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22 May 2023
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Genetic bases of resistance to the rice hoja blanca disease deciphered by a QTL approach

Scoring symptoms of a plant viral disease

Recommended by based on reviews by Grégoire Aubert and Valérie Geffroy

The paper from Silva et al. (2023) provides new insights into the genetic bases of natural resistance of rice to the Rice Hoja Blanca (RHB) disease, one of its most serious diseases in tropical countries of the American continent and the Caribbean. This disease is caused by the Rice Hoja Blanca Virus, or RHBV, the vector of which is the planthopper insect Tagosodes orizicolus Müir. It is responsible for serious damage to the rice crop (Morales and Jennings 2010). The authors take a Quantitative Trait Loci (QTL) detection approach to find genomic regions statistically associated with the resistant phenotype. To this aim, they use four resistant x susceptible crosses (the susceptible parent being the same in all four crosses) to maximize the chances to find new QTLs. The F2 populations derived from the crosses are genotyped using Single Nucleotide Polymorphisms (SNPs) extracted from whole-genome sequencing (WGS) data of the resistant parents, and the F3 families derived from the F2 individuals are scored for disease symptoms. For this, they use a computer-aided image analysis protocol that they designed so they can estimate the severity of the damages in the plant. They find several new QTLs, some being apparently more associated with disease severity, others with disease incidence. They also find that a previously identified QTL of Oryza sativa ssp. japonica origin is also present in the indica cluster (Romero et al. 2014). Finally, they discuss the candidate genes that could underlie the QTLs and provide a simple model for resistance.

It has to be noted that scoring symptoms of a viral disease such as RHB is very challenging. It requires maintaining populations of viruliferous insect vectors, mastering times and conditions for infestation by nymphs, and precise symptom scoring. It also requires the preparation of segregating populations, their genotyping with enough genetic markers, and mastering QTL detection methods. All these aspects are present in this work. In particular, the phenotyping of symptom severity implemented using computer-aided image processing represents an impressive, enormous amount of work.

From the genomics side, the fine-scale genotyping is based on the WGS of the parental lines (resistant and susceptible), followed by the application of suitable bioinformatic tools for SNP extraction and primers prediction that can be used on their Fluidigm platform. It also required implementing data correction algorithms to achieve precise genetic maps in the four crosses. The QTL detection itself required careful statistical pre-processing of phenotypic data. The authors then used a combination of several QTL detection methods, including an original meta-QTL method they developed in the software MapDisto. 

The authors then perform a very complete and convincing analysis of candidate genes, which includes genes already identified for a similar disease (RSV) on chromosome 11 of rice. What remains to elucidate is whether the candidate genes are actually involved or not in the disease resistance process. The team has already started implementing gene knockout strategies to study some of them in more detail. It will be interesting to see whether those genes act against the virus itself, or against the insect vector. 

Overall the work is of high quality and represents an important advance in the knowledge of disease resistance. In addition, it has many implications for crop breeding, allowing the setup of large-scale, marker-assisted strategies, for new resistant elite varieties of rice.

References

Morales F and Jennings P (2010) Rice hoja blanca: a complex plant-virus-vector pathosystem. CAB Reviews. https://doi.org/10.1079/PAVSNNR20105043

Romero LE, Lozano I, Garavito A, et al (2014) Major QTLs control resistance to Rice hoja blanca virus and its vector Tagosodes orizicolus. G3 | Genes, Genomes, Genetics 4:133–142. https://doi.org/10.1534/g3.113.009373

Silva A, Montoya ME, Quintero C, Cuasquer J, Tohme J, Graterol E, Cruz M, Lorieux M (2023) Genetic bases of resistance to the rice hoja blanca disease deciphered by a QTL approach. bioRxiv, 2022.11.07.515427, ver. 2 peer-reviewed and recommended by Peer Community in Genomics https://doi.org/10.1101/2022.11.07.515427

Genetic bases of resistance to the rice hoja blanca disease deciphered by a QTL approachAlexander Silva, Maria Elker Montoya, Constanza Quintero, Juan Cuasquer, Joe Tohme, Eduardo Graterol, Maribel Cruz, Mathias Lorieux<p style="text-align: justify;">Rice hoja blanca (RHB) is one of the most serious diseases in rice growing areas in tropical Americas. Its causal agent is Rice hoja blanca virus (RHBV), transmitted by the planthopper <em>Tagosodes orizicolus </em>...Functional genomics, PlantsOlivier Panaud2022-11-09 09:13:30 View
10 Jul 2023
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SNP discovery by exome capture and resequencing in a pea genetic resource collection

The value of a large Pisum SNP dataset

Recommended by based on reviews by Rui Borges and 1 anonymous reviewer

One important goal of modern genetics is to establish functional associations between genotype and phenotype. Single nucleotide polymorphisms (SNPs) are numerous and widely distributed in the genome and can be obtained from nucleic acid sequencing (1). SNPs allow for the investigation of genetic diversity, which is critical for increasing crop resilience to the challenges posed by global climate change. The associations between SNPs and phenotypes can be captured in genome-wide association studies. SNPs can also be used in combination with machine learning, which is becoming more popular for predicting complex phenotypic traits like yield and biotic and abiotic stress tolerance from genotypic data (2). The availability of many SNP datasets is important in machine learning predictions because this approach requires big data to build a comprehensive model of the association between genotype and phenotype.

