEs and their host genome requires tools to easily cross-annotate both types of entities. In particular, one needs to be able to identify all TEs located in the close vicinity of genes or inside them. Such task may not always be obvious for many biologists, as it requires informatics knowledge to develop their own script codes.

In their work, Meguerdichian et al. (2021) propose a command-line pipeline that takes as input the annotations of both genes and TEs for a given genome, then detects and reports the positional relationships between each TE insertion and their closest genes. The results are processed into an R script to provide tables displaying some statistics and graphs to visualize these relationships. 

This tool has the potential to be very useful for performing preliminary analyses before studying the impact of TEs on gene functioning, especially for biologists. Indeed, it makes it possible to identify genes close to TE insertions. These identified genes could then be specifically considered in order to study in more detail the link between the presence of TEs and their functioning. For example, the identification of TEs close to genes may allow to determine their potential role on gene expression.

References

Biémont C (2010). A brief history of the status of transposable elements: from junk DNA to major players in evolution. Genetics, 186, 1085–1093. https://doi.org/10.1534/genetics.110.124180

Chung H, Bogwitz MR, McCart C, Andrianopoulos A, ffrench-Constant RH, Batterham P, Daborn PJ (2007). Cis-regulatory elements in the Accord retrotransposon result in tissue-specific expression of the Drosophila melanogaster insecticide resistance gene Cyp6g1. Genetics, 175, 1071–1077. https://doi.org/10.1534/genetics.106.066597

Guio L, Vieira C, González J (2018). Stress affects the epigenetic marks added by natural transposable element insertions in Drosophila melanogaster. Scientific Reports, 8, 12197. https://doi.org/10.1038/s41598-018-30491-w

Meguerditchian C, Ergun A, Decroocq V, Lefebvre M, Bui Q-T (2021). A pipeline to detect the relationship between transposable elements and adjacent genes in host genomes. bioRxiv, 2021.02.25.432867, ver. 4 peer-reviewed and recommended by Peer Community In Genomics. https://doi.org/10.1101/2021.02.25.432867

Sundaram V, Cheng Y, Ma Z, Li D, Xing X, Edge P, Snyder MP, Wang T (2014). Widespread contribution of transposable elements to the innovation of gene regulatory networks. Genome Research, 24, 1963–1976. https://doi.org/10.1101/gr.168872.113

2021-05-05
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A primer and discussion on DNA-based microbiome data and related bioinformatics analyses

Recommended by based on reviews by Rafael Cuadrat, Nicolas Pollet and 1 anonymous reviewer

A hitchhiker’s guide to DNA-based microbiome analysis

In the last two decades, microbial research in its different fields has been increasingly focusing on microbiome studies. These are defined as studies of complete assemblages of microorganisms in given environments and have been benefiting from increases in sequencing length, quality, and yield, coupled with ever-dropping prices per sequenced nucleotide. Alongside localized microbiome studies, several global collaborative efforts have emerged, including the Human Microbiome Project [1], the Earth Microbiome Project [2], the Extreme Microbiome Project, and MetaSUB [3].

Coupled with the development of sequencing technologies and the ever-increasing amount of data output, multiple standalone or online bioinformatic tools have been designed to analyze these data. Often these tools have been focusing on either of two main tasks: 1) Community analysis, providing information on the organisms present in the microbiome, or 2) Functionality, in the case of shotgun metagenomic data, providing information on the metabolic potential of the microbiome. Bridging between the two types of data, often extracted from the same dataset, is typically a daunting task that has been addressed by a handful of tools only.

The extent of tools and approaches to analyze microbiome data is great and may be overwhelming to researchers new to microbiome or bioinformatic studies. In their paper “A primer and discussion on DNA-based microbiome data and related bioinformatics analyses”, Douglas and Langille [4] guide us through the different sequencing approaches useful for microbiome studies. alongside their advantages and caveats and a selection of tools to analyze these data, coupled with examples from their own field of research.

