Louis du Plessis

The what, where, how and why of gene ontology—a primer for bioinformaticians

With high-throughput technologies providing vast amounts of data, it has become more important to provide systematic, quality annotations. The Gene Ontology (GO) project is the largest resource for cataloguing gene function. Nonetheless, its use is not yet ubiquitous and is still fraught with pitfalls. In this review, we provide a short primer to the GO for bioinformaticians. We summarize important aspects of the structure of the ontology, describe sources and types of functional annotations, survey measures of GO annotation similarity, review typical uses of GO and discuss other important considerations pertaining to the use of GO in bioinformatics applications.

A depauperate immune repertoire precedes evolution of sociality in bees

BackgroundSociality has many rewards, but can also be dangerous, as high population density and low genetic diversity, common in social insects, is ideal for parasite transmission. Despite this risk, honeybees and other sequenced social insects have far fewer canonical immune genes relative to solitary insects. Social protection from infection, including behavioral responses, may explain this depauperate immune repertoire. Here, based on full genome sequences, we describe the immune repertoire of two ecologically and commercially important bumblebee species that diverged approximately 18 million years ago, the North American Bombus impatiens and European Bombus terrestris.ResultsWe find that the immune systems of these bumblebees, two species of honeybee, and a solitary leafcutting bee, are strikingly similar. Transcriptional assays confirm the expression of many of these genes in an immunological context and more strongly in young queens than males, affirming Bateman’s principle of greater investment in female immunity. We find evidence of positive selection in genes encoding antiviral responses, components of the Toll and JAK/STAT pathways, and serine protease inhibitors in both social and solitary bees. Finally, we detect many genes across pathways that differ in selection between bumblebees and honeybees, or between the social and solitary clades.ConclusionsThe similarity in immune complement across a gradient of sociality suggests that a reduced immune repertoire predates the evolution of sociality in bees. The differences in selection on immune genes likely reflect divergent pressures exerted by parasites across social contexts.

Gene expression differences underlying genotype-by-genotype specificity in a host-parasite system.

Significance Some genotypes of parasites can infect some genotypes of hosts but not others, whereas hosts also vary in susceptibility to a given parasite genotype. Variation in genes important for defenses against parasites could produce this specificity. Here, we find that variation in gene expression depended on both the genotype of the host and the genotype of the parasite. Moreover, we found that bumblebees that were exposed to infectious genotypes of a trypanosome parasite had low gene expression of immune genes but upregulation of genes that control expression. A poorly infecting parasite genotype, however, induced expression of immune genes. These results suggest that variation in the regulation of gene expression may also contribute to producing genotype-by-genotype specificity. In many systems, host–parasite evolutionary dynamics have led to the emergence and maintenance of diverse parasite and host genotypes within the same population. Genotypes vary in key attributes: Parasite genotypes vary in ability to infect, host genotypes vary in susceptibility, and infection outcome is frequently the result of both parties’ genotypic identities. These host–parasite genotype-by-genotype (GH × GP) interactions influence evolutionary and ecological dynamics in important ways. Interactions can be produced through genetic variation; however, here, we assess the role of variable gene expression as an additional source of GH × GP interactions. The bumblebee Bombus terrestris and its trypanosome gut parasite Crithidia bombi are a model system for host–parasite matching. Full-transcriptome sequencing of the bumblebee host revealed that different parasite genotypes indeed induce fundamentally different host expression responses and host genotypes vary in their responses to the infecting parasite genotype. It appears that broadly and successfully infecting parasite genotypes lead to reduced host immune gene expression relative to unexposed bees but induce the expression of genes responsible for controlling gene expression. Contrastingly, a poorly infecting parasite genotype induced the expression of immunologically important genes, including antimicrobial peptides. A targeted expression assay confirmed the transcriptome results and also revealed strong host genotype effects. In all, the expression of a number of genes depends on the host genotype and the parasite genotype and the interaction between both host and parasite genotypes. These results suggest that alongside sequence variation in coding immunological genes, variation that controls immune gene expression can also produce patterns of host–parasite specificity.