Aubert and colleagues have studied, as part of the PeaMUST project, the genetic diversity of 240 Pisum accessions (3). They sequenced exome-enriched genomic libraries, a technique that enables the identification of high-density, high-quality SNPs at a low cost (4). This technique involves capturing and sequencing only the exonic regions of the genome, which are the protein-coding regions. A total of 2,285,342 SNPs were obtained in this study. The analysis of these SNPs with the annotations of the genome sequence of one of the studied pea accessions (5) identified a number of SNPs that could have an impact on gene activity. Additional analyses revealed 647,220 SNPs that were unique to individual pea accessions, which might contribute to the fitness and diversity of accessions in different habitats. Phylogenetic and clustering analyses demonstrated that the SNPs could distinguish Pisum germplasms based on their agronomic and evolutionary histories. These results point out the power of selected SNPs as markers for identifying Pisum individuals.

Overall, this study found high-quality SNPs that are meaningful in a biological context. This dataset was derived from a large set of germplasm and is thus particularly useful for studying genotype-phenotype associations, as well as the diversity within Pisum species. These SNPs could also be used in breeding programs to develop new pea varieties that are resilient to abiotic and biotic stressors.  

References


1.         Fallah M, Jean M, Boucher St-Amour VT, O’Donoughue L, Belzile F. The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing. Genome. 2022 Aug;65(8):413–25.

https://doi.org/10.1139/gen-2021-005


2.         Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, Valliyodan B, et al. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC Plant Biology. 2022 Apr 8;22(1):180.

https://doi.org/10.1186/s12870-022-03559-z


3.         Aubert G, Kreplak J, Leveugle M, Duborjal H, Klein A, Boucherot K, et al. SNP discovery by exome capture and resequencing in a pea genetic resource collection., biorxiv, ver. 4, peer-reviewed and recommended by Peer Community in Genomics.

https://doi.org/10.1101/2022.08.03.502586 


4.         Warr A, Robert C, Hume D, Archibald A, Deeb N, Watson M. Exome sequencing: current and future perspectives. G3 Genes|Genomes|Genetics. 2015 Aug 1;5(8):1543–50.

https://doi.org/10.1534/g3.115.018564


5.         Kreplak J, Madoui MA, Cápal P, Novák P, Labadie K, Aubert G, et al. A reference genome for pea provides insight into legume genome evolution. Nat Genet. 2019 Sep;51(9):1411–22.

https://doi.org/10.1038/s41588-019-0480-1

SNP discovery by exome capture and resequencing in a pea genetic resource collectionG. Aubert, J. Kreplak, M. Leveugle, H. Duborjal, A. Klein, K. Boucherot, E. Vieille, M. Chabert-Martinello, C. Cruaud, V. Bourion, I. Lejeune-Hénaut, M.L. Pilet-Nayel, Y. Bouchenak-Khelladi, N. Francillonne, N. Tayeh, J.P. Pichon, N. Rivière, J. B...<p style="text-align: justify;"><strong>Background &amp; Summary</strong></p> <p style="text-align: justify;">In addition to being the model plant used by Mendel to establish genetic laws, pea (<em>Pisum sativum</em> L., 2n=14) is a major pulse c...Plants, Population genomicsWanapinun Nawae2022-11-29 09:29:06 View
25 Nov 2022
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Phenotypic and transcriptomic analyses reveal major differences between apple and pear scab nonhost resistance

Apples and pears: two closely related species with differences in scab nonhost resistance

Recommended by based on reviews by 3 anonymous reviewers

Nonhost resistance is a common form of disease resistance exhibited by plants against microorganisms that are pathogenic to other plant species [1]. Apples and pears are two closely related species belonging to Rosaceae family, both affected by scab disease caused by fungal pathogens in the Venturia genus. These pathogens appear to be highly host-specific. While apples are nonhosts for Venturia pyrina, pears are nonhosts for Venturia inaequalis. To date, the molecular bases of scab nonhost resistance in apple and pear have not been elucidated.

This preprint by Vergne, et al (2022) [2] analyzed nonhost resistance symptoms in apple/V. pyrina and pear/V. inaequalis interactions as well as their transcriptomic responses. Interestingly, the author demonstrated that the nonhost apple/V. pyrina interaction was almost symptomless while hypersensitive reactions were observed for pear/V. inaequalis interaction. The transcriptomic analyses also revealed a number of differentially expressed genes (DEGs) that corresponded to the severity of the interactions, with very few DEGs observed during the apple/V. pyrina interaction and a much higher number of DEGs during the pear/V. inaequalis interaction.