Standing out in their primer-style review is the emphasis on the coupling between taxonomic/phylogenetic identification of the organisms and their functionality. This type of analysis, though highly important to understand the role of different microorganisms in an environment as well as to identify potential functional redundancy, is often not conducted. For this, the authors identify two approaches. The first, using shotgun metagenomics, has higher chances of attributing a function to the correct taxon. The second, using amplicon sequencing of marker genes, allows for a deeper coverage of the microbiome at a lower cost, and extrapolates the amplicon data to close relatives with a sequenced genome. As clearly stated, this approach makes the leap between taxonomy and functionality and has been shown to be erroneous in cases where the core genome of the bacterial genus or family does not encompass the functional diversity of the different included species. This practice was already common before the genomic era, but its accuracy is improving thanks to the increasing availability of sequenced reference genomes from cultures, environmentally picked single cells or metagenome-assembled genome.

In addition to their description of standalone tools useful for linking taxonomy and functionality, one should mention the existence of online tools that may appeal to researchers who do not have access to adequate bioinformatics infrastructure. Among these are the Integrated Microbial Genomes and Microbiomes (IMG) from the Joint Genome Institute [5], KBase [6] and MG-RAST [7].

A second important point arising from this review is the need for standardization in microbiome data analyses and the complexity of achieving this. As Douglas and Langille [4] state, this has been previously addressed, highlighting the variability in results obtained with different tools. It is often the case that papers describing new bioinformatic tools display their superiority relative to existing alternatives, potentially misleading newcomers to the field that the newest tool is the best and only one to be used. This is often not the case, and while benchmarking against well-defined datasets serves as a powerful testing tool, “real-life” samples are often not comparable. Thus, as done here, future primer-like reviews should highlight possible cross-field caveats, encouraging researchers to employ and test several approaches and validate their results whenever possible.

In summary, Douglas and Langille [4] offer both the novice and experienced researcher a detailed guide along the paths of microbiome data analysis, accompanied by informative background information, suggested tools with which analyses can be started, and an insightful view on where the field should be heading.

References

[1]  Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI (2007) The Human Microbiome Project. Nature, 449, 804–810. https://doi.org/10.1038/nature06244

[2]  Gilbert JA, Jansson JK, Knight R (2014) The Earth Microbiome project: successes and aspirations. BMC Biology, 12, 69. https://doi.org/10.1186/s12915-014-0069-1

[3]  Mason C, Afshinnekoo E, Ahsannudin S, Ghedin E, Read T, Fraser C, Dudley J, Hernandez M, Bowler C, Stolovitzky G, Chernonetz A, Gray A, Darling A, Burke C, Łabaj PP, Graf A, Noushmehr H, Moraes  s., Dias-Neto E, Ugalde J, Guo Y, Zhou Y, Xie Z, Zheng D, Zhou H, Shi L, Zhu S, Tang A, Ivanković T, Siam R, Rascovan N, Richard H, Lafontaine I, Baron C, Nedunuri N, Prithiviraj B, Hyat S, Mehr S, Banihashemi K, Segata N, Suzuki H, Alpuche Aranda CM, Martinez J, Christopher Dada A, Osuolale O, Oguntoyinbo F, Dybwad M, Oliveira M, Fernandes A, Oliveira M, Fernandes A, Chatziefthimiou AD, Chaker S, Alexeev D, Chuvelev D, Kurilshikov A, Schuster S, Siwo GH, Jang S, Seo SC, Hwang SH, Ossowski S, Bezdan D, Udekwu K, Udekwu K, Lungjdahl PO, Nikolayeva O, Sezerman U, Kelly F, Metrustry S, Elhaik E, Gonnet G, Schriml L, Mongodin E, Huttenhower C, Gilbert J, Hernandez M, Vayndorf E, Blaser M, Schadt E, Eisen J, Beitel C, Hirschberg D, Schriml L, Mongodin E, The MetaSUB International Consortium (2016) The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report. Microbiome, 4, 24. https://doi.org/10.1186/s40168-016-0168-z

[4]  Douglas GM, Langille MGI (2021) A primer and discussion on DNA-based microbiome data and related bioinformatics analyses. OSF Preprints, ver. 4 peer-reviewed and recommended by Peer Community In Genomics. https://doi.org/10.31219/osf.io/3dybg

