Publications by authors named "Joe Wan"

4 Publications

  • Page 1 of 1

A meta-analysis of global fungal distribution reveals climate-driven patterns.

Nat Commun 2019 11 13;10(1):5142. Epub 2019 Nov 13.

Laboratory of Environmental Microbiology, Institute of Microbiology of the Czech Academy of Sciences, Vídeňská 1083, 14220, Praha 4, Czech Republic.

The evolutionary and environmental factors that shape fungal biogeography are incompletely understood. Here, we assemble a large dataset consisting of previously generated mycobiome data linked to specific geographical locations across the world. We use this dataset to describe the distribution of fungal taxa and to look for correlations with different environmental factors such as climate, soil and vegetation variables. Our meta-study identifies climate as an important driver of different aspects of fungal biogeography, including the global distribution of common fungi as well as the composition and diversity of fungal communities. In our analysis, fungal diversity is concentrated at high latitudes, in contrast with the opposite pattern previously shown for plants and other organisms. Mycorrhizal fungi appear to have narrower climatic tolerances than pathogenic fungi. We speculate that climate change could affect ecosystem functioning because of the narrow climatic tolerances of key fungal taxa.
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November 2019

Resource-ratio theory predicts mycorrhizal control of litter decomposition.

New Phytol 2019 08;223(3):1595-1606

Department of Environmental Systems Science, ETH Zürich, 8092, Zürich, Switzerland.

Ecosystems with ectomycorrhizal plants have high soil carbon : nitrogen ratios, but it is not clear why. The Gadgil effect, where competition between ectomycorrhizal and saprotrophic fungi for nitrogen slows litter decomposition, may increase soil carbon. However, experimental evidence for the Gadgil effect is equivocal. Here, we apply resource-ratio theory to assess whether interguild fungal competition for different forms of organic nitrogen can affect litter decomposition. We focus on variation in resource input ratios and fungal resource use traits, and evaluate our model's predictions by synthesizing prior experimental literature examining ectomycorrhizal effects on litter decomposition. In our model, resource input ratios determined whether ectomycorrhizal fungi suppressed saprotrophic fungi. Recalcitrant litter inputs favored the former over the latter, allowing the Gadgil effect only when such inputs predominated. Although ectomycorrhizal fungi did not always hamper litter decomposition, ectomycorrhizal nitrogen uptake always increased carbon : nitrogen ratios in litter. Our meta-analysis of empirical studies supports our theoretical results: ectomycorrhizal fungi appear to slow decomposition of leaf litter only in forests where litter inputs are highly recalcitrant. We thus find that the specific contribution of the Gadgil effect to high soil carbon : nitrogen ratios in ectomycorrhizal ecosystems may be smaller than predicted previously.
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August 2019

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis.

Bioinformatics 2018 04;34(8):1411-1413

Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.

Motivation: Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Results: The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.

Availability And Implementation: and

Contact: [email protected]
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April 2018


Pac Symp Biocomput 2016 ;21:144-55

Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA, 94305., USA,

The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.
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October 2016