This type of reciprocal host-pathogen interaction study is valuable in gaining new insights into how plants interact with microorganisms that are potential pathogens in related species. A few processes appeared to be involved in the pear resistance against the nonhost pathogen V. inaequalis at the transcriptomic level, such as stomata closure, modification of cell wall and production of secondary metabolites as well as phenylpropanoids. Based on the transcriptomics changes during the nonhost interaction, the author compared the responses to those of host-pathogen interactions and revealed some interesting findings. They proposed a series of cascading effects in pear induced by the presence of V. inaequalis, which I believe helps shed some light on the basic mechanism for nonhost resistance.

I am recommending this study because it provides valuable information that will strengthen our understanding of nonhost resistance in the Rosaceae family and other plant species. The knowledge gained here may be applied to genetically engineer plants for a broader resistance against a number of pathogens in the future.​

References

1. Senthil-Kumar M, Mysore KS (2013) Nonhost Resistance Against Bacterial Pathogens: Retrospectives and Prospects. Annual Review of Phytopathology, 51, 407–427. https://doi.org/10.1146/annurev-phyto-082712-102319

2. Vergne E, Chevreau E, Ravon E, Gaillard S, Pelletier S, Bahut M, Perchepied L (2022) Phenotypic and transcriptomic analyses reveal major differences between apple and pear scab nonhost resistance. bioRxiv, 2021.06.01.446506, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.06.01.446506

Phenotypic and transcriptomic analyses reveal major differences between apple and pear scab nonhost resistanceE. Vergne, E. Chevreau, E. Ravon, S. Gaillard, S. Pelletier, M. Bahut, L. Perchepied<p style="text-align: justify;"><strong>Background. </strong>Nonhost resistance is the outcome of most plant/pathogen interactions, but it has rarely been described in Rosaceous fruit species. Apple (<em>Malus x domestica</em> Borkh.) have a nonho...Functional genomics, PlantsWirulda Pootakham Jessica Soyer, Anonymous2022-05-13 15:06:08 View
20 Nov 2023
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Building a Portuguese Coalition for Biodiversity Genomics

The Portuguese genomics community teams up with iconic species to understand the destruction of biodiversity

Recommended by based on reviews by Svein-Ole Mikalsen and 1 anonymous reviewer

This manuscript describes the ongoing work and plans of Biogenome Portugal: a new network of researchers in the Portuguese biodiversity genomics community. The aims of this network are to jointly train scientists in ecology and evolution, generate new knowledge and understanding of Portuguese biodiversity, and better engage with the public and with international researchers, so as to advance conservation efforts in the region. In collaboration across disciplines and institutions, they are also contributing to the European Reference Genome Atlas (ERGA): a massive scientific effort, seeking to eventually produce reference-quality genomes for all species in the European continent (Mc Cartney et al. 2023).

The manuscript centers around six iconic and/or severely threatened species, whose range extends across parts of what is today considered Portuguese territory. Via the Portugal chapter of ERGA (ERGA-Portugal), the researchers will generate high-quality genome sequences from these species. The species are the Iberian hare, the Azores laurel, the Black wheatear, the Portuguese crowberry, the Cave ground beetle and the Iberian minnowcarp. In ignorance of human-made political borders, some of these species also occupy large parts of the rest of the Iberian peninsula, highlighting the importance of transnational collaboration in biodiversity efforts. The researchers extracted samples from members of each of these species, and are building reference genome sequences from them. In some cases, these sequences will also be co-analyzed with additional population genomic data from the same species or genetic data from cohabiting species. The researchers aim to answer a variety of ecological and evolutionary questions using this information, including how genetic diversity is being affected by the destruction of their habitat, and how they are being forced to adapt as a consequence of the climate emergency.

The authors did a very good job in providing a justification for the choice of pilot species, a thorough methodological overview of current work, and well thought-out plans for future analyses once the genome sequences are available for study. The authors also describe plans for networking and training activities to foster a well-connected Portuguese biodiversity genomics community.

Applying a genomic analysis lens is important for understanding the ever faster process of devastation of our natural world. Governments and corporations around the globe are destroying nature at ever larger scales (Diaz et al. 2019). They are also destabilizing the climatic conditions on which life has existed for thousands of years (Trisos et al. 2020). Thus, genetic diversity is decreasing faster than ever in human history, even when it comes to non-threatened species (Exposito-Alonso et al. 2022), and these decreases are disrupting ecological processes worldwide (Richardson et al. 2023). This, in turn, is threatening the conditions on which the stability of our societies rest (Gardner and Bullock 2021). The efforts of Biogenome Portal and ERGA-Portugal will go a long way in helping us understand in greater detail how this process is unfolding in Portuguese territories.