Insights into the Early Epidemic Spread of Ebola in Sierra Leone Provided by Viral Sequence Data

Background and Methodology: The current Ebola virus epidemic in West Africa has been spreading at least since December 2013. The first confirmed case of Ebola virus in Sierra Leone was identified on May 25. Based on viral genetic sequencing data from 72 individuals in Sierra Leone collected between the end of May and mid June, we utilize a range of phylodynamic methods to estimate the basic reproductive number (R0). We additionally estimate the expected lengths of the incubation and infectious periods of the virus. Finally, we use phylogenetic trees to examine the role played by population structure in the epidemic. Results: The median estimates of R0 based on sequencing data alone range between 1.65-2.18, with the most plausible model yielding a median R0 of 2.18 (95% HPD 1.24-3.55). Importantly, our results indicate that, at least until mid June, relief efforts in Sierra Leone were ineffective at lowering the effective reproductive number of the virus. We estimate the expected length of the infectious period to be 2.58 days (median; 95% HPD 1.24-6.98). The dataset appears to be too small in order to estimate the incubation period with high certainty (median expected incubation period 4.92 days; 95% HPD 2.11-23.20). While our estimates of the duration of infection tend to be smaller than previously reported, phylodynamic analyses support a previous estimate that 70% of cases were observed and included in the present dataset. The dataset is too small to show a particular population structure with high significance, however our preliminary analyses suggest that half the population is spreading the virus with an R0 well above 2, while the other half of the population is spreading with an R0 below 1. Conclusions: Overall we show that sequencing data can robustly infer key epidemiological parameters. Such estimates inform public health officials and help to coordinate effective public health efforts. Thus having more sequencing data available for the ongoing Ebola virus epidemic and at the start of new outbreaks will foster a quick understanding of the dynamics of the pathogen.

Getting to the root of epidemic spread with phylodynamic analysis of genomic data.

When epidemiological and evolutionary dynamics occur on similar timescales, pathogen genomes sampled from infected hosts carry a signature of the dynamics of epidemic spread. Phylodynamic inference methods aim to extract this signature from genetic data. We discuss the contribution of phylodynamics toward understanding the 2014 West African Ebola virus epidemic.

Epidemic establishment and cryptic transmission of Zika virus in Brazil and the Americas

Zika virus (ZIKV) transmission in the Americas was first confirmed in May 2015 in Northeast Brazil1. Brazil has the highest number of reported ZIKV cases worldwide (>200,000 by 24 Dec 20162) as well as the greatest number of cases associated with microcephaly and other birth defects (2,366 confirmed cases by 31 Dec 20162). Following the initial detection of ZIKV in Brazil, 47 countries and territories in the Americas have reported local ZIKV transmission, with 22 of these reporting ZIKV-associated severe disease3. Yet the origin and epidemic history of ZIKV in Brazil and the Americas remain poorly understood, despite the value of such information for interpreting past trends in reported microcephaly. To address this we generated 53 complete or partial ZIKV genomes, mostly from Brazil, including data generated by the ZiBRA project – a mobile genomics lab that travelled across Northeast Brazil in 2016. One sequence represents the earliest confirmed ZIKV infection in Brazil. Joint analyses of viral genomes with ecological and epidemiological data estimate that the ZIKV epidemic first became established in NE Brazil by March 2014 and likely disseminated from there, both nationally and internationally, before the first detection of ZIKV in the Americas. Estimated dates of the international spread of ZIKV from Brazil coincide with periods of high vector suitability in recipient regions and indicate the duration of pre-detection cryptic transmission in those regions. NE Brazil’s role in the establishment of ZIKV in the Americas is further supported by geographic analysis of ZIKV transmission potential and by estimates of the virus’ basic reproduction number. One Sentence Summary Virus genomes reveal the establishment of Zika virus in Northeast Brazil and the Americas, and provide an appropriate timeframe for baseline (pre-Zika) microcephaly in different regions.

How good are statistical models at approximating complex fitness landscapes

Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.

The genomes of Crithidia bombi and C. expoeki, common parasites of bumblebees.

