Tropospheric winds from northeastern China carry the etiologic agent of Kawasaki disease from its source to Japan

Abstract

Evidence indicates that the densely cultivated region of northeastern China acts as a source for the wind-borne agent of Kawasaki disease (KD). KD is an acute, coronary artery vasculitis of young children, and still a medical mystery after more than 40 y. We used residence times from simulations with the flexible particle dispersion model to pinpoint the source region for KD. Simulations were generated from locations spanning Japan from days with either high or low KD incidence. The postepidemic interval (1987-2010) and the extreme epidemics (1979, 1982, and 1986) pointed to the same source region. Results suggest a very short incubation period (<24 h) from exposure, thus making an infectious agent unlikely. Sampling campaigns over Japan during the KD season detected major differences in the microbiota of the tropospheric aerosols compared with ground aerosols, with the unexpected finding of the Candida species as the dominant fungus from aloft samples (54% of all fungal strains). These results, consistent with the Candida animal model for KD, provide support for the concept and feasibility of a windborne pathogen. A fungal toxin could be pursued as a possible etiologic agent of KD, consistent with an agricultural source, a short incubation time and synchronized outbreaks. Our study suggests that the causative agent of KD is a preformed toxin or environmental agent rather than an organism requiring replication. We propose a new paradigm whereby an idiosyncratic immune response, influenced by host genetics triggered by an environmental exposure carried on winds, results in the clinical syndrome known as acute KD.

Keywords: FLEXPART; agriculture; cereal croplands; heart disease; northeastern China source.

Fig. 1.

Upstream grid cell locations registering residence times over 30 s for the ensemble of FLEXPART 10-d backward simulations (light brown) for dates within the three epidemics (1979, 1982, and 1986) when KD cases were at or above the 95% threshold of cases (threshold calculated for the entire timespan, 1977–2010). The ensemble represents a total of 257 dates. A 0.5° grid scale was used (latitude, longitude). Brown dots denote crops according to the land cover type yearly climate modeling grid (CMG) datasets with 0.05° resolution from the NASA Land Processes Distributed Active Archive Center (LP DAAC, Sioux Falls, SD), ASTER L1B (32). Grid cells with dots have at least 50% or more subgrids as crops or 100% subgrids as mosaic (mosaic representing crops + natural vegetation).

Fig. 2.

Daily time series of KD date of onset for patients in Tokyo (blue) and Kanagawa (red) during the epidemics of 1982 (see Fig. S4 for 1986). Axes display cases (Y) and day since epidemic onset (X). See SDC (11) analysis between the KD datasets for Tokyo and Kanagawa, as well as for all cities in the Greater Tokyo Area in Data and Methods and Table S1. Maximum overall correlation is attained in this 1982 epidemic, with over 75% of the total KD variability synchronized between Tokyo and Kanagawa during at least 3 wk.

Fig. 3.

Areas of (A) high–low or (B) low–high differences in the residence time between sets of cases with high KD and low KD occurrences in the postepidemic interval. Residence time differences higher than 30 s are shown for Tokyo (blue), Sapporo (green), and Nagoya (yellow). The ensembles included have been generated averaging residence time data corresponding to lags of 1–3 d, which appears to capture all of the potential range of values for the KD incubation time. The total number of dates used in the simulations was 1,344. Red areas indicate overlapping high-residence time differences from two cities (light) or three cities (dark) residence time anomaly areas.

Fig. 4.

Epidemic evolution 30 km from the initial site of infection, for β = 10 d⁻¹ and γ = 0.1 d⁻¹. The maximum number of infected denotes the time at which the disease reaches the next town (on average after 5.66 d).

Fig. 5.

Differences in the mycobiome distribution from tropospheric and surface-level aerosols. (A) Fungal 18S rRNA gene PCR demonstrates amplification products in flight and surface filters. DNA extracted from soil was used as a positive control for fungal amplification. (B) Pie charts demonstrating the percent abundance of fungal taxa identified from clone library sequencing (100 clones per filter) of 18S rRNA gene amplification products from flight and surface filters. Candida sp. sequences accounted for 54% of total flight filter sequences, whereas Aspergillus sp. sequences accounted for 100% of surface filter sequences.