 

 

References

Díaz, Sandra, et al. "Pervasive human-driven decline of life on Earth points to the need for transformative change." Science 366.6471 (2019): eaax3100. https://doi.org/10.1126/science.aax3100

Exposito-Alonso, Moises, et al. "Genetic diversity loss in the Anthropocene." Science 377.6613 (2022): 1431-1435. https://doi.org/10.1126/science.abn5642

Gardner, Charlie J., and James M. Bullock. "In the climate emergency, conservation must become survival ecology." Frontiers in Conservation Science 2 (2021): 659912. https://doi.org/10.3389/fcosc.2021.659912

Mc Cartney, Ann M., et al. "The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics." bioRxiv (2023): 2023-09, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.32942/X20W3Q

Richardson, Katherine, et al. "Earth beyond six of nine planetary boundaries." Science Advances 9.37 (2023): eadh2458. https://doi.org/10.1126/sciadv.adh2458

Trisos, Christopher H., Cory Merow, and Alex L. Pigot. "The projected timing of abrupt ecological disruption from climate change." Nature 580.7804 (2020): 496-501. https://doi.org/10.1038/s41586-020-2189-9

Building a Portuguese Coalition for Biodiversity GenomicsJoão Pedro Marques, Paulo Célio Alves, Isabel R. Amorim, Ricardo J. Lopes, Mónica Moura, Gene Meyers, Manuela Sim-Sim, Carla Sousa-Santos, Maria Judite Alves, Paulo AV Borges, Thomas Brown, Miguel Carneiro, Carlos Carrapato, Luís Ceríaco, Claudio ...<p style="text-align: justify;">The diverse physiography of the Portuguese land and marine territory, spanning from continental Europe to the Atlantic archipelagos, has made it an important repository of biodiversity throughout the Pleistocene gla...ERGA, ERGA PilotFernando Racimo2023-07-14 11:24:22 View
20 Jul 2021
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Genetic mapping of sex and self-incompatibility determinants in the androdioecious plant Phillyrea angustifolia

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

Recommended by and based on reviews by 2 anonymous reviewers

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

Genetic mapping of sex and self-incompatibility determinants in the androdioecious plant Phillyrea angustifoliaAmelie Carre, Sophie Gallina, Sylvain Santoni, Philippe Vernet, Cecile Gode, Vincent Castric, Pierre Saumitou-Laprade<p style="text-align: justify;">The diversity of mating and sexual systems in angiosperms is spectacular, but the factors driving their evolution remain poorly understood. In plants of the Oleaceae family, an unusual self-incompatibility (SI) syst...Evolutionary genomics, PlantsTatiana Giraud2021-05-04 10:37:26 View
22 Nov 2023
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The slow evolving genome of the xenacoelomorph worm Xenoturbella bocki

Genomic idiosyncrasies of Xenoturbella bocki: morphologically simple yet genetically complex

Recommended by ORCID_LOGO based on reviews by Christopher Laumer and 1 anonymous reviewer

Xenoturbella is a genus of morphologically simple bilaterians inhabiting benthic environments. Until very recently, only one species was known from the genus, Xenoturbella bocki Westblad 1949 [1]. Less than a decade ago, five more species were discovered (X. churro, X. monstrosa, X. profunda, X. hollandorum [2] and X. japonica [3]). These enigmatic animals lack an anus, a coelom, reproductive organs, nephrocytes and a centralized nervous system [1]. The systematic classification of the genus has substantially changed in the last decades, with first being considered as its own phylum (Xenoturbellida) and then being clustered together with acoels and nemertodermatids into the phylum Xenacoelomorpha [4,5]. The phylogenetic position of the xenacoelomorphs has been recalcitrant to resolution, with its position ranging from being the sister group to Nephrozoa (ie, protostomes and deuterostomes [6]) to the sister group to Ambulacraria (ie, Hemichordata and Echinodermata) in a clade called Xenambulacraria [4]. Recent studies based on expanded datasets and more refined analyses support either topology [7,8]. Either way, it is clear that additional studies on Xenoturbella could provide important insights into the origins of bilaterian traits such as the anus, the nephrons and the evolution of a centralized nervous system. 


Small but mighty genome - In this work [9], the authors present the chromosome-level genome of X. bocki - the first one for xenoturbellids - and explore their genomic idiosyncrasies in the context of other animal phyla. The first thing they discuss is the complexity of the genome, with X. bocki having a similar number of genes to other bilaterians (despite its small size of 111Mb), retained ancestral metazoan synteny, conserved clusters of Hox genes, largely complete signaling pathways and most bilaterian miRNAs present. This is not a surprise, though, as we know that the relationship between genomic and morphological complexity is far from straightforward - for instance, protist lineages closely related to animals share many gene families with us [10], and it is not the presence or absence of these gene families but their evolutionary dynamics what defines complexity in each animal phyla (eg [11]). However, the relationship between both is far from well-understood, and having a high-quality genome is the first crucial step towards a holistic understanding of genome evolution, allowing us to ask questions about how and when genes are regulated, how they interact in 3D space, or how their epigenetic landscape is shaped, for instance.


Xenacoelomorphs: deuterostomes or not? - The authors also discuss the phylogenetic position of xenacoelomorphs (including the newly generated high-quality genome of X. bocki) based on a gene presence/absence matrix. Although there is much more to be done to robustly assess the phylogenetic position of the phylum, these analyses represent a first attempt to investigate what the phylogeny looks like after the addition of the new high-quality data. The new analyses reflected once more the previously recovered phylogenies mentioned above, but this time with a twist: X. bocki was recovered as the sister group to echinoderms, yet acoels appeared as sister to all deuterostomes, hence not recovering Xenacoelomorpha as monophyletic. Thus, it is clear that much remains to be explored to disentangle the phylogenetic position of these mysterious lineages, where more sophisticated methodologies such as synteny-based orthology inference or models of evolution accounting for heterotachy probably have an important role to play. 

In any case, we are approaching a qualitative jump in how we understand phylogenomics thanks to efforts derived from the availability of chromosome-level genome assemblies for a growing number of species. Exciting times are ahead for us, evolutionary biologists, to explore what high-quality genomes - in combination with multiomics datasets - will reveal about animal evolution. I am personally really looking forward to it.  

References

1. Westblad E. (1949). Xenoturbella bocki n.g., n.sp., a peculiar, primitive Turbellarian type. Arkiv för Zoologi 1, 3-29 (1949).

2. Rouse, G. W., Wilson, N. G., Carvajal, J. I. & Vrijenhoek, R. C. New deep-sea species of Xenoturbella and the position of Xenacoelomorpha. Nature 530, 94–97 (2016). https://doi.org/10.1038/nature16545

3. Nakano, H. et al. Correction to: A new species of Xenoturbella from the western Pacific Ocean and the evolution of Xenoturbella. BMC Evol. Biol. 18, 1–2 (2018). https://doi.org/10.1186/s12862-018-1190-5​https://doi.org/10.1186/s12862-018-1190-5

4. Philippe, H. et al. Acoelomorph flatworms are deuterostomes related to Xenoturbella. Nature 470, 255–258 (2011). https://doi.org/10.1038/nature09676

5. Hejnol, A. et al. Assessing the root of bilaterian animals with scalable phylogenomic methods. Proc. Biol. Sci. 276, 4261–4270 (2009). https://doi.org/10.1098/rspb.2009.0896

6. Cannon, J. T. et al. Xenacoelomorpha is the sister group to Nephrozoa. Nature 530, 89–93 (2016). https://doi.org/10.1038/nature16520

7. Laumer, C. E. et al. Revisiting metazoan phylogeny with genomic sampling of all phyla. Proc. Biol. Sci. 286, 20190831 (2019). https://doi.org/10.1098/rspb.2019.0831

8. Philippe, H. et al. Mitigating anticipated effects of systematic errors supports sister-group relationship between Xenacoelomorpha and Ambulacraria. Curr. Biol. 29, 1818–1826.e6 (2019). https://doi.org/10.1016/j.cub.2019.04.009

9. Schiffer, P. H., Natsidis, P., Leite D. J., Robertson, H., Lapraz, F., Marlétaz, F., Fromm, B., Baudry, L., Simpson, F., Høye, E., Zakrzewski, A-C., Kapli, P., Hoff, K. J., Mueller, S., Marbouty, M., Marlow, H., Copley, R. R., Koszul, R., Sarkies, P. & Telford, M .J. The slow evolving genome of the xenacoelomorph worm Xenoturbella bocki. bioRxiv (2023), ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.06.24.497508

10. Suga, H. et al. The Capsaspora genome reveals a complex unicellular prehistory of animals. Nat. Commun. 4, 2325 (2013). https://doi.org/10.1038/ncomms3325

11. Fernández, R. & Gabaldón, T. Gene gain and loss across the metazoan tree of life. Nat Ecol Evol 4, 524–533 (2020). https://doi.org/10.1038/s41559-019-1069-x

The slow evolving genome of the xenacoelomorph worm *Xenoturbella bocki*Philipp H. Schiffer, Paschalis Natsidis, Daniel J. Leite, Helen Robertson, François Lapraz, Ferdinand Marlétaz, Bastian Fromm, Liam Baudry, Fraser Simpson, Eirik Høye, Anne-C. Zakrzewski, Paschalia Kapli, Katharina J. Hoff, Steven Mueller, Martial...<p style="text-align: justify;">The evolutionary origins of Bilateria remain enigmatic. One of the more enduring proposals highlights similarities between a cnidarian-like planula larva and simple acoel-like flatworms. This idea is based in part o...Evolutionary genomicsRosa Fernandez2022-11-01 12:31:53 View
15 Dec 2022
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Botrytis cinerea strains infecting grapevine and tomato display contrasted repertoires of accessory chromosomes, transposons and small RNAs

