Molecular epidemiology, phylogeny and evolution of the filarial nematode Wuchereria bancrofti

Abstract

Wuchereria bancrofti (Wb) is the most widely distributed of the three nematodes known to cause lymphatic filariasis (LF), the other two being Brugia malayi and Brugia timori. Current tools available to monitor LF are limited to diagnostic tests targeting DNA repeats, filarial antigens, and anti-filarial antibodies. While these tools are useful for detection and surveillance, elimination programs have yet to take full advantage of molecular typing for inferring infection history, strain fingerprinting, and evolution. To date, molecular typing approaches have included whole mitochondrial genomes, genotyping, targeted sequencing, and random amplified polymorphic DNA (RAPDs). These studies have revealed much about Wb biology. For example, in one study in Papua New Guinea researchers identified 5 major strains that were widespread and many minor strains some of which exhibit geographic stratification. Genome data, while rare, has been utilized to reconstruct evolutionary relationships among taxa of the Onchocercidae (the clade of filarial nematodes) and identify gene synteny. Their phylogeny reveals that speciation from the common ancestor of both B. malayi and Wb occurred around 5-6 millions years ago with shared ancestry to other filarial nematodes as recent as 15 million years ago. These discoveries hold promise for gene discovery and identifying drug targets in species that are more amenable to in vivo experiments. Continued technological developments in whole genome sequencing and data analysis will likely replace many other forms of molecular typing, multiplying the amount of data available on population structure, genetic diversity, and phylogenetics. Once widely available, the addition of population genetic data from genomic studies should hasten the elimination of LF parasites like Wb. Infectious disease control programs have benefited greatly from population genetics data and recently from population genomics data. However, while there is currently a surplus of data for diseases like malaria and HIV, there is a scarcity of this data for filarial nematodes. With the falling cost of genome sequencing, research on filarial nematodes could benefit from the addition of population genetics statistics and phylogenetics especially in dealing with elimination programs. A comprehensive review focusing on population genetics of filarial nematode does not yet exist. Here our goal is to provide a current overview of the molecular epidemiology of W. bancrofti (Wb) the primary causative agent of LF. We begin by reviewing studies utilizing molecular typing techniques with specific focus on genomic and population datasets. Next, we used whole mitochondrial genome data to construct a phylogeny and examine the evolutionary history of the Onchocercidae. Then, we provide a perspective to aid in understanding how population genetic techniques translate to modern epidemiology. Finally, we introduce the concept of genomic epidemiology and provide some examples that will aid in future studies of Wb.

Keywords: Filariasis; Genetics; Genomics; Molecular epidemiology; Transmission; Wuchereria bancrofti.

Figure 1. Mitochondrial genome phylogeny of filarial nematodes

The phylogeny was truncated to emphasize relationships within the filarial worms (Onchocercidae) while providing out-group information from other well-known species. The phylogeny was constructed using the program BEAST 1.7.5 (Drummond et al., 2012) with divergence times calibrated using the mitochondrial mutation rate of Pristionchus pacificus (Molnar et al., 2011) and the estimated divergence time of 80–110 million years between Caenorhabditis elegans and C. briggsae (Stein et al., 2003). Divergence times are placed on each node as a decimal with a coarse scale on the bottom-horizontal of the phylogeny. All times are represented in millions of generations. We reconstructed host preferences by way of ancestral trait inference in BEAST 1.75, after obtaining a well-supported phylogeny (all nodes >95% unless otherwise noted), For each branch and node, colors denote the host preference as well as the inferred state of the most recent common ancestor. Colors correspond to: pink=mammals, red=birds, yellow=plants, green=free-living/bacterial feeders, blue=fish. We note that there is an over representation of mammalian parasitic nematodes on the phylogeny biasing deep nodes preferences. Colors of external branches are therefore extended only as deep as 95% confidence surrounding the ancestral node.

Figure 2. Simple genetic model of parasite transmission

Two pieces of important information are gained by sequencing from an infrapopulation. First, we gain information on the number of strains in an infection (represented as different patterned circles). Second, we gain information from the frequency of any specific strain in an infrapopulation (represented by the size of the circles). With this data in hand we construct a simple model of Wb transmission. In this model thin horizontal lines connect similar strains and heavy vertical arrows represent transmission event. The total height of each genealogy (in time) reflects the genetic similarity between two infrapopulations. A) In high transmission areas, the infrapopulations are more genetically similar and therefore the genealogy is shorter. B) In a moderate transmission area, the populations are less similar and the genealogy will be longer.

Figure 3. Inferring parasite dispersal and human migration

To estimate human migration and subsequently parasite dispersal, we illustrate two different methods (B) K-means clustering and (C) admixture analysis (C). The map in (A) denotes the sampling localities as well as the color designated to each community. Each infrapopulation is then characterized by genotype and allele frequency. B) The optimum number of clusters for the K-means analysis was four, which was found to minimize the genetic variance. Infrapopulations retain the color of their original communities, while background color clarifies clusters. C) For the admixture analysis, individuals are displayed along the horizontal axis with the probability of assignment to a cluster, denoted as percent color. Here the best answer was K=5 clusters.