Parasitoid wasps have developed different mechanisms to increase their parasitic success, usually at the expense of host survival (Fellowes and Godfray, 2000). Eggs of these insects are deposited inside the juvenile stages of their hosts, which in turn deploy several immune response strategies to eliminate or disable them (Yang et al., 2020). Drosophila melanogaster protects itself against parasitoid attacks through the production of specific elongated haemocytes called lamellocytes which form a capsule around the invading parasite (Lavine and Strand, 2002; Rizki and Rizki, 1992) and the subsequent activation of the phenol-oxidase cascade leading to the release of toxic radicals (Nappi et al., 1995). On the parasitoid side, robust responses have evolved to evade host immune defenses as for example the Drosophila-specific endoparasite Leptopilina boulardi, which releases venom during oviposition that modifies host behaviour (Varaldi et al., 2006) and inhibits encapsulation (Gueguen et al., 2011; Martinez et al., 2012).
Studies have shown that the wasp parasitic capacity is correlated to venom presence and its content (Colinet et al., 2009; Poirié et al., 2014), including that evolution of venom protein composition is driven by different levels of host susceptibility to infection (Cavigliasso et al., 2019). However, it had not been determined to this day, if and how parasitic range can affect venom protein composition and to which extent host specialization requires broad-spectrum factors or a plethora of specialized components.
These outstanding questions are now approached in a study by Cavigliasso and colleagues (Cavigliasso et al., 2021), where they perform experimental evolution of L. boulardi for 9 generations exposing it to different Drosophila host species and genetic backgrounds (two strains of D. melanogaster, D. simulans and D. yakuba). The authors tested whether the parasitic success of each selection regime was host-specific and how they influenced venom composition in parasitoids. For the first part, infection outcomes were assayed for each selection regime when cross-infecting different hosts. To get a finer measurement of the mechanisms under selection, the authors differentiated three phenotypes: overall parasitic success, encapsulation inhibition and escape from capsule. Throughout the course of experimental evolution, only encapsulation inhibition did not show an improved response upon selection on any host. Importantly, the cross-infection scenario revealed a clear specificity to the selected host for each evolved resistance.
As for venom composition, a trend of differential evolution was detected between host species, although a significant part of that was due to a larger differentiation in the D. yakuba regime, which showed a completely different directionality. Importantly, the authors could identify some of the specific proteins targeted by the several selection regimes, whether selected or counter-selected for. Interestingly, the D. yakuba regime is the only case where the key parasitoid protein LbSPNy (Colinet et al., 2009) was not counter-selected and the only regime in which the overall venom composition did not evolve towards the Ism strain, one of the two ancestral strains of L. boulardi used in the study. It is possible that these two results are correlated, since LbSPNy has been described to inhibit activation of the phenoloxidase cascade in D. yakuba and is one of the most abundant proteins in the ISy venom, making it a good target for selection (Colinet et al., 2013). The authors also discuss the possibility that this difference is related to the geographical distribution of the strains of L. boulardi, since each coincide with either D. melanogaster or D. yakuba.
This methodologically broad work by Cavigliasso and colleagues constitutes an important experimental contribution towards the understanding of how parasitoid adaptation to specific hosts is achieved at different phenotypic and mechanistic levels. It provides compelling evidence that venom composition evolves differently in response to specific parasitic ranges, particularly considering the evolutionary difference between the selective hosts. In line with this result, it is also concluded that the majority of venom proteins selected are lineage-specific, although a few broad-spectrum factors could also be detected. 
The question of whether parasitic range can affect venom composition and parasitic success is still open to more contributions. A potentially interesting long-term direction will be to use a similar setup of experimental evolution on the generalist L. heterotoma (Schlenke et al., 2007) . On a more immediate horizon, comparing the venom evolution of both L. heterotoma and L. boulardi under selection with different hosts and under cross-infection scenarios could reveal interesting patterns. The recent sequencing of the L. boulardi genome together with the vast number of studies addressing mechanisms of Drosophila resistance to parasitoid infection, will enable the thorough characterization of the genetic basis of host-parasitoid interactions and the deeper understanding of these ubiquitous and economically-relevant relationships.
 
*This recommendation text has been co-written with Tânia F. Paulo who is not a recommender of PCI Evol Biol

References

Cavigliasso, F., Mathé-Hubert, H., Gatti, J.-L., Colinet, D. and Poirié, M. (2021) Parasitic success and venom composition evolve upon specialization of parasitoid wasps to different host species. bioRxiv, 2020.10.24.353417, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.10.24.353417

Cavigliasso, F., Mathé-Hubert, H., Kremmer, L., Rebuf, C., Gatti, J.-L., Malausa, T., Colinet, D., Poiré, M. and  Léne. (2019). Rapid and Differential Evolution of the Venom Composition of a Parasitoid Wasp Depending on the Host Strain. Toxins, 11(629). https://doi.org/10.3390/toxins11110629

Colinet, D., Deleury, E., Anselme, C., Cazes, D., Poulain, J., Azema-Dossat, C., Belghazi, M., Gatti, J. L. and  Poirié, M. (2013). Extensive inter- and intraspecific venom variation in closely related parasites targeting the same host: The case of Leptopilina parasitoids of Drosophila. Insect Biochemistry and Molecular Biology, 43(7), 601–611. https://doi.org/10.1016/j.ibmb.2013.03.010

Colinet, D., Dubuffet, A., Cazes, D., Moreau, S., Drezen, J. M. and  Poirié, M. (2009). A serpin from the parasitoid wasp Leptopilina boulardi targets the Drosophila phenoloxidase cascade. Developmental and Comparative Immunology, 33(5), 681–689. https://doi.org/10.1016/j.dci.2008.11.013

Fellowes, M. D. E. and  Godfray, H. C. J. (2000). The evolutionary ecology of resistance to parasitoids by Drosophila. Heredity, 84(1), 1–8. https://doi.org/10.1046/j.1365-2540.2000.00685.x

Gueguen, G., Rajwani, R., Paddibhatla, I., Morales, J. and  Govind, S. (2011). VLPs of Leptopilina boulardi share biogenesis and overall stellate morphology with VLPs of the heterotoma clade. Virus Research, 160(1–2), 159–165. https://doi.org/10.1016/j.virusres.2011.06.005

Lavine, M. D. and  Strand, M. R. (2002). Insect hemocytes and their role in immunity. Insect Biochemistry and Molecular Biology, 32(10), 1295–1309. https://doi.org/10.1016/S0965-1748(02)00092-9

Martinez, J., Duplouy, A., Woolfit, M., Vavre, F., O’Neill, S. L. and  Varaldi, J. (2012). Influence of the virus LbFV and of Wolbachia in a host-parasitoid interaction. PloS One, 7(4), e35081. https://doi.org/10.1371/journal.pone.0035081

Nappi, A. J., Vass, E., Frey, F. and  Carton, Y. (1995). Superoxide anion generation in Drosophila during melanotic encapsulation of parasites. European Journal of Cell Biology, 68(4), 450–456.

Poirié, M., Colinet, D. and  Gatti, J. L. (2014). Insights into function and evolution of parasitoid wasp venoms. Current Opinion in Insect Science, 6, 52–60. https://doi.org/10.1016/j.cois.2014.10.004

Rizki, T. M. and  Rizki, R. M. (1992). Lamellocyte differentiation in Drosophila larvae parasitized by Leptopilina. Developmental and Comparative Immunology, 16(2–3), 103–110. https://doi.org/10.1016/0145-305X(92)90011-Z

Schlenke, T. A., Morales, J., Govind, S. and  Clark, A. G. (2007). Contrasting infection strategies in generalist and specialist wasp parasitoids of Drosophila melanogaster. PLoS Pathogens, 3(10), 1486–1501. https://doi.org/10.1371/journal.ppat.0030158

Varaldi, J., Petit, S., Boulétreau, M. and  Fleury, F. (2006). The virus infecting the parasitoid Leptopilina boulardi exerts a specific action on superparasitism behaviour. Parasitology, 132(Pt 6), 747–756. https://doi.org/10.1017/S0031182006009930

Yang, L., Qiu, L., Fang, Q., Stanley, D. W. and  Gong‐Yin, Y. (2020). Cellular and humoral immune interactions between Drosophila and its parasitoids. Insect Science. https://doi.org/10.1111/1744-7917.12863

