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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

A 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

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

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