[5]  Chen I-MA, Markowitz VM, Chu K, Palaniappan K, Szeto E, Pillay M, Ratner A, Huang J, Andersen E, Huntemann M, Varghese N, Hadjithomas M, Tennessen K, Nielsen T, Ivanova NN, Kyrpides NC (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Research, 45, D507–D516. https://doi.org/10.1093/nar/gkw929

[6]  Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, Dehal P, Ware D, Perez F, Canon S, Sneddon MW, Henderson ML, Riehl WJ, Murphy-Olson D, Chan SY, Kamimura RT, Kumari S, Drake MM, Brettin TS, Glass EM, Chivian D, Gunter D, Weston DJ, Allen BH, Baumohl J, Best AA, Bowen B, Brenner SE, Bun CC, Chandonia J-M, Chia J-M, Colasanti R, Conrad N, Davis JJ, Davison BH, DeJongh M, Devoid S, Dietrich E, Dubchak I, Edirisinghe JN, Fang G, Faria JP, Frybarger PM, Gerlach W, Gerstein M, Greiner A, Gurtowski J, Haun HL, He F, Jain R, Joachimiak MP, Keegan KP, Kondo S, Kumar V, Land ML, Meyer F, Mills M, Novichkov PS, Oh T, Olsen GJ, Olson R, Parrello B, Pasternak S, Pearson E, Poon SS, Price GA, Ramakrishnan S, Ranjan P, Ronald PC, Schatz MC, Seaver SMD, Shukla M, Sutormin RA, Syed MH, Thomason J, Tintle NL, Wang D, Xia F, Yoo H, Yoo S, Yu D (2018) KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology, 36, 566–569. https://doi.org/10.1038/nbt.4163

[7]  Wilke A, Bischof J, Gerlach W, Glass E, Harrison T, Keegan KP, Paczian T, Trimble WL, Bagchi S, Grama A, Chaterji S, Meyer F (2016) The MG-RAST metagenomics database and portal in 2015. Nucleic Acids Research, 44, D590–D594. https://doi.org/10.1093/nar/gkv1322

2021-04-27
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Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzzii

Recommended by based on reviews by Yann Bourgeois and 1 anonymous reviewer

Anopheles coluzzii, a new system to study how transposable elements may foster adaptation to urban environments

Transposable elements (TEs) are mobile DNA sequences that can increase their copy number and move from one location to another within the genome [1]. Because of their transposition dynamics, TEs constitute a significant fraction of eukaryotic genomes. TEs are also known to play an important functional role and a wealth of studies has now reported how TEs may influence single host traits [e.g. 2–4]. Given that TEs are more likely than classical point mutations to cause extreme changes in gene expression and phenotypes, they might therefore be especially prone to produce the raw diversity necessary for individuals to respond to challenging environments [5,6] such as the ones found in urban area.  
In their study [7], Vargas et al. establish the foundation to investigate how TEs may help Anopheles coluzzii -  the primary vectors of human malaria in sub-Saharan Africa - adapt to urban environments. To cover natural breeding sites in major Central Africa cities, they made use of the previously available An. coluzzii genome from Yaoundé (Cameroon) and sequenced with long-read technology six additional ones originating from Douala (Cameroon) and Libreville (Gabon). The de novo annotation of TEs in these genomes revealed 64 new anopheline TE families and allowed to identify seven active families. As a first step towards characterizing the potential role of TEs in the adaptation of An. coluzzii to urban environments, they further analyzed the distribution of TEs across the seven genomes. By doing so, they identified a significant number of polymorphic or fixed TE insertions located in the vicinity of genes involved in insecticide resistance and immune response genes.  
The availability of seven An. coluzzii genomes allowed the authors to explore how TE diversity may affect genes functionally relevant for the adaptation to urban environments and provide ground for further functional validation studies. More and more studies have demonstrated the impact of TEs on adaptation and as such, the work of Vargas et al. contributes to fostering our understanding of the link between TEs and gain of function in a species facing strong anthropogenic pressures.  
 