Trypanosomatids (Trypanosomatidae, Kinetoplastida) are flagellated protozoa containing many parasites of medical or agricultural importance. Among those, Crithidia bombi and C. expoeki, are common parasites in bumble bees around the world, and phylogenetically close to Leishmania and Leptomonas. They have a simple and direct life cycle with one host, and partially castrate the founding queens greatly reducing their fitness. Here, we report the nuclear genome sequences of one clone of each species, extracted from a field-collected infection. Using a combination of Roche 454 FLX Titanium, Pacific Biosciences PacBio RS, and Illumina GA2 instruments for C. bombi, and PacBio for C. expoeki, we could produce high-quality and well resolved sequences. We find that these genomes are around 32 and 34 MB, with 7,808 and 7,851 annotated genes for C. bombi and C. expoeki, respectively—which is somewhat less than reported from other trypanosomatids, with few introns, and organized in polycistronic units. A large fraction of genes received plausible functional support in comparison primarily with Leishmania and Trypanosoma. Comparing the annotated genes of the two species with those of six other trypanosomatids (C. fasciculata, L. pyrrhocoris, L. seymouri, B. ayalai, L. major, and T. brucei) shows similar gene repertoires and many orthologs. Similar to other trypanosomatids, we also find signs of concerted evolution in genes putatively involved in the interaction with the host, a high degree of synteny between C. bombi and C. expoeki, and considerable overlap with several other species in the set. A total of 86 orthologous gene groups show signatures of positive selection in the branch leading to the two Crithidia under study, mostly of unknown function. As an example, we examined the initiating glycosylation pathway of surface components in C. bombi, finding it deviates from most other eukaryotes and also from other kinetoplastids, which may indicate rapid evolution in the extracellular matrix that is involved in interactions with the host. Bumble bees are important pollinators and Crithidia-infections are suspected to cause substantial selection pressure on their host populations. These newly sequenced genomes provide tools that should help better understand host-parasite interactions in these pollinator pathogens.

Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico

Summary The Zika virus (ZIKV) epidemic in the Americas established ZIKV as a major public health threat and uncovered its association with severe diseases, including microcephaly. However, genetic epidemiology in some at-risk regions, particularly Central America and Mexico, remains limited. We report 61 ZIKV genomes from this region, generated using metagenomic sequencing with ZIKV-specific enrichment, and combine phylogenetic, epidemiological, and environmental data to reconstruct ZIKV transmission. These analyses revealed multiple independent ZIKV introductions to Central America and Mexico. One introduction, likely from Brazil via Honduras, led to most infections and the undetected spread of ZIKV through the region from late 2014. Multiple lines of evidence indicate biannual peaks of ZIKV transmission in the region, likely driven by varying local environmental conditions for mosquito vectors and herd immunity. The spatial and temporal heterogeneity of ZIKV transmission in Central America and Mexico challenges arbovirus surveillance and disease control measures.

Impact of the tree prior on estimating clock rates during epidemic outbreaks

Significance Genetic sequencing data of pathogens allow one to quantify the evolutionary rate together with epidemiological dynamics using Bayesian phylodynamic methods. Such tools are particularly useful for obtaining a timely understanding of newly emerging epidemic outbreaks. During the West African Ebola virus disease epidemic, an unusually high evolutionary rate was initially estimated, promoting discussions regarding the potential danger of the strain quickly evolving into an even more dangerous virus. We show here that such high evolutionary rates are not necessarily real but can stem from methodological biases in the analyses. While most analyses of epidemic outbreak data are performed such that these biases may be present, we suggest a solution to overcome these biases in the future. Bayesian phylogenetics aims at estimating phylogenetic trees together with evolutionary and population dynamic parameters based on genetic sequences. It has been noted that the clock rate, one of the evolutionary parameters, decreases with an increase in the sampling period of sequences. In particular, clock rates of epidemic outbreaks are often estimated to be higher compared with the long-term clock rate. Purifying selection has been suggested as a biological factor that contributes to this phenomenon, since it purges slightly deleterious mutations from a population over time. However, other factors such as methodological biases may also play a role and make a biological interpretation of results difficult. In this paper, we identify methodological biases originating from the choice of tree prior, that is, the model specifying epidemiological dynamics. With a simulation study we demonstrate that a misspecification of the tree prior can upwardly bias the inferred clock rate and that the interplay of the different models involved in the inference can be complex and nonintuitive. We also show that the choice of tree prior can influence the inference of clock rate on real-world Ebola virus (EBOV) datasets. While commonly used tree priors result in very high clock-rate estimates for sequences from the initial phase of the epidemic in Sierra Leone, tree priors allowing for population structure lead to estimates agreeing with the long-term rate for EBOV.