Exploring genomic determinants of host specialization in Botrytis cinerea

Recommended by based on reviews by Cecile Lorrain and Thorsten Langner

The genomics era has pushed forward our understanding of fungal biology. Much progress has been made in unraveling new gene functions and pathways, as well as the evolution or adaptation of fungi to their hosts or environments through population studies (Hartmann et al. 2019; Gladieux et al. 2018). Closing gaps more systematically in draft genomes using the most recent long-read technologies now seems the new standard, even with fungal species presenting complex genome structures (e.g. large and highly repetitive dikaryotic genomes; Duan et al. 2022). Understanding the genomic dynamics underlying host specialization in phytopathogenic fungi is of utmost importance as it may open new avenues to combat diseases. A strong host specialization is commonly observed for biotrophic and hemi-biotrophic fungal species or for necrotrophic fungi with a narrow host range, whereas necrotrophic fungi with broad host range are considered generalists (Liang and Rollins, 2018; Newman and Derbyshire, 2020). However, some degrees of specialization towards given hosts have been reported in generalist fungi and the underlying mechanisms remain to be determined.

Botrytis cinerea is a polyphagous necrotrophic phytopathogen with a particularly wide host range and it is notably responsible for grey mould disease on many fruits, such as tomato and grapevine. Because of its importance as a plant pathogen, its relatively small genome size and its taxonomical position, it has been targeted for early genome sequencing and a first reference genome was provided in 2011 (Amselem et al. 2011). Other genomes were subsequently sequenced for other strains, and most importantly a gapless assembled version of the initial reference genome B05.10 was provided to the community (van Kan et al. 2017). This genomic resource has supported advances in various aspects of the biology of B. cinerea such as the production of specialized metabolites, which plays an important role in host-plant colonization, or more recently in the production of small RNAs which interfere with the host immune system, representing a new class of non-proteinaceous virulence effectors (Dalmais et al. 2011; Weiberg et al. 2013).

In the present study, Simon et al. (2022) use PacBio long-read sequencing for Sl3 and Vv3 strains, which represent genetic clusters in B. cinerea populations found on tomato and grapevine. The authors combined these complete and high-quality genome assemblies with the B05.10 reference genome and population sequencing data to perform a comparative genomic analysis of specialization towards the two host plants. Transposable elements generate genomic diversity due to their mobile and repetitive nature and they are of utmost importance in the evolution of fungi as they deeply reshape the genomic landscape (Lorrain et al. 2021). Accessory chromosomes are also known drivers of adaptation in fungi (Möller and Stukenbrock, 2017). Here, the authors identify several genomic features such as the presence of different sets of accessory chromosomes, the presence of differentiated repertoires of transposable elements, as well as related small RNAs in the tomato and grapevine populations, all of which may be involved in host specialization. Whereas core chromosomes are highly syntenic between strains, an accessory chromosome validated by pulse-field electrophoresis is specific of the strains isolated from grapevine. Particularly, they show that two particular retrotransposons are discriminant between the strains and that they allow the production of small RNAs that may act as effectors. The discriminant accessory chromosome of the Vv3 strain harbors one of the unraveled retrotransposons as well as new genes of yet unidentified function.

I recommend this article because it perfectly illustrates how efforts put into generating reference genomic sequences of higher quality can lead to new discoveries and allow to build strong hypotheses about biology and evolution in fungi. Also, the study combines an up-to-date genomics approach with a classical methodology such as pulse-field electrophoresis to validate the presence of accessory chromosomes. A major input of this investigation of the genomic determinants of B. cinerea is that it provides solid hints for further analysis of host-specialization at the population level in a broad-scale phytopathogenic fungus.

References

Amselem J, Cuomo CA, Kan JAL van, Viaud M, Benito EP, Couloux A, Coutinho PM, Vries RP de, Dyer PS, Fillinger S, Fournier E, Gout L, Hahn M, Kohn L, Lapalu N, Plummer KM, Pradier J-M, Quévillon E, Sharon A, Simon A, Have A ten, Tudzynski B, Tudzynski P, Wincker P, Andrew M, Anthouard V, Beever RE, Beffa R, Benoit I, Bouzid O, Brault B, Chen Z, Choquer M, Collémare J, Cotton P, Danchin EG, Silva CD, Gautier A, Giraud C, Giraud T, Gonzalez C, Grossetete S, Güldener U, Henrissat B, Howlett BJ, Kodira C, Kretschmer M, Lappartient A, Leroch M, Levis C, Mauceli E, Neuvéglise C, Oeser B, Pearson M, Poulain J, Poussereau N, Quesneville H, Rascle C, Schumacher J, Ségurens B, Sexton A, Silva E, Sirven C, Soanes DM, Talbot NJ, Templeton M, Yandava C, Yarden O, Zeng Q, Rollins JA, Lebrun M-H, Dickman M (2011) Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorum and Botrytis cinerea. PLOS Genetics, 7, e1002230. https://doi.org/10.1371/journal.pgen.1002230

Dalmais B, Schumacher J, Moraga J, Le Pêcheur P, Tudzynski B, Collado IG, Viaud M (2011) The Botrytis cinerea phytotoxin botcinic acid requires two polyketide synthases for production and has a redundant role in virulence with botrydial. Molecular Plant Pathology, 12, 564–579. https://doi.org/10.1111/j.1364-3703.2010.00692.x

