Next Meeting:

Pacific: FEBRUARY 8th 2022

Premier Session


🇺🇸  20.30pm New York (Day before)
🇬🇧  1.30am London
🇯🇵  10.30am Tokyo

Second Session


🇺🇸 10.00am New York
🇬🇧 15.00pm London
🇯🇵 00.00 am Tokyo (Day After)



Topic models for interpretable multidomain microbiome data

prof Susan Holmes

Trained in the French school of Data Analysis in Montpellier, Susan Holmes has been working in non-parametric multivariate statistics applied to Biology since 1985. She has taught at MIT, Harvard and was an Associate Professor of Biometry at Cornell before moving to Stanford in 1998. Her theoretical interests include applied probability, Monte Carlo Markov chains, Graph Limit Theory, Differential Geometry, and the topology of the space of Phylogenetic Trees. 

She uses computational statistics, in particular, nonparametric computer-intensive methods to draw inferences about many complex biological phenomena, mostly from the areas of Immunology, Cancer Biology, and Microbial Ecology. She is interested in integrating the information provided by phylogenetic trees, community interaction graphs, and metabolic networks with sequencing data and clinical covariates, mostly from the areas of Immunology, Cancer Biology, and Microbial Ecology. Her current focus is improving the statistical analyses and reproducibility of data in perturbation studies of the Human Microbiome.


Autism-related dietary preferences mediate autism-gut microbiome associations

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

Chloe X Yap

Mater Research Institute, University of Queensland; Institute for Molecular Bioscience, University of Queensland

Using strain-resolved analysis to identify contamination in metagenomics data

Metagenomics analyses can be negatively impacted by DNA contamination. While external sources of contamination such as DNA extraction kits have been widely reported and investigated, contamination originating within the study itself remains underreported. Here we applied high-resolution strain-resolved analyses to identify contamination in two large-scale clinical metagenomics datasets. By mapping strain sharing to DNA extraction plates, we identified well-to-well contamination in both negative controls and biological samples in one dataset. Such contamination is more likely to occur among samples that are on the same or adjacent columns or rows of the extraction plate than samples that are far apart. Our strain-resolved workflow also reveals the presence of externally derived contamination, primarily in the other dataset. Overall in both datasets, contamination is more significant in samples with lower biomass. Our work demonstrates that genome-resolved strain tracking, with its essentially genome-wide nucleotide-level resolution, can be used to detect contamination in sequencing-based microbiome studies. Our results underscore the value of strain-specific methods to detect contamination and the critical importance of looking for contamination beyond negative and positive controls.

Yue Clare Lou

University of California, Berkeley

The genetic and ecological landscape of plasmids in the human gut

Plasmids are mobile genetic elements that often carry key determinants of fitness, yet their diversity in natural systems is poorly understood. Here we trained a machine learning model on reference genomes to recognize the genetic architecture of plasmids. We applied our model to a global collection of human gut metagenomes to identify 68,350 non-redundant plasmids, which represent a 200-fold increase in the number of detectable plasmids in the human gut. This broad view of plasmid diversity revealed 1,169 ‘plasmid systems’, an evolutionary phenomenon where a backbone sequence with core plasmid functions, such as replication, is recombined with cargo functions, such as antibiotic resistance, depending on the environment. This work unearths the astonishing diversity of plasmids and provides a framework for studying their ecology and evolution.

Michael Yu

Toyota Technological Institute at Chicago



Identification of Gut Bacteria such as Lactobacillus johnsonii that Disseminate to Systemic Tissues of Wild Type and MyD88–/– Mice

In healthy hosts the gut microbiota is restricted to gut tissues by several barriers some of which require MyD88-dependent innate immune sensor pathways. Nevertheless, some gut taxa have been reported to disseminate to systemic tissues. However, the extent to which this normally occurs during homeostasis in healthy organisms is still unknown. In this study, we recovered viable gut bacteria from systemic tissues of healthy wild type (WT) and MyD88−/− mice. Shotgun metagenomic-sequencing revealed a marked increase in the relative abundance of L. johnsonii in intestinal tissues of MyD88−/− mice compared to WT mice. Lactobacillus johnsonii was detected most frequently from multiple systemic tissues and at higher levels in MyD88−/− mice compared to WT mice. Viable L. johnsonii strains were recovered from different cell types sorted from intestinal and systemic tissues of WT and MyD88−/− mice. L. johnsonii could persist in dendritic cells and may represent murine immunomodulatory endosymbionts.

Sreeram Udayan

Washington University School of Medicine in St Louis

Processed foods drive intestinal barrier permeability and microvascular diseases

Intake of processed foods has increased markedly over the past decades, coinciding with increased microvascular diseases such as chronic kidney disease (CKD) and diabetes. Here, we show in rodent models that long-term consumption of a processed diet drives intestinal barrier permeability and an increased risk of CKD. Inhibition of the advanced glycation pathway, which generates Maillard reaction products within foods upon thermal processing, reversed kidney injury. Consequently, a processed diet leads to innate immune complement activation and local kidney inflammation and injury via the potent proinflammatory effector molecule complement 5a (C5a). In a mouse model of diabetes, a high resistant starch fiber diet maintained gut barrier integrity and decreased severity of kidney injury via suppression of complement. These results demonstrate mechanisms by which processed foods cause inflammation that leads to chronic disease.

Matthew Snelson

Monash University