References  
  
[1] Wicker T, Sabot F, Hua-Van A, Bennetzen JL, Capy P, Chalhoub B, Flavell A, Leroy P, Morgante M, Panaud O, Paux E, SanMiguel P, Schulman AH (2007) A unified classification system for eukaryotic transposable elements. Nature Reviews Genetics, 8, 973–982. https://doi.org/10.1038/nrg2165    
  
[2] van’t Hof AE, Campagne P, Rigden DJ, Yung CJ, Lingley J, Quail MA, Hall N, Darby AC, Saccheri IJ (2016) The industrial melanism mutation in British peppered moths is a transposable element. Nature, 534, 102–105. https://doi.org/10.1038/nature17951    
  
[3] González J, Karasov TL, Messer PW, Petrov DA (2010) Genome-wide patterns of adaptation to temperate environments associated with transposable elements in Drosophila. PLOS Genetics, 6, e1000905. https://doi.org/10.1371/journal.pgen.1000905  
  
[4] Lisch D (2013) How important are transposons for plant evolution? Nature Reviews Genetics, 14, 49–61. https://doi.org/10.1038/nrg3374    
  
[5] Bonchev G, Parisod C (2013) Transposable elements and microevolutionary changes in natural populations. Molecular Ecology Resources, 13, 765–775. https://doi.org/10.1111/1755-0998.12133  
  
[6] Casacuberta E, González J (2013) The impact of transposable elements in environmental adaptation. Molecular Ecology, 22, 1503–1517. https://doi.org/10.1111/mec.12170    
  
[7] Vargas-Chavez C, Pendy NML, Nsango SE, Aguilera L, Ayala D, González J (2021). Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzzii. bioRxiv, 2020.11.22.393231, ver. 3 peer-reviewed and recommended by Peer community in Genomics. https://doi.org/10.1101/2020.11.22.393231  

 

2021-04-06
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Evidence for shared ancestry between Actinobacteria and Firmicutes bacteriophages

Recommended by based on reviews by 3 anonymous reviewers

Viruses of bacteria: phages evolution across phylum boundaries

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 

2021-04-02
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Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Recommended by based on reviews by Alexander Suh and 1 anonymous reviewer

Toward a critical assessment of virus detection in plants

The advent of High Throughput Sequencing (HTS) since the last decade has revealed previously unsuspected diversity of viruses as well as their (sometimes) unexpected presence in some healthy individuals. These results demonstrate that genomics offers a powerful tool for studying viruses at the individual level, allowing an in-depth inventory of those that are infecting an organism. Such approaches make it possible to study viromes with an unprecedented level of detail, both qualitative and quantitative, which opens new venues for analyses of viruses of humans, animals and plants. Consequently, the diagnostic field is using more and more HTS, fueling the need for efficient and reliable bioinformatics tools. 

Many such tools have already been developed, but in plant disease diagnostics, validation of the bioinformatics pipelines used for the detection of viruses in HTS datasets is still in its infancy. There is an urgent need for benchmarking the different tools and algorithms using well-designed reference datasets generated for this purpose. This is a crucial step to move forward and to improve existing solutions toward well-standardized bioinformatics protocols. This context has led to the creation of the Plant Health Bioinformatics Network (PHBN), a Euphresco network project aiming to build a bioinformatics community working on plant health. One of their objectives is to provide researchers with open-access reference datasets allowing to compare and validate virus detection pipelines. 

In this framework, Tamisier et al. [1] present real, semi-artificial, and completely artificial datasets, each aimed at addressing challenges that could affect virus detection. These datasets comprise real RNA-seq reads from virus-infected plants as well as simulated virus reads. Such a work, providing open-access datasets for benchmarking bioinformatics tools, should be encouraged as they are key to software improvement as demonstrated by the well-known success story of the protein structure prediction community: their pioneer community-wide effort, called Critical Assessment of protein Structure Prediction (CASP)[2], has been providing research groups since 1994 with an invaluable way to objectively test their structure prediction methods, thereby delivering an independent assessment of state-of-art protein-structure modelling tools. Following this success, many other bioinformatic community developed similar “competitions”, such as RNA-puzzles [3] to predict RNA structures, Critical Assessment of Function Annotation [4] to predict gene functions, Critical Assessment of Prediction of Interactions [5] to predict protein-protein interactions, Assemblathon [6] for genome assembly, etc. These are just a few examples from a long list of successful initiatives. Such efforts enable rigorous assessments of tools, stimulate the developers’ creativity, but also provide user communities with a state-of-art evaluation of available tools.

