Bipartite Graphs for Visualization Analysis of Microbiome Data

Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs

Current visualizations in microbiome research rely on aggregations in taxonomic classifications or do not show less abundant taxa. We introduce Snowflake: a new visualization method that creates a clear overview of the microbiome composition in collected samples without losing any information due to classification or neglecting less abundant reads. Snowflake displays every observed OTU/ASV in the microbiome abundance table and provides a solution to include the data's hierarchical structure and additional information obtained from downstream analysis (e.g., alpha- and beta-diversity) and metadata. Based on the value-driven ICