Multiple avirulence paralogues in cereal powdery mildew fungi may contribute to parasite fitness and defeat of plant resistance

Powdery mildews, obligate biotrophic fungal parasites on a wide range of important crops, can be controlled by plant resistance (R) genes, but these are rapidly overcome by parasite mutants evading recognition. It is unknown how this rapid evolution occurs without apparent loss of parasite fitness. R proteins recognize avirulence (AVR) molecules from parasites in a gene-for-gene manner and trigger defense responses. We identify AVRa10 and AVRk1 of barley powdery mildew fungus, Blumeria graminis f sp hordei (Bgh), and show that they induce both cell death and naccessibility when transiently expressed in Mla10 and Mlk1 barley (Hordeum vulgare) varieties, respectively. In contrast with other reported fungal AVR genes, AVRa10 and AVRk1 encode proteins that lack secretion signal peptides and enhance infection success on susceptible host plant cells. AVRa10 and AVRk1 belong to a large family with mayor que30 paralogues in the genome of Bgh, and homologous sequences are present in other formae speciales of the fungus infecting other grasses. Our findings imply that the mildew fungus has a repertoire of AVR genes, which may function as effectors and contribute to parasite virulence. Multiple copies of related but distinct AVR effector paralogues might enable populations of Bgh to rapidly overcome host R genes while maintaining virulence.

Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears

Background: There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist’s time. Objective: This research tests the feasibility of a crowdsourced approach to malaria image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. Methods: The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. Data were collected through the MalariaSpot website. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. In the game, players had to find and tag as many parasites as possible in 1 minute. In the event that players found all the parasites present in the image, they were presented with a new image. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. Results: Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. Results revealed that combining 22 games from nonexpert players achieved a parasite counting accuracy higher than 99%. This performance could be obtained also by combining 13 games from players trained for 1 minute. Exhaustive computations measured the parasite counting accuracy for all players as a function of the number of games considered and the experience of the players. In addition, we propose a mathematical equation that accurately models the collective parasite counting performance. Conclusions: This research validates the online gaming approach for crowdsourced counting of malaria parasites in images of thick blood films. The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists. This experiment illustrates the potential of the crowdsourced gaming approach for performing routine malaria parasite quantification, and more generally for solving biomedical image analysis problems, with future potential for telediagnosis related to global health challenges.