Inspired by these success stories, the authors propose a “VIROMOCK challenge” [7], asking researchers in the field to test their tools and to provide feedback on each dataset through a repository. This initiative, if well followed, will undoubtedly improve the field of virus detection in plants, but also probably in many other organisms. This will be a major contribution to the field of viruses, leading to better diagnostics and, consequently, a better understanding of viral diseases, thus participating in promoting human, animal and plant health.   

References

[1] Tamisier, L., Haegeman, A., Foucart, Y., Fouillien, N., Al Rwahnih, M., Buzkan, N., Candresse, T., Chiumenti, M., De Jonghe, K., Lefebvre, M., Margaria, P., Reynard, J.-S., Stevens, K., Kutnjak, D. and Massart, S. (2021) Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. Zenodo, 4273791, version 4 peer-reviewed and recommended by Peer community in Genomics. doi: https://doi.org/10.5281/zenodo.4273791

[2] Critical Assessment of protein Structure Prediction” (CASP) - https://en.wikipedia.org/wiki/CASP

[3] RNA-puzzles - https://www.rnapuzzles.org

[4] Critical Assessment of Function Annotation (CAFA) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Function_Annotation

[5] Critical Assessment of Prediction of Interactions (CAPI) - https://en.wikipedia.org/wiki/Critical_Assessment_of_Prediction_of_Interactions

[6] Assemblathon - https://assemblathon.org

[7] VIROMOCK challenge - https://gitlab.com/ilvo/VIROMOCKchallenge

2021-03-11
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Gut microbial ecology of Xenopus tadpoles across life stages

Recommended by based on reviews by Vanessa Marcelino and 1 anonymous reviewer

A comprehensive look at Xenopus gut microbiota: effects of feed, developmental stages and parental transmission

It is well established that the gut microbiota play an important role in the overall health of their hosts (Jandhyala et al. 2015). To date, there are still a limited number of studies on the complex microbial communites inhabiting vertebrate digestive systems, especially the ones that also explored the functional diversity of the microbial community (Bletz et al. 2016).

This preprint by Scalvenzi et al. (2021) reports a comprehensive study on the phylogenetic and metabolic profiles of the Xenopus gut microbiota. The author describes significant changes in the gut microbiome communities at different developmental stages and demonstrates different microbial community composition across organs. In addition, the study also investigates the impact of diet on the Xenopus tadpole gut microbiome communities as well as how the bacterial communities are transmitted from parents to the next generation.

This is one of the first studies that addresses the interactions between gut bacteria and tadpoles during the development. The authors observe the dynamics of gut microbiome communities during tadpole growth and metamorphosis. They also explore host-gut microbial community metabolic interactions and demostrate the capacity of the microbiome to complement the metabolic pathways of the Xenopus genome. Although this study is limited by the use of Xenopus tadpoles in a laboratory, which are probably different from those in nature, I believe it still provides important and valuable information for the research community working on vertebrate’s microbiota and their interaction with the host. 

References

Bletz et al. (2016). Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nature Communications, 7(1), 1-12. doi: https://doi.org/10.1038/ncomms13699

Jandhyala, S. M., Talukdar, R., Subramanyam, C., Vuyyuru, H., Sasikala, M., & Reddy, D. N. (2015). Role of the normal gut microbiota. World journal of gastroenterology: WJG, 21(29), 8787. doi: https://dx.doi.org/10.3748%2Fwjg.v21.i29.8787

Scalvenzi, T., Clavereau, I., Bourge, M. & Pollet, N. (2021) Gut microbial ecology of Xenopus tadpoles across life stages. bioRxiv, 2020.05.25.110734, ver. 4 peer-reviewed and recommended by Peer community in Geonmics. https://doi.org/10.1101/2020.05.25.110734

2021-02-18
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Traces of transposable element in genome dark matter co-opted by flowering gene regulation networks

Recommended by based on reviews by Josep Casacuberta and 1 anonymous reviewer

Using small fragments to discover old TE remnants: the Duster approach empowers the TE detection