Sequencing and analyzing SARS-Cov-2 genomes in nearly real time has the potential to quickly confirm (and inform) our knowledge of, and response to, the current pandemic [1,2]. In this manuscript [3], Danesh and colleagues use the earliest set of available SARS-Cov-2 genome sequences available from France to make inferences about the timing of the major epidemic wave, the duration of infections, and the efficacy of lockdown measures. Their phylodynamic estimates -- based on fitting genomic data to molecular clock and transmission models -- are reassuringly close to estimates based on 'traditional' epidemiological methods: the French epidemic likely began in mid-January or early February 2020, and spread relatively rapidly (doubling every 3-5 days), with people remaining infectious for a median of 5 days [4,5]. These transmission parameters are broadly in line with estimates from China [6,7], but are currently unknown in France (in the absence of contact tracing data). By estimating the temporal reproductive number (Rt), the authors detected a slowing down of the epidemic in the most recent period of the study, after mid-March, supporting the efficacy of lockdown measures.
Along with the three other reviewers of this manuscript, I was impressed with the careful and exhaustive phylodynamic analyses reported by Danesh et al. [3]. Notably, they take care to show that the major results are robust to the choice of priors and to sampling. The authors are also careful to note that the results are based on a limited sample size of SARS-Cov-2 genomes, which may not be representative of all regions in France. Their analysis also focused on the dominant SARS-Cov-2 lineage circulating in France, which is also circulating in other countries. The variations they inferred in epidemic growth in France could therefore be reflective on broader control policies in Europe, not only those in France. Clearly more work is needed to fully unravel which control policies (and where) were most effective in slowing the spread of SARS-Cov-2, but Danesh et al. [3] set a solid foundation to build upon with more data. Overall this is an exemplary study, enabled by rapid and open sharing of sequencing data, which provides a template to be replicated and expanded in other countries and regions as they deal with their own localized instances of this pandemic.

References

[1] Grubaugh, N. D., Ladner, J. T., Lemey, P., Pybus, O. G., Rambaut, A., Holmes, E. C., & Andersen, K. G. (2019). Tracking virus outbreaks in the twenty-first century. Nature microbiology, 4(1), 10-19. doi: 10.1038/s41564-018-0296-2
[2] Fauver et al. (2020) Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States. Cell, 181(5), 990-996.e5. doi: 10.1016/j.cell.2020.04.021
[3] Danesh, G., Elie, B., Michalakis, Y., Sofonea, M. T., Bal, A., Behillil, S., Destras, G., Boutolleau, D., Burrel, S., Marcelin, A.-G., Plantier, J.-C., Thibault, V., Simon-Loriere, E., van der Werf, S., Lina, B., Josset, L., Enouf, V. and Alizon, S. and the COVID SMIT PSL group (2020) Early phylodynamics analysis of the COVID-19 epidemic in France. medRxiv, 2020.06.03.20119925, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/2020.06.03.20119925
[4] Salje et al. (2020) Estimating the burden of SARS-CoV-2 in France. hal-pasteur.archives-ouvertes.fr/pasteur-02548181
[5] Sofonea, M. T., Reyné, B., Elie, B., Djidjou-Demasse, R., Selinger, C., Michalakis, Y. and Samuel Alizon, S. (2020) Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France. medRxiv, 2020.05.22.20110593. doi: 10.1101/2020.05.22.20110593
[6] Rambaut, A. (2020) Phylogenetic analysis of nCoV-2019 genomes. virological.org/t/phylodynamic-analysis-176-genomes-6-mar-2020/356
[7] Li et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med, 382: 1199-1207. doi: 10.1056/NEJMoa2001316

