The interaction among evolutionary forces in the pathogenic fungus Mycosphaerella graminicola

Abstract

The population genetic dynamic of a species is driven by interactions among mutation, migration, drift, mating system, and selection, but it is rare to have sufficient empirical data to estimate values for all of these forces and to allow comparison of the relative magnitudes of these evolutionary forces. We combined data from a mark-release-recapture experiment, extensive population surveys, and computer simulations to evaluate interactions among these evolutionary forces in the pathogenic fungus Mycosphaerella graminicola. The results from these studies showed that, on average, the immigration rate was 0.027, the fraction of outcrossing individuals was 0.035, and the selection coefficient associated with immigrants was 0.106 each generation. We also estimated that effective population sizes for this fungus were larger than 24,000 and the mutation rate for the RFLP markers used in surveys and field experiments was ∼4 × 10⁻⁵. Computer simulations based on these estimates indicate that, on average, the global population of M. graminicola has reached equilibrium. Population genetic parameters including number of alleles, gene diversity, and population subdivision estimated from the computer simulations were surprisingly close to empirical estimates. Simulations also revealed that random drift is the major evolutionary force decreasing genetic variation in this fungus, followed by natural selection. The major force adding to genetic variation was mutation, followed by gene flow and sexual recombination. Gene flow played the leading role in decreasing population subdivision while natural selection was the major factor increasing population subdivision.

Introduction

A primary goal in population genetic studies of pathogens is to reveal the evolutionary history of parasitic species by understanding the processes and mechanisms through which heritable changes have occurred (Hartl and Clark, 1997). The majority of population genetic studies in fungal pathogens to date have focused on population genetic structure (e.g., Douhan et al., 2002; Gavino and Fry, 2002; Sampaio et al., 2003) as a starting point to elucidate the interactions among mutation, migration, drift, mating system, and selection. Though the theoretical consequences of each evolutionary force on population genetic dynamics are well understood, the magnitudes and relative contributions of these evolutionary forces have rarely been quantified and validated empirically and comprehensively within the same pathogen species.

A secondary goal of population genetics research on pathogens is to predict their evolutionary potential (Cowen, 2001; McDonald and Linde, 2002) and, in some cases, to manipulate their patterns of evolution. From this perspective, population genetics study of pathogens could have some important clinical and agricultural applications and is the main philosophy upon which Darwinian medicine (LeGrand and Brown, 2002; Williams and Nesse, 1991) is based.

To apply knowledge of pathogen population genetics in Darwinian medicine, quantitative knowledge of each of the five evolutionary forces is desirable. The majority of research on interactions among evolutionary forces currently is based on arbitrary data sets (Hastings, 2001; Hastings and Mackinnon, 1998; Reinhold, 2002; Zeyl and DeVisser, 2001) and usually considers the interaction among only two or three evolutionary forces. Evaluation of the relative contribution of each evolutionary force to the formation and maintenance of genetic variation and prediction of the evolutionary potential of a pathogen species based on empirically derived values of population genetic parameters is relatively rare.

Experimental estimation of the evolutionary forces in natural systems is very difficult and has traditionally been limited to model genetic organisms, such as Drosophila (Akashi, 1997; Chavarrias et al., 2001; Przeworski et al., 2001; Vazquez et al., 2000) and Escherichia coli (Berg, 1996; Imhof and Schlotterer, 2001; Souza et al., 1997; Vulic et al., 1999). Many biological and ecological processes can introduce stochastic “noise” that influences the interpretation of experimental estimations. As a result, experimental studies conducted in controlled or semi-controlled agricultural ecosystems may offer an opportunity to quantify the evolutionary forces with a better precision.

Agroecosystems involving plants have less environmental variation as a result of intensive cultivation practices. Experimental studies with plant pathogens can be repeated through time and space under controlled or semi-controlled conditions and experimental materials can be manipulated without ethical or other constraints. Pathogenic microorganisms such as fungi offer an additional advantage because they have short generation times, wide geographic distributions, and are easy to handle in large numbers.

Mycosphaerella graminicola (Fuckel) Schroeter (anamorph Septoria tritici Rob. ex Desm.) is a filamentous fungus distributed globally across a wide range of geographic niches (Eyal, 1999; King et al., 1983). It causes one of the most ancient diseases of wheat (Shipton et al., 1971). The life cycle of this pathogen includes both asexual and sexual reproduction. Asexual pycnidiospores are disseminated from plant to plant via rain-splash, hence their potential for long-distance movement during an epidemic is limited (Bannon and Cooke, 1998). However, the asexual stage has the potential for long-distance dissemination through international trading of infected seeds and grain (Brokenshire, 1975). Ascospores produced by the sexual stage are dispersed by wind and have the potential to be blown over a considerable distance (Sanderson, 1972).

The genetic structure of M. graminicola populations has been studied for over a decade (e.g., Schnieder et al., 2001; Zhan et al., 2003). Through the use of molecular genetic markers (RFLPs, DNA fingerprints, and DNA sequencing) and field experiments, much knowledge related to the population genetic structure and evolutionary potential of this fungus has accumulated (Boeger et al., 1993; Chen et al., 1994; Chen and McDonald, 1996; McDonald and Martinez, 1990a, McDonald and Martinez, 1990b; McDonald et al., 1996; Zhan et al., 1998, Zhan et al., 2000, Zhan et al., 2001, Zhan et al., 2002a, Zhan et al., 2002b). In this manuscript we integrate the findings from 14 years of research to: (1) quantify the evolutionary forces governing the population dynamics of M. graminicola; (2) compare empirical estimates of population genetic parameters with estimates obtained from computer simulations; and (3) evaluate the relative significance of each evolutionary force on the population dynamics and evolution of this fungus.