Transposable elements are the raw material of the dark matter of the genome, the foundation of the next generation of genes and regulation networks". This sentence could be the essence of the paper of Baud et al. (2021). Transposable elements (TEs) are endogenous mobile genetic elements found in almost all genomes, which were discovered in 1948 by Barbara McClintock (awarded in 1983 the only unshared Medicine Nobel Prize so far). TEs are present everywhere, from a single isolated copy for some elements to more than millions for others, such as Alu. They are founders of major gene lineages (HET-A, TART and telomerases, RAG1/RAG2 proteins from mammals immune system; Diwash et al, 2017), and even of retroviruses (Xiong & Eickbush, 1988). However, most TEs appear as selfish elements that replicate, land in a new genomic region, then start to decay and finally disappear in the midst of the genome, turning into genomic ‘dark matter’ (Vitte et al, 2007). The mutations (single point, deletion, recombination, and so on) that occur during this slow death erase some of their most notable features and signature sequences, rendering them completely unrecognizable after a few million years. Numerous TE detection tools have tried to optimize their detection (Goerner-Potvin & Bourque, 2018), but further improvement is definitely challenging. This is what Baud et al. (2021) accomplished in their paper. They used a simple, elegant and efficient k-mer based approach to find small signatures that, when accumulated, allow identifying very old TEs. Using this method, called Duster, they improved the amount of annotated TEs in the model plant Arabidopsis thaliana by 20%, pushing the part of this genome occupied by TEs up from 40 to almost 50%. They further observed that these very old Duster-specific TEs (i.e., TEs that are only detected by Duster) are, among other properties, close to genes (much more than recent TEs), not targeted by small RNA pathways, and highly associated with conserved regions across the rosid family. In addition, they are highly associated with flowering or stress response genes, and may be involved through exaptation in the evolution of responses to environmental changes. TEs are not just selfish elements: more and more studies have shown their key role in the evolution of their hosts, and tools such as Duster will help us better understand their impact.
 

References

Baud, A., Wan, M., Nouaud, D., Francillonne, N., Anxolabéhère, D. and Quesneville, H. (2021). Traces of transposable elements in genome dark matter co-opted by flowering gene regulation networks. bioRxiv, 547877, ver. 5 peer-reviewed and recommended by PCI Genomics.doi: https://doi.org/10.1101/547877
 
Bourque, G., Burns, K.H., Gehring, M. et al. (2018) Ten things you should know about transposable elements. Genome Biology 19:199. doi: https://doi.org/10.1186/s13059-018-1577-z
 
Goerner-Potvin, P., Bourque, G. Computational tools to unmask transposable elements. Nature Reviews Genetics 19:688–704 (2018) https://doi.org/10.1038/s41576-018-0050-x
 
Jangam, D., Feschotte, C. and Betrán, E. (2017) Transposable element domestication as an adaptation to evolutionary conflicts. Trends in Genetics 33:817-831. doi: https://doi.org/10.1016/j.tig.2017.07.011
 
Vitte, C., Panaud, O. and Quesneville, H. (2007) LTR retrotransposons in rice (Oryza sativa, L.): recent burst amplifications followed by rapid DNA loss. BMC Genomics 8:218. doi: https://doi.org/10.1186/1471-2164-8-218
 
Xiong, Y. and Eickbush, T. H. (1988) Similarity of reverse transcriptase-like sequences of viruses, transposable elements, and mitochondrial introns. Molecular Biology and Evolution 5: 675–690. doi: https://doi.org/10.1093/oxfordjournals.molbev.a040521

2020-10-09
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An evaluation of pool-sequencing transcriptome-based exon capture for population genomics in non-model species

Recommended by and based on reviews by Valentin Wucher and 1 anonymous reviewer

Assessing a novel sequencing-based approach for population genomics in non-model species

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

2020-09-24
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A rapid and simple method for assessing and representing genome sequence relatedness

Recommended by based on reviews by Gavin Douglas and 1 anonymous reviewer

A quick alternative method for resolving bacterial taxonomy using short identical DNA sequences in genomes or metagenomes

