Biodiversity is unevenly distributed over time, space and the tree of life [1]. The fact that regions are richer than others as exemplified by the latitudinal diversity gradient has fascinated biologists as early as the first explorers travelled around the world [2]. Provincialism was one of the first general features of land biotic distributions noted by famous nineteenth century biologists like the phytogeographers J.D. Hooker and A. de Candolle, and the zoogeographers P.L. Sclater and A.R. Wallace [3]. When these explorers travelled among different places, they were struck by the differences in their biotas (e.g. [4]). The limited distributions of distinctive endemic forms suggested a history of local origin and constrained dispersal. Much biogeographic research has been devoted to identifying areas where groups of organisms originated and began their initial diversification [3]. Complementary efforts found evidence of both historical barriers that blocked the exchange of organisms between adjacent regions and historical corridors that allowed dispersal between currently isolated regions. The result has been a division of the Earth into a hierarchy of regions reflecting patterns of faunal and floral similarities (e.g. regions, subregions, provinces). Therefore a first ensuing question is: “how regional species pools have been assembled through time and space?”, which can be followed by a second question: “what are the ecological and evolutionary processes leading to differences in species richness among species pools?”.

To address these questions, the study of Calatayud et al. [5] developed and performed an interesting approach relying on phylogenetic data to identify regional and sub-regional pools of European beetles (using the iconic ground beetle genus Carabus). Specifically, they analysed the processes responsible for the assembly of species pools, by comparing the effects of dispersal barriers, niche similarities and phylogenetic history. They found that Europe could be divided in seven modules that group zoogeographically distinct regions with their associated faunas, and identified a transition zone matching the limit of the ice sheets at Last Glacial Maximum (19k years ago). Deviance of species co-occurrences across regions, across sub-regions and within each region was significantly explained, primarily by environmental niche similarity, and secondarily by spatial connectivity, except for northern regions. Interestingly, southern species pools are mostly separated by dispersal barriers, whereas northern species pools are mainly sorted by their environmental niches. Another important finding of Calatayud et al. [5] is that most phylogenetic structuration occurred during the Pleistocene, and they show how extreme recent historical events (Quaternary glaciations) can profoundly modify the composition and structure of geographic species pools, as opposed to studies showing the role of deep-time evolutionary processes.

The study of biogeographic assembly of species pools using phylogenies has never been more exciting and promising than today. Catalayud et al. [5] brings a nice study on the importance of Pleistocene glaciations along with geographical barriers and niche-based processes in structuring the regional faunas of European beetles. The successful development of powerful analytical tools in recent years, in conjunction with the rapid and massive increase in the availability of biological data (including molecular phylogenies, fossils, georeferrenced occurrences and ecological traits), will allow us to disentangle complex evolutionary histories. Although we still face important limitations in data availability and methodological shortcomings, the last decade has witnessed an improvement of our understanding of how historical and biotic triggers are intertwined on shaping the Earth’s stupendous biological diversity. I hope that the Catalayud et al.’s approach (and analytical framework) will help movement in that direction, and that it will provide interesting perspectives for future investigations of other regions. Applied to a European beetle radiation, they were able to tease apart the relative contributions of biotic (niche-based processes) versus abiotic (geographic barriers and climate change) factors.

References

[1] Rosenzweig ML. 1995. Species diversity in space and time. Cambridge: Cambridge University Press.
[2] Mittelbach GG, Schemske DW, Cornell HV, Allen AP, Brown JM et al. 2007. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecology Letters. 10: 315–331. doi: 10.1111/j.1461-0248.2007.01020.x
[3] Lomolino MV, Riddle BR, Whittaker RJ and Brown JH. 2010. Biogeography, 4th edn. Sinauer Associates, Inc., Sunderland, MA.
[4] Wallace AR. 1876. The geographical distribution of animals: with a study of the relations of living and extinct faunas as elucidating the past changes of the earth's surface. New York: Harper and Brothers, Publishers.
[5] Calatayud J, Rodríguez MÁ, Molina-Venegas R, Leo M, Hórreo JL and Hortal J. 2018. Pleistocene climate change and the formation of regional species pools. bioRxiv 149617 ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/149617

Symbiotic associations with bacterial partners have facilitated important evolutionary transitions in the life histories of eukaryotes. For instance, many insects have established long-term interactions with intracellular bacteria that provide them with essential nutrients lacking in their diet. However, despite the high level of interdependency among organisms involved in endosymbiotic systems, examples of symbiont replacements along the evolutionary history of insect hosts are numerous. In their paper, Husnik and McCutcheon [1] test the stability of symbiotic systems in a particularly imbricated Russian-doll type interaction, where one bacterium lives insides another bacterium, which itself lives inside insect cells. For their study, they chose representative species of mealybugs (Pseudococcidae), a species rich group of sap-feeding insects that hosts diverse and complex symbiotic systems. In species of the subfamily Pseudococcinae, data published so far suggest that the primary symbiont, a ß-proteobacterium named Tremblaya princeps, is supplemented by a second bacterial symbiont (a ϒ-proteobaterium) that lives within its cytoplasm; both participate to the metabolic pathways that provide essential amino acids and vitamins to their hosts. Here, Husnik and McCutcheon generate host and endosymbiont genome data for five phylogenetically divergent species of Pseudococcinae in order to better understand: 1) the evolutionary history of the symbiotic associations; 2) the metabolic roles of each partner, 3) the timing and origin of Horizontal Gene Transfers (HGT) between the hosts and their symbionts.
Their results show that all species harbour the primary and at least one secondary symbiont, whose intra-bacterial localization was verified using fluorescence in situ hybridization. In one species (Pseudococcus longispinus), Tremblaya even hosts two intracellular bacteria each with a large genome (even though it is not entirely clear whether these two symbionts are indeed found within Tremblaya). The obligate presence of an intra-bacterial symbiont is best explained by the loss in Tremblaya princeps of critical genes for translation that require “intracellular complementation”.
The most striking result concerns the identity of the intra-bacterial symbiont: genome sizes and structures of the “secondary” (intra-Tremblaya) symbionts vary drastically according to the host species. Phylogenetic analyses based on 80 conserved proteins, place all these ϒ-proteoacteria (except one of the P. longispinus symbionts) in a Sodalis allied clade. However, their relationships do not mirror the one of their hosts, and some of them show signs of very recent acquisition. Altogether these results provide strong evidence for several independent acquisitions of these highly intra-bacterial integrated symbionts. Scenarios for the history of the symbiosis are clearly laid out and discussed by the authors, and the scenario involving several independent replacements of a the intra-Tremblaya symbiont by diverse Sodalis-like bacteria appears the most likely given the data presented here. Selected biosynthetic functions are then mapped onto the host and symbiont genomes, showing the high level of interdependency of the partners for the synthesis of essential amino acids and vitamin. These maps also identify genes in the host genome that might have been acquired through HGT from bacteria and show that many of them are shared by all mealybug species sequenced so far. HGT events have thus predated the acquisitions of the current “intra-Tremblaya“ symbionts and have probably been acquired from previous symbiont infections.
Overall, this is a thorough study, using a diverse set of data and meticulous analyses that present convincing evidence that replacements of symbionts occurred repeatedly even in an imbricated symbiotic system. The phylogenetic analyses inferring the timing of HGTs also depict a highly dynamic history of gene losses and retentions in both host and symbiont genomes. Altogether these results demonstrate how the chimeric nature of individuals allows shuffling at different levels of organisation: organisms like symbionts can be replaced, genes can be exchanged among the partners of the chimera. The surprise is that constraints arising from interdependencies do not impede these exchanges. In the discussion, the authors present a very interesting parallel with the evolution of organelles: if the story presented here mirrors the one of mitochondria, it supports a gradualist view where mitochondria arrived late in the evolution of eukaryotic cells that already contained many bacterial genes resulting from HGT from previous symbionts.

Reference

[1] Husnik F., McCutcheon JP. 2016. Repeated replacements of an intrabacterial symbiont in the tripartite nested mealybug symbiosis. PNAS 113: E5416-E5424. doi: 10.1073/pnas.1603910113

Perhaps because of its deadliness, the 2013-2016 Ebola Virus (EBOV) epidemics in West-Africa has led to unprecedented publication and sharing of full virus genome sequences. This was both rapid (90 full genomes were shared within weeks [1]) and important (more than 1500 full genomes have been released overall [2]). Furthermore, the availability of the metadata (especially GPS location) has led to depth analyses of the geographical spread of the epidemics [3].
In their work, Dellicour et al. [4] pursue earlier phylogeographical investigations in an original and yet simple approach to address questions of key public health importance. The originality of the approach is dual. First, from a technical standpoint, they capture the spread of infectious diseases in a continuous framework using a novel model that allows for rare long-distance dispersal events. Second, in a more classical discrete meta-population framework, they simulate the effect of public health interventions by pruning the phylogenetic tree and assessing how this affects key parameters. For instance, to simulate the effect of closing borders they remove subsets of the phylogeny that involved dispersal between countries and to simulate the effect of protecting a region by quarantine they remove all the leaves (i.e. the infections sampled) from this region. This phylogeny pruning is both original and simple. It is however limited because it currently assumes that policies are 100% effective and earlier modelling work on human influenza showed that long distance travel bans had to be implemented with >99% efficiency in order to slow epidemic growth from a time scale of days to weeks [5].
From a biological standpoint, Dellicour et al. [4] corroborate earlier findings that highly populated locations (>1,000,000 inhabitants) were crucial in explaining the magnitude of the epidemics but also show the importance of the transmission between the three capital cities. They also show that rare long-distance dispersing events of the virus are not key to explaining the magnitude of the epidemics (even though they assume 100% efficiency of suppressing long-distance event). Finally, thanks to their continuous model they estimate the speed of spread of the epidemics and are able to detect the effect of border closing on this speed.
Overall, this study [4], which involves state-of-the-art Bayesian inference methods of infection phylogenies using MCMC, stands out because of its effort to simulate public health interventions. It stands as an encouragement for the development of intervention models with increased realism and for even faster and larger virus sequence data sharing.

References

[1] Gire et al. 2014. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345: 1369–1372. doi: 10.1126/science.1259657.
[2] Holmes EC, Dudas G, Rambaut A and Andersen KG. 2016. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature 538: 193–200. doi: 10.1038/nature19790.
[3] Dudas et al. 2017. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544: 309–315 (2017). doi: 10.1038/nature22040.
[4] Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaud A and Lemey P. 2018. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. bioRxiv, 163691, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/163691.
[5] Hollingsworth TD, Ferguson NM and Anderson RM. 2006. Will travel restrictions control the international spread of pandemic influenza? Nature Medicine 12, 497–499. doi: 10.1038/nm0506-497.

Recent advances in next-generation sequencing are allowing us to uncover the evolution of sex chromosomes in non-model organisms. This study [1] represents an example of this application to birds of two Sylvioidea species from the genus Zosterops (commonly known as white-eyes). The study is exemplary in the amount and types of data generated and in the thoroughness of the analysis applied. Both male and female genomes were sequenced to allow the authors to identify sex-chromosome specific scaffolds. These data were augmented by generating the transcriptome (RNA-seq) data set. The findings after the analysis of these extensive data are intriguing: neoZ and neoW chromosome scaffolds and their breakpoints were identified. Novel sex chromosome formation appears to be accompanied by translocation events. The timing of formation of novel sex chromosomes was identified using molecular dating and appears to be relatively recent. Yet first signatures of distinct evolutionary patterns of sex chromosomes vs. autosomes could be already identified. These include the accumulation of transposable elements and changes in GC content. The changes in GC content could be explained by biased gene conversion and altered recombination landscape of the neo sex chromosomes. The authors also study divergence and diversity of genes located on the neo sex chromosomes. Here their findings appear to be surprising and need further exploration. The neoW chromosome already shows unique patterns of divergence and diversity at protein-coding genes as compared with genes on either neoZ or autosomes. In contrast, the genes on the neoZ chromosome do not display divergence or diversity patterns different from those for autosomes. This last observation is puzzling and I believe should be explored in further studies. Overall, this study significantly advances our knowledge of the early stages of sex chromosome evolution in vertebrates, provides an example of how such a study could be conducted in other non-model organisms, and provides several avenues for future work.

References

[1] Leroy T., Anselmetti A., Tilak M.K., Bérard S., Csukonyi L., Gabrielli M., Scornavacca C., Milá B., Thébaud C. and Nabholz B. (2019). A bird’s white-eye view on neo-sex chromosome evolution. bioRxiv, 505610, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/505610

Sirtuin proteins are class III histone deacetylases that are involved in a variety of fundamental biological functions mostly related to aging. These proteins are located in different subcellular compartments and are associated with different biological functions such as metabolic regulation, stress response, and cell cycle control [1]. In mammals, the sirtuin gene family is composed of seven paralogs (SIRT1-7) grouped into four classes [2]. Due to their involvement in maintaining cell cycle integrity, sirtuins have been studied as a way to understand fundamental mechanisms governing longevity [1]. Indeed, the downregulation of sirtuin genes with aging seems to explain much of the pathophysiology that accumulates with aging [3]. Biomedical studies have thus explored the potential therapeutic implications of sirtuins [4] but whether they can effectively be used as molecular targets for the treatment of human diseases remains to be demonstrated [1]. Despite this biomedical interest and some phylogenetic analyses of sirtuin paralogs mostly conducted in mammals, a comprehensive evolutionary analysis of the sirtuin gene family at the scale of vertebrates was still lacking.