Duan H, Jones AW, Hewitt T, Mackenzie A, Hu Y, Sharp A, Lewis D, Mago R, Upadhyaya NM, Rathjen JP, Stone EA, Schwessinger B, Figueroa M, Dodds PN, Periyannan S, Sperschneider J (2022) Physical separation of haplotypes in dikaryons allows benchmarking of phasing accuracy in Nanopore and HiFi assemblies with Hi-C data. Genome Biology, 23, 84. https://doi.org/10.1186/s13059-022-02658-2

Gladieux P, Condon B, Ravel S, Soanes D, Maciel JLN, Nhani A, Chen L, Terauchi R, Lebrun M-H, Tharreau D, Mitchell T, Pedley KF, Valent B, Talbot NJ, Farman M, Fournier E (2018) Gene Flow between Divergent Cereal- and Grass-Specific Lineages of the Rice Blast Fungus Magnaporthe oryzae. mBio, 9, e01219-17. https://doi.org/10.1128/mBio.01219-17

Hartmann FE, Rodríguez de la Vega RC, Carpentier F, Gladieux P, Cornille A, Hood ME, Giraud T (2019) Understanding Adaptation, Coevolution, Host Specialization, and Mating System in Castrating Anther-Smut Fungi by Combining Population and Comparative Genomics. Annual Review of Phytopathology, 57, 431–457. https://doi.org/10.1146/annurev-phyto-082718-095947

Liang X, Rollins JA (2018) Mechanisms of Broad Host Range Necrotrophic Pathogenesis in Sclerotinia sclerotiorum. Phytopathology®, 108, 1128–1140. https://doi.org/10.1094/PHYTO-06-18-0197-RVW

Lorrain C, Oggenfuss U, Croll D, Duplessis S, Stukenbrock E (2021) Transposable Elements in Fungi: Coevolution With the Host Genome Shapes, Genome Architecture, Plasticity and Adaptation. In: Encyclopedia of Mycology (eds Zaragoza Ó, Casadevall A), pp. 142–155. Elsevier, Oxford. https://doi.org/10.1016/B978-0-12-819990-9.00042-1

Möller M, Stukenbrock EH (2017) Evolution and genome architecture in fungal plant pathogens. Nature Reviews Microbiology, 15, 756–771. https://doi.org/10.1038/nrmicro.2017.76

Newman TE, Derbyshire MC (2020) The Evolutionary and Molecular Features of Broad Host-Range Necrotrophy in Plant Pathogenic Fungi. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.591733

Simon A, Mercier A, Gladieux P, Poinssot B, Walker A-S, Viaud M (2022) Botrytis cinerea strains infecting grapevine and tomato display contrasted repertoires of accessory chromosomes, transposons and small RNAs. bioRxiv, 2022.03.07.483234, ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.03.07.483234

Van Kan JAL, Stassen JHM, Mosbach A, Van Der Lee TAJ, Faino L, Farmer AD, Papasotiriou DG, Zhou S, Seidl MF, Cottam E, Edel D, Hahn M, Schwartz DC, Dietrich RA, Widdison S, Scalliet G (2017) A gapless genome sequence of the fungus Botrytis cinerea. Molecular Plant Pathology, 18, 75–89. https://doi.org/10.1111/mpp.12384

Weiberg A, Wang M, Lin F-M, Zhao H, Zhang Z, Kaloshian I, Huang H-D, Jin H (2013) Fungal Small RNAs Suppress Plant Immunity by Hijacking Host RNA Interference Pathways. Science, 342, 118–123. https://doi.org/10.1126/science.1239705

Botrytis cinerea strains infecting grapevine and tomato display contrasted repertoires of accessory chromosomes, transposons and small RNAsAdeline Simon, Alex Mercier, Pierre Gladieux, Benoit Poinssot, Anne-Sophie Walker, Muriel Viaud<p style="text-align: justify;">The fungus <em>Botrytis cinerea</em> is a polyphagous pathogen that encompasses multiple host-specialized lineages. While several secreted proteins, secondary metabolites and retrotransposons-derived small RNAs have...Fungi, Structural genomics, Viruses and transposable elementsSebastien Duplessis Cecile Lorrain, Thorsten Langner2022-03-15 11:15:48 View
05 May 2021
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A primer and discussion on DNA-based microbiome data and related bioinformatics analyses

A hitchhiker’s guide to DNA-based microbiome analysis

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

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

A primer and discussion on DNA-based microbiome data and related bioinformatics analysesGavin M. Douglas and Morgan G. I. Langille<p style="text-align: justify;">The past decade has seen an eruption of interest in profiling microbiomes through DNA sequencing. The resulting investigations have revealed myriad insights and attracted an influx of researchers to the research are...Bioinformatics, MetagenomicsDanny Ionescu2021-02-17 00:26:46 View
07 Aug 2023
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Genomic data suggest parallel dental vestigialization within the xenarthran radiation

What does dental gene decay tell us about the regressive evolution of teeth in South American mammals?