Genetic correlations among traits are ubiquitous in nature. However, we still have a limited understanding of the genetic architecture of trait correlations. Some genetic correlations among traits arise because of pleiotropy - single mutations or genotypes that have effects on multiple traits. Other genetic correlations among traits arise because of linkage among mutations that have independent effects on different traits. Teasing apart the differential effects of pleiotropy and linkage on trait correlations is difficult, because they result in very similar genetic patterns. However, understanding these differential effects gives important insights into how ubiquitous pleiotropy may be in nature.
In the preprint "Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies", Chebib and Guillaume [1] explore the conditions under which trait correlations caused by pleiotropy result in similar and different genetic patterns than trait correlations caused by linkage. Their main finding is that pleiotropic architectures result in higher trait correlations than do architectures in which completely linked mutations affect different traits. This results clarifies and goes against a previous theoretical study that predicted that pleiotropic architectures could not be distinguished from completely linked mutations that affect independent traits.
In genome-wide association studies (GWAS), it is difficult to know if a significant signal is a causal variant that truly affects the trait, a false positive neutral variant linked to a causal variant, or a false positive causal variant that affects a different trait but is significant because of trait correlations. In their study, Chebib and Guillaume [1] show that this latter category can be a common source of false positives in GWAS studies when mutations affecting different traits are linked. One of the main limitation of this aspect of their analysis is the lack of simulation of neutral loci, which would likely show even higher rates of false positives than reported in their study.
The main limitation in their study is the restrictive assumptions about the genetic architectures (e.g. all pairs of loci have a fixed recombination rate among them). In reality, new causal mutations that arise near another causal mutation may have higher or lower establishment probabilities depending on the direction of effects on the trait and the parameters for selection and demography. Their study still deserves a recommendation, however, because of the new insights it gives into the genetic architecture of trait correlations.

References

[1] Chebib, J. and Guillaume, F. (2019). Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies. bioRxiv, 656413, v3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/656413

In this study, Ishigohoka and colleagues tackle an important, yet often overlooked, question on the causes of genetic variation. While genome-wide patterns represent population structure, local variation is often associated with selection. Authors propose that an alternative cause for variation in individual loci is reduced recombination rate.

To test this hypothesis, authors perform local Principal Component Analysis (PCA) (Li & Ralph, 2019) to identify local deviations in population structure in the Eurasian blackcap (Sylvia atricapilla) (Ishigohoka et al. 2022). This approach is typically used to detect chromosomal rearrangements or any long region of linked loci (e.g., due to reduced recombination or selection) (Mérot et al. 2021). While other studies investigated the effect of low recombination on genetic variation (Booker et al. 2020), here authors provide a comprehensive analysis of the effect of recombination to local PCA patterns both in empirical and simulated data sets. Findings demonstrate that low recombination (and not selection) can be the sole explanatory variable for outlier windows. The study also describes patterns of genetic variation along the genome of Eurasian blackcaps, localising at least two polymorphic inversions (Ishigohoka et al. 2022).

Further investigations on the effect of model parameters (e.g., window sizes and thresholds for defining low-recombining regions), as well as the use of powerful neutrality tests are in need to clearly assess whether outlier regions experience selection and reduced recombination, and to what extent.

References

Booker, T. R., Yeaman, S., & Whitlock, M. C. (2020). Variation in recombination rate affects detection of outliers in genome scans under neutrality. Molecular Ecology, 29 (22), 4274–4279. https://doi.org/10.1111/mec.15501

Ishigohoka, J., Bascón-Cardozo, K., Bours, A., Fuß, J., Rhie, A., Mountcastle, J., Haase, B., Chow, W., Collins, J., Howe, K., Uliano-Silva, M., Fedrigo, O., Jarvis, E. D., Pérez-Tris, J., Illera, J. C., Liedvogel, M. (2022) Distinct patterns of genetic variation at low-recombining genomic regions represent haplotype structure. bioRxiv 2021.12.22.473882, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.12.22.473882

Li, H., & Ralph, P. (2019). Local PCA Shows How the Effect of Population Structure Differs Along the Genome. Genetics, 211 (1), 289–304. https://doi.org/10.1534/genetics.118.301747

Mérot, C., Berdan, E. L., Cayuela, H., Djambazian, H., Ferchaud, A.-L., Laporte, M., Normandeau, E., Ragoussis, J., Wellenreuther, M., & Bernatchez, L. (2021). Locally Adaptive Inversions Modulate Genetic Variation at Different Geographic Scales in a Seaweed Fly. Molecular Biology and Evolution, 38 (9), 3953–3971. https://doi.org/10.1093/molbev/msab143