In this preprint, Opazo and collaborators [5] took advantage of the increasing availability of whole-genome sequences for species representing all main groups of vertebrates to unravel the evolution of the sirtuin gene family. To do so, they undertook a phylogenomic approach in its original sense aimed at improving functional predictions by evolutionary analysis [6] in order to inventory the full vertebrate sirtuin gene repertoire and reconstruct its precise duplication history. Harvesting genomic databases, they extracted all predicted sirtuin proteins and performed phylogenetic analyses based on probabilistic inference methods. Maximum likelihood and Bayesian analyses resulted in well-resolved and congruent phylogenetic trees dividing vertebrate sirtuin genes into three major clades. These analyses also revealed an additional eighth paralog that was previously overlooked because of its restricted phyletic distribution. This newly identified sirtuin family member (named SIRT8) was recovered with unambiguous statistical support as a sister-group to the SIRT3 clade. Comparative genomic analyses based on conserved gene synteny confirmed that SIRT8 was present in all sampled non-amniote vertebrate genomes (cartilaginous fish, bony fish, coelacanth, lungfish, and amphibians) except cyclostomes. SIRT8 has thus most likely been lost in the last common ancestor of amniotes (mammals, reptiles, and birds). Discovery of such previously unknown genes in vertebrates is not completely surprising given the plethora of high-quality genomes now available. However, this study highlights the importance of considering a broad taxonomic sampling to infer evolutionary patterns of gene families that have been mostly studied in mammals because of their potential importance for human biology.

Based on its phylogenetic position as closely related to SIRT3 within class I, it could be predicted that the newly identified SIRT8 paralog likely has a deacetylase activity and is probably located in mitochondria. To test these evolutionary predictions, Opazo and collaborators [5] conducted further bioinformatics analyses and functional experiments using the elephant shark (Callorhinchus milii) as a model species. RNAseq expression data were analyzed to determine tissue-specific transcription of sirtuin genes in vertebrates, including SIRT8 found to be mainly expressed in the ovary, which suggests a potential role in biological processes associated with reproduction. The elephant shark SIRT8 protein sequence was used with other vertebrates for comparative analyses of protein structure modeling and subcellular localization prediction both pointing to a probable mitochondrial localization. The protein localization and its function were further characterized by immunolocalization in transfected cells, and enzymatic and functional assays, which all confirmed the prediction that SIRT8 proteins are targeted to the mitochondria and have deacetylase activity. The extensive experimental efforts made in this study to shed light on the function of this newly discovered gene are both rare and highly commendable.

Overall, this work by Opazo and collaborators [5] provides a comprehensive phylogenomic study of the sirtuin gene family in vertebrates based on detailed evolutionary analyses using state-of-the-art phylogenetic reconstruction methods. It also illustrates the power of adopting an integrative comparative approach supplementing the reconstruction of the duplication history of the gene family with complementary functional experiments in order to elucidate the function of the newly discovered SIRT8 family member. These results provide a reference phylogenetic framework for the evolution of sirtuin genes and the further functional characterization of the eight vertebrate paralogs with potential relevance for understanding the cellular biology of aging and its associated diseases in human.

References

[1] Vassilopoulos A, Fritz KS, Petersen DR, Gius D (2011) The human sirtuin family: Evolutionary divergences and functions. Human Genomics, 5, 485. https://doi.org/10.1186/1479-7364-5-5-485

[2] Yamamoto H, Schoonjans K, Auwerx J (2007) Sirtuin Functions in Health and Disease. Molecular Endocrinology, 21, 1745–1755. https://doi.org/10.1210/me.2007-0079

[3] Morris BJ (2013) Seven sirtuins for seven deadly diseases ofaging. Free Radical Biology and Medicine, 56, 133–171. https://doi.org/10.1016/j.freeradbiomed.2012.10.525

[4] Bordo D Structure and Evolution of Human Sirtuins. Current Drug Targets, 14, 662–665. http://dx.doi.org/10.2174/1389450111314060007

[5] Opazo JC, Vandewege MW, Hoffmann FG, Zavala K, Meléndez C, Luchsinger C, Cavieres VA, Vargas-Chacoff L, Morera FJ, Burgos PV, Tapia-Rojas C, Mardones GA (2022) How many sirtuin genes are out there? evolution of sirtuin genes in vertebrates with a description of a new family member. bioRxiv, 2020.07.17.209510, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology.  https://doi.org/10.1101/2020.07.17.209510

[6] Eisen JA (1998) Phylogenomics: Improving Functional Predictions for Uncharacterized Genes by Evolutionary Analysis. Genome Research, 8, 163–167. https://doi.org/10.1101/gr.8.3.163