The transition of cetaceans (whales, dolphins, and porpoises) from terrestrial to aquatic lifestyles is a striking example of natural selection driving major phenotypic changes (Figure 1). For instance, cetaceans have evolved the ability to withstand high pressure and to store oxygen for long periods, among other adaptations (Das et al. 2023). Many phenotypic changes, such as shifts in organ structure, have been well-characterized through fossils (Thewissen et al. 2009). Although such phenotypic transitions are now well understood, we have only a partial understanding of the underlying genetic mechanisms. Scanning for signatures of adaptation in genes related to phenotypes of interest is one approach to better understand these mechanisms. This was the focus of Uribe and colleagues’ (2024) work, who tested for such signatures across cetacean protein-coding genes.

Figure 1: The skeletons of Ambulocetus (an early whale; top) and Pakicetus (the earliest known cetacean, which lived about 50 million years ago; bottom). Copyright: J. G. M. Thewissen. Displayed here with permission from the copyright holder.

The authors were specifically interested in investigating the evolution of ion channels, as these proteins play fundamental roles in physiological processes. An important aspect of their work was to develop a bioinformatic pipeline to identify orthologous ion channel genes across a set of genomes. After applying their bioinformatic workflow to 18 mammalian species (including nine cetaceans), they conducted tests to find out whether these genes showed signatures of positive selection in the cetacean lineage. For many ion channel genes, elevated ratios of non-synonymous to synonymous substitution rates were detected (for at least a subset of sites, and not necessarily the entire coding region of the genes). The genes concerned were enriched for several functions, including heart and nervous system-related phenotypes.

One top gene hit among the putatively selected genes was SCN5A, which encodes a sodium channel expressed in the heart. Interestingly, the authors noted a specific amino acid replacement, which is associated with sensitivity to the toxin tetrodotoxin in other lineages. This substitution appears to have occurred in the common ancestor of toothed whales, and then was reversed in the ancestor of bottlenose dolphins. The authors describe known bottlenose dolphin interactions with toxin-producing pufferfish that could result in high tetrodotoxin exposure, and thus perhaps higher selection for tetrodotoxin resistance. Although this observation is intriguing, the authors emphasize it requires experimental confirmation.

The authors also recapitulated the previously described observation (Yim et al. 2014; Huelsmann et al. 2019) that cetaceans have fewer protein-coding genes compared to terrestrial mammals, on average. This signal has previously been hypothesized to partially reflect adaptive gene loss. For example, specific gene loss events likely decreased the risk of developing blood clots while diving (Huelsmann et al. 2019). Uribe and colleagues also considered overall gene turnover rate, which encompasses gene copy number variation across lineages, and found the cetacean gene turnover rate to be three times higher than that of terrestrial mammals. Finally, they found that cetaceans have a higher proportion of ion channel genes (relative to all protein-coding genes in a genome) compared to terrestrial mammals. 

Similar investigations of the relative non-synonymous to synonymous substitution rates across cetacean and terrestrial mammal orthologs have been conducted previously, but these have primarily focused on dolphins as the sole cetacean representative (McGowen et al. 2012; Nery et al. 2013; Sun et al. 2013). These projects have also been conducted across a large proportion of orthologous genes, rather than a subset with a particular function. Performing proteome-wide investigations can be valuable in that they summarize the genome-wide signal, but can suffer from a high multiple testing burden. More generally, investigating a more targeted question, such as the extent of positive selection acting on ion channels in this case, or on genes potentially linked to cetaceans’ increased brain sizes (McGowen et al. 2011) or hypoxia tolerance (Tian et al. 2016), can be easier to interpret, as opposed to summarizing broader signals. However, these smaller-scale studies can also experience a high multiple testing burden, especially as similar tests are conducted across numerous studies, which often is not accounted for (Ioannidis 2005). In addition, integrating signals across the entire genome will ultimately be needed given that many genetic changes undoubtedly underlie cetaceans’ phenotypic diversification. As highlighted by the fact that past genome-wide analyses have produced some differing biological interpretations (McGowen et al. 2012; Nery et al. 2013; Sun et al. 2013), this is not a trivial undertaking. 

Nonetheless, the work performed in this preprint, and in related research, is valuable for (at least) three reasons. First, although it is a challenging task, a better understanding of the genetic basis of cetacean phenotypes could have benefits for many aspects of cetacean biology, including conservation efforts. In addition, the remarkable phenotypic shifts in cetaceans make the question of what genetic mechanisms underlie these changes intrinsically interesting to a wide audience. Last, since the cetacean fossil record is especially well-documented (Thewissen et al. 2009), cetaceans represent an appealing system to validate and further develop statistical methods for inferring adaptation from genetic data. Uribe and colleagues’ (2024) analyses provide useful insights relevant to each of these points, and have generated intriguing hypotheses for further investigation.

References

Das K, Sköld H, Lorenz A, Parmentier E (2023) Who are the marine mammals? In: “Marine Mammals: A Deep Dive into the World of Science”. Brennecke D, Knickmeier K, Pawliczka I, Siebert U, Wahlberg, M (editors). Springer, Cham. p. 1–14. https://doi.org/10.1007/978-3-031-06836-2_1

Huelsmann M, Hecker N, Springer MS, Gatesy J, Sharma V, Hiller M (2019) Genes lost during the transition from land to water in cetaceans highlight genomic changes associated with aquatic adaptations. Science Advances, 5, eaaw6671. https://doi.org/10.1126/sciadv.aaw6671

Ioannidis JPA (2005) Why most published research findings are false. PLOS Medicine, 2, e124. https://doi.org/10.1371/journal.pmed.0020124

McGowen MR, Montgomery SH, Clark C, Gatesy J (2011) Phylogeny and adaptive evolution of the brain-development gene microcephalin (MCPH1) in cetaceans. BMC Evolutionary Biology, 11, 98. https://doi.org/10.1186/1471-2148-11-98

McGowen MR, Grossman LI, Wildman DE (2012) Dolphin genome provides evidence for adaptive evolution of nervous system genes and a molecular rate slowdown. Proceedings of the Royal Society B: Biological Sciences, 279, 3643–3651. https://doi.org/10.1098/rspb.2012.0869

Nery MF, González DJ, Opazo JC (2013) How to make a dolphin: molecular signature of positive selection in cetacean genome. PLOS ONE, 8, e65491. https://doi.org/10.1371/journal.pone.0065491

Sun YB, Zhou WP, Liu HQ, Irwin DM, Shen YY, Zhang YP (2013) Genome-wide scans for candidate genes involved in the aquatic adaptation of dolphins. Genome Biology and Evolution, 5, 130–139. https://doi.org/10.1093/gbe/evs123

Tian R, Wang Z, Niu X, Zhou K, Xu S, Yang G (2016) Evolutionary genetics of hypoxia tolerance in cetaceans during diving. Genome Biology and Evolution, 8, 827–839. https://doi.org/10.1093/gbe/evw037

Thewissen JGM, Cooper LN, George JC, Bajpai  (2009) From land to water: the origin of whales, dolphins, and porpoises. Evolution: Education and Outreach, 2, 272–288. https://doi.org/10.1007/s12052-009-0135-2

Uribe C, Nery M, Zavala K, Mardones G, Riadi G, Opazo J (2024) Evolution of ion channels in cetaceans: A natural experiment in the tree of life. bioRxiv, ver. 8 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.06.15.545160

Yim HS, Cho YS, Guang X, Kang SG, Jeong JY, Cha SS, Oh HM, Lee JH, Yang EC, Kwon KK, et al. (2014) Minke whale genome and aquatic adaptation in cetaceans. Nature Genetics, 46, 88–92. https://doi.org/10.1038/ng.2835

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

Several variations of the “universal” genetic code are known. Among the most striking are those where a codon can either encode for an amino acid or a stop signal depending on the context. Such ambiguous codes are known to have evolved in eukaryotes multiple times independently, particularly in ciliates – eight different codes have so far been discovered (1). We generally view such genetic codes are rare ‘variants’ of the standard code restricted to single species or strains, but this might as well reflect a lack of study of closely related species. In this study, Seah and co-authors (2) explore the possibility of codon reassignment in karyorelict ciliates closely related to Parduczia sp., which has been shown to contain an ambiguous genetic code (1). Here, single-cell transcriptomics are used, along with similar available data, to explore the possibility of codon reassignment across the diversity of Karyorelictea (four out of the six recognized families). Codon reassignments were inferred from their frequencies within conserved Pfam (3) protein domains, whereas stop codons were inferred from full-length transcripts with intact 3’-UTRs.

Results show the reassignment of UAA and UAG stop codons to code for glutamine (Q) and the reassignment of the UGA stop codon into tryptophan (W). This occurs only within the coding sequences, whereas the end of transcription is marked by UGA as the main stop codon, and to a lesser extent by UAA. In agreement with a previous model proposed that explains the functioning of ambiguous codes (1,4), the authors observe a depletion of in-frame UGAs before the UGA codon that indicates the stop, thus avoiding premature termination of transcription. The inferred codon reassignments occur in all studied karyorelicts, including the previously studied Parduczia sp. Despite the overall clear picture, some questions remain. Data for two out of six main karyorelict lineages are so far absent and the available data for Cryptopharyngidae was inconclusive; the phylogenetic affinities of Cryptopharyngidae have also been questioned (5). This indicates the need for further study of this interesting group of organisms. As nicely discussed by the authors, experimental evidence could further strengthen the conclusions of this paper, including ribosome profiling, mass spectrometry – as done for Condylostoma (1) – or even direct genetic manipulation. 

The uniformity of the ambiguous genetic code across karyorelicts might at first seem dull, but when viewed in a phylogenetic context character distribution strongly suggest that this genetic code has an ancient origin in the karyorelict ancestor ~455 Ma in the Proterozoic (6). This ambiguous code is also not a rarity of some obscure species, but it is shared by ciliates that are very diverse and ecologically important. The origin of the karyorelict code is also intriguing. Adaptive arguments suggest that it could confer robustness to mutations causing premature stop codons. However, we lack evidence for ambiguous codes being linked to specific habitats of lifestyles that could account for it. Instead, the authors favor the neutral view of an ancient “frozen accident”, fixed stochastically simply because it did not pose a significant selective disadvantage. Once a stop codon is reassigned to an amino acid, it is increasingly difficult to revert this without the deleterious effect of prematurely terminating translation. At the end, the origin of the genetic code itself is thought to be a frozen accident too (7).

References

1. Swart EC, Serra V, Petroni G, Nowacki M. Genetic codes with no dedicated stop codon: Context-dependent translation termination. Cell 2016;166: 691–702. https://doi.org/10.1016/j.cell.2016.06.020

2. Seah BKB, Singh A, Swart EC (2022) Karyorelict ciliates use an ambiguous genetic code with context-dependent stop/sense codons. bioRxiv, 2022.04.12.488043. ver. 4 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.04.12.488043

3. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A. Pfam: The protein families database in 2021, Nuc Acids Res 2020;49: D412-D419. https://doi.org/10.1093/nar/gkaa913

4. Alkalaeva E, Mikhailova T. Reassigning stop codons via translation termination: How a few eukaryotes broke the dogma. Bioessays. 2017;39. https://doi.org/10.1002/bies.201600213

5. Xu Y, Li J, Song W, Warren A. Phylogeny and establishment of a new ciliate family, Wilbertomorphidae fam. nov. (Ciliophora, Karyorelictea), a highly specialized taxon represented by Wilbertomorpha colpoda gen. nov., spec. nov. J Eukaryot Microbiol. 2013;60: 480–489. https://doi.org/10.1111/jeu.12055

6. Fernandes NM, Schrago CG. A multigene timescale and diversification dynamics of Ciliophora evolution. Mol Phylogenet Evol. 2019;139: 106521. https://doi.org/10.1016/j.ympev.2019.106521

7. Crick FH. The origin of the genetic code. J Mol Biol. 1968;38: 367–379. https://doi.org/10.1016/0022-2836(68)90392-6

Green algae of the genus Coccomyxa (family Trebouxiophyceae) are extremely diverse in their morphology, habitat (i.e., in marine, freshwater, and terrestrial environments) and lifestyle, including free-living and mutualistic forms. Coccomyxa viridis (strain SAG 216–4) is a photobiont in the lichen Peltigera aphthosa, which was isolated in Switzerland more than 70 years ago (cf. SAG, the Culture Collection of Algae at the University of Göttingen, Germany). Despite the high diversity and plasticity in Coccomyxa, integrative taxonomic analyses led Darienko et al. (2015) to propose clear species boundaries. These authors also showed that symbiotic strains that form lichens evolved multiple times independently in Coccomyxa.