The reliability of published scientific papers has been the topic of much recent discussion, notably in the biomedical sciences [1]. Although small sample size is regularly pointed as one of the culprits, big data can also be a concern. The advent of high-throughput sequencing, and the processing of sequence data by opaque bioinformatics workflows, mean that sequences with often high error rates are produced, and that exact but slow analyses are not feasible.
The troubles with bioinformatics arise from the increased complexity of the tools used by scientists, and from the lack of incentives and/or skills from authors (but also reviewers and editors) to make sure of the quality of those tools. As a much discussed example, a bug in the widely used PLINK software [2] has been pointed as the explanation [3] for incorrect inference of selection for increased height in European Human populations [4].
High-throughput sequencing often generates high rates of genotyping errors, so that the development of bioinformatics tools to assess the quality of data and correct them is a major issue. The work of Gillingham et al. [5] contributes to the latter goal. In this work, the authors propose a new bioinformatics workflow (ACACIA) for performing genotyping analysis of multigene complexes, such as self-incompatibility genes in plants, major histocompatibility genes (MHC) in vertebrates, and homeobox genes in animals, which are particularly challenging to genotype in non-model organisms. PCR and sequencing of multigene families generate artefacts, hence spurious alleles. A key to Gillingham et al.‘ s method is to call candidate genes based on Oligotyping, a software pipeline originally conceived for identifying variants from microbiome 16S rRNA amplicons [6]. This allows to reduce the number of false positives and the number of dropout alleles, compared to previous workflows.
This method is not based on an explicit probability model, and thus it is not conceived to provide a control of the rate of errors as, say, a valid confidence interval should (a confidence interval with coverage c for a parameter should contain the parameter with probability c, so the error rate 1- c is known and controlled by the user who selects the value of c). However, the authors suggest a method to adapt the settings of ACACIA to each application.
To compare and validate the new workflow, the authors have constructed new sets of genotypes representing different extents copy number variation, using already known genotypes from chicken MHC. In such conditions, it was possible to assess how many alleles are not detected and what is the rate of false positives. Gillingham et al. additionally investigated the effect of using non-optimal primers. They found better performance of ACACIA compared to a preexisting pipeline, AmpliSAS [7], for optimal settings of both methods. However, they do not claim that ACACIA will always be better than AmpliSAS. Rather, they warn against the common practice of using the default settings of the latter pipeline. Altogether, this work and the ACACIA workflow should allow for better ascertainment of genotypes from multigene families.

References

[1] Ioannidis, J. P. A, Greenland, S., Hlatky, M. A., Khoury, M. J., Macleod, M. R., Moher, D., Schulz, K. F. and Tibshirani, R. (2014) Increasing value and reducing waste in research design, conduct, and analysis. The Lancet, 383, 166-175. doi: 10.1016/S0140-6736(13)62227-8
[2] Chang, C. C., Chow, C. C., Tellier, L. C. A. M., Vattikuti, S., Purcell, S. M. and Lee, J. J. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7, s13742-015-0047-8. doi: 10.1186/s13742-015-0047-8
[3] Robinson, M. R. and Visscher, P. (2018) Corrected sibling GWAS data release from Robinson et al. http://cnsgenomics.com/data.html
[4] Field, Y., Boyle, E. A., Telis, N., Gao, Z., Gaulton, K. J., Golan, D., Yengo, L., Rocheleau, G., Froguel, P., McCarthy, M.I . and Pritchard J. K. (2016) Detection of human adaptation during the past 2000 years. Science, 354(6313), 760-764. doi: 10.1126/science.aag0776
[5] Gillingham, M. A. F., Montero, B. K., Wihelm, K., Grudzus, K., Sommer, S. and Santos P. S. C. (2020) A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system. bioRxiv 638288, ver. 3 peer-reviewed and recommended by Peer Community In Evolutionary Biology. doi: 10.1101/638288
[6] Eren, A. M., Maignien, L., Sul, W. J., Murphy, L. G., Grim, S. L., Morrison, H. G., and Sogin, M.L. (2013) Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods in Ecology and Evolution 4(12), 1111-1119. doi: 10.1111/2041-210X.12114
[7] Sebastian, A., Herdegen, M., Migalska, M. and Radwan, J. (2016) AMPLISAS: a web server for multilocus genotyping using next‐generation amplicon sequencing data. Mol Ecol Resour, 16, 498-510. doi: 10.1111/1755-0998.12453