The bacterial species problem can be summarized as follows: bacteria recombine too little, and yet too much (Shapiro 2019).
Too little in the sense that recombination is not obligately coupled with reproduction, as in sexual eukaryotes. So the Biological Species Concept (BSC) of reproductive isolation does not strictly apply to clonally reproducing organisms like bacteria. Too much in the sense that genetic exchange can occur promiscuously across species (or even Domains), potentially obscuring species boundaries.
In parallel to such theoretical considerations, several research groups have taken more pragmatic approaches to defining bacterial species based on sequence similarity cutoffs, such as genome-wide average nucleotide identity (ANI). At a cutoff above 95% ANI, genomes are considered to come from the same species. While this cutoff may appear arbitrary, a discontinuity around 95% in the distribution of ANI values has been argued to provide a 'natural' cutoff (Jain et al. 2018). This discontinuity has been criticized as being an artefact of various biases in genome databases (Murray, Gao, and Wu 2020), but appears to be a general feature of relatively unbiased metagenome-assembled genomes as well (Olm et al. 2020). The 95% cutoff has been suggested to represent a barrier to homologous recombination (Olm et al. 2020), although clusters of genetic exchange consistent with BSC-like species are observed at much finer identity cutoffs (Shapiro 2019; Arevalo et al. 2019).
Although 95% ANI is the most widely used genomic standard for species delimitation, it is by no means the only plausible approach. In particular, tracts of identical DNA provide evidence for recent genetic exchange, which in turn helps define BSC-like clusters of genomes (Arevalo et al. 2019). In this spirit, Briand et al. (2020) introduce a genome-clustering method based on the number of shared identical DNA sequences of length k (or k-mers). Using a test dataset of Pseudomonas genomes, they find that 95% ANI corresponds to approximately 50% of shared 15-mers. Applying this cutoff yields 350 Pseudomonas species, whereas the current taxonomy only includes 207 recognized species. To determine whether splitting the genus into a greater number of species is at all useful, they compare their new classification scheme to the traditional one in terms of the ability to taxonomically classify metagenomic sequencing reads from three Pseudomonas-rich environments. In all cases, the new scheme (termed K-IS for "Kinship relationships Identification with Shared k-mers") yielded a higher number of classified reads, with an average improvement of 1.4-fold. This is important because increasing the number of genome sequences in a reference database – without consistent taxonomic annotation of these genomes – paradoxically leads to fewer classified metagenomic reads. Thus a rapid, automated taxonomy such as the one proposed here offers an opportunity to more fully harness the information from metagenomes.
KI-S is also fast to run, so it is feasible to test several values of k and quickly visualize the clustering using an interactive, zoomable circle-packing display (that resembles a cross-section of densely packed, three-dimensional dendrogram). This interface allows the rapid flagging of misidentified species, or understudied species with few sequenced representatives as targets for future study. Hopefully these initial Pseudomonas results will inspire future studies to apply the method to additional taxa, and to further characterize the relationship between ANI and shared identical k-mers. Ultimately, I hope that such investigations will resolve the issue of whether or not there is a 'natural' discontinuity for bacterial species, and what evolutionary forces maintain this cutoff.

References

Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF (2019) A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations. Cell, 178, 820-834.e14. https://doi.org/10.1016/j.cell.2019.06.033
 
Briand M, Bouzid M, Hunault G, Legeay M, Saux MF-L, Barret M (2020) A rapid and simple method for assessing and representing genome sequence relatedness. bioRxiv, 569640, ver. 5 peer-reveiwed and recommended by PCI Genomics. https://doi.org/10.1101/569640
 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S (2018) High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9, 5114. https://doi.org/10.1038/s41467-018-07641-9
 
Murray CS, Gao Y, Wu M (2020) There is no evidence of a universal genetic boundary among microbial species. bioRxiv, 2020.07.27.223511. https://doi.org/10.1101/2020.07.27.223511
 
Olm MR, Crits-Christoph A, Diamond S, Lavy A, Carnevali PBM, Banfield JF (2020) Consistent Metagenome-Derived Metrics Verify and Delineate Bacterial Species Boundaries. mSystems, 5. https://doi.org/10.1128/mSystems.00731-19
 
Shapiro BJ (2019) What Microbial Population Genomics Has Taught Us About Speciation. In: Population Genomics: Microorganisms Population Genomics. (eds Polz MF, Rajora OP), pp. 31–47. Springer International Publishing, Cham. https://doi.org/10.1007/13836201810