Using state-of-the-art sequencing data and bioinformatic methods, including Pac-Bio HiFi and ONT long reads, as well as Hi-C chromatin conformation information, Kraege et al. (2024) generated a high-quality genome assembly for the Coccomyxa viridis strain SAG 216–4. They reconstructed 19 complete nuclear chromosomes, flanked by telomeric regions, totaling 50.9 Mb, plus the plastid and mitochondrial genomes. The performed quality controls leave no doubt of the high quality of the genome assemblies and structural annotations. An interesting observation is the lack of conserved synteny with the close relative Coccomyxa subellipsoidea, but further comparative studies with additional Coccomyxa strains will be required to grasp the genomic evolution in this genus of green algae. This project is framed within the ERGA pilot project, which aims to establish a pan-European genomics infrastructure and contribute to cataloging genomic biodiversity and producing resources that can inform conservation strategies (Formenti et al. 2022). This complete reference genome represents an important step towards this goal, in addition to contributing to future genomic analyses of Coccomyxa more generally.

References

Darienko T, Gustavs L, Eggert A, Wolf W, Pröschold T (2015) Evaluating the species boundaries of green microalgae (Coccomyxa, Trebouxiophyceae, Chlorophyta) using integrative taxonomy and DNA barcoding with further implications for the species identification in environmental samples. PLOS ONE, 10, e0127838. https://doi.org/10.1371/journal.pone.0127838

Formenti G, Theissinger K, Fernandes C, Bista I, Bombarely A, Bleidorn C, Ciofi C, Crottini A, Godoy JA, Höglund J, Malukiewicz J, Mouton A, Oomen RA, Paez S, Palsbøll PJ, Pampoulie C, Ruiz-López MJ, Svardal H, Theofanopoulou C, de Vries J, Waldvogel A-M, Zhang G, Mazzoni CJ, Jarvis ED, Bálint M, European Reference Genome Atlas Consortium (2022) The era of reference genomes in conservation genomics. Trends in Ecology & Evolution, 37, 197–202. https://doi.org/10.1016/j.tree.2021.11.008

Kraege A, Chavarro-Carrero EA, Guiglielmoni N, Schnell E, Kirangwa J, Heilmann-Heimbach S, Becker K, Köhrer K, WGGC Team, DeRGA Community, Schiffer P, Thomma BPHJ, Rovenich H (2024) High quality genome assembly and annotation (v1) of the eukaryotic terrestrial microalga Coccomyxa viridis SAG 216-4. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.07.11.548521

Sobala (2024) introduces a new, comprehensive, and curated eukaryotic database. It consolidates information from EukProt (Richter et al. 2022) and various other resources to enhance Metazoa representation in existing protein databases. The preprint is of significant interest to the phylogenomics and comparative genomics communities, and I commend the author for their work.

LukProt, the expanded database, significantly increases the taxon sampling within holozoans. It integrates data from the previously assembled EukProt and AniProtDB (Barreira et al. 2021) databases, with additional datasets from early-diverging animal lineages such as ctenophores, sponges, and cnidarians. This effort will undoubtedly be useful for researchers investigating these clades and their origins, as well as for the broader field of comparative genomics.

The author provides both web-portal and command-line versions of the database, making it accessible to users with varying degrees of bioinformatic proficiency. The curation effort is commendable, and I believe the comparative genomics community, especially those interested in animal origins, will find LukProt to be a valuable resource.

References

Barreira SN, Nguyen A-D, Fredriksen MT, Wolfsberg TG, Moreland RT, Baxevanis AD (2021) AniProtDB: A collection of consistently generated metazoan proteomes for comparative genomics studies. Molecular Biology and Evolution 38, 4628–4633. https://doi.org/10.1093/molbev/msab165

Richter DJ, Berney C, Strassert JFH, Poh Y-P, Herman EK, Muñoz-Gómez SA, Wideman JG, Burki F, de Vargas C (2022) EukProt: A database of genome-scale predicted proteins across the diversity of eukaryotes. Peer Community Journal 2, e56. https://doi.org/10.24072/pcjournal.173

Sobala ŁF (2024) LukProt: A database of eukaryotic predicted proteins designed for investigations of animal origins. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2024.01.30.577650