Recommended by based on reviews by Juan C. Opazo, Régis Debruyne and Nicolas Pollet

A group of mammals, Xenathra, evolved and diversified in South America during its long period of isolation in the early to mid Cenozoic era. More recently, as a result of the Great Faunal Interchange between South America and North America, many xenarthran species went extinct. The thirty-one extant species belong to three groups: armadillos, sloths and anteaters. They share dental degeneration. However, the level of degeneration is variable. Anteaters entirely lack teeth, sloths have intermediately regressed teeth and most armadillos have a toothless premaxilla, as well as peg-like, single-rooted teeth that lack enamel in adult animals (Vizcaíno 2009). This diversity raises a number of questions about the evolution of dentition in these mammals. Unfortunately, the fossil record is too poor to provide refined information on the different stages of regressive evolution in these clades. In such cases, the identification of loss-of-function mutations and/or relaxed selection in genes related to a character regression can be very informative (Emerling and Springer 2014; Meredith et al. 2014; Policarpo et al. 2021). Indeed, shared and unique pseudogenes/relaxed selection can tell us to what extent regression has occurred in common ancestors and whether some changes are lineage-specific. In addition, the distribution of pseudogenes/relaxed selection on the branches of a phylogenetic tree is related to the evolutionary processes involved. A much higher density of pseudogenes in the most internal branches indicates that degeneration took place early and over a short period of time, consistent with selection against the presence of the morphological character with which they are associated, while pseudogenes distributed evenly in many internal and external branches suggest a more gradual process over many millions of years, in line with relaxed selection and fixation of loss-of-function mutations by genetic drift.

In this paper (Emerling et al. 2023), the authors examined the dynamics of decay of 11 dental genes that may parallel teeth regression. The analyses of the data reported in this paper clearly point to xenarthran teeth having repeatedly regressed in parallel in the three clades. In fact, no loss-of-function mutation is shared by all species examined. However, more genes should be studied to confirm the hypothesis that the common ancestor of extant xenarthrans had normal dentition. There are distinct patterns of gene loss in different lineages that are associated with the variation in dentition observed across the clades. These patterns of gene loss suggest that regressive evolution took place both gradually and in relatively rapid, discrete phases during the diversification of xenarthrans. This study underscores the utility of using pseudogenes to reconstruct evolutionary history of morphological characters when fossils are sparse.

References

Emerling CA, Gibb GC, Tilak M-K, Hughes JJ, Kuch M, Duggan AT, Poinar HN, Nachman MW, Delsuc F. 2023. Genomic data suggest parallel dental vestigialization within the xenarthran radiation. bioRxiv, 2022.12.09.519446, ver 2, peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2022.12.09.519446

Emerling CA, Springer MS. 2014. Eyes underground: Regression of visual protein networks in subterranean mammals. Molecular Phylogenetics and Evolution 78: 260-270. https://doi.org/10.1016/j.ympev.2014.05.016

Meredith RW, Zhang G, Gilbert MTP, Jarvis ED, Springer MS. 2014. Evidence for a single loss of mineralized teeth in the common avian ancestor. Science 346: 1254390. https://doi.org/10.1126/science.1254390

Policarpo M, Fumey J, Lafargeas P, Naquin D, Thermes C, Naville M, Dechaud C, Volff J-N, Cabau C, Klopp C, et al. 2021. Contrasting gene decay in subterranean vertebrates: insights from cavefishes and fossorial mammals. Molecular Biology and Evolution 38: 589-605. https://doi.org/10.1093/molbev/msaa249

Vizcaíno SF. 2009. The teeth of the “toothless”: novelties and key innovations in the evolution of xenarthrans (Mammalia, Xenarthra). Paleobiology 35: 343-366. https://doi.org/10.1666/0094-8373-35.3.343

Genomic data suggest parallel dental vestigialization within the xenarthran radiationChristopher A Emerling, Gillian C Gibb, Marie-Ka Tilak, Jonathan J Hughes, Melanie Kuch, Ana T Duggan, Hendrik N Poinar, Michael W Nachman, Frederic Delsuc<p style="text-align: justify;">The recent influx of genomic data has provided greater insights into the molecular basis for regressive evolution, or vestigialization, through gene loss and pseudogenization. As such, the analysis of gene degradati...Evolutionary genomics, VertebratesDidier Casane2022-12-12 16:01:57 View
19 Jul 2021
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TransPi - a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly

TransPI: A balancing act between transcriptome assemblers

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

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

TransPi - a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assemblyRamon E Rivera-Vicens, Catalina Garcia-Escudero, Nicola Conci, Michael Eitel, Gert Wörheide<p style="text-align: justify;">The use of RNA-Seq data and the generation of de novo transcriptome assemblies have been pivotal for studies in ecology and evolution. This is distinctly true for non-model organisms, where no genome information is ...Bioinformatics, Evolutionary genomicsOleg Simakov2021-02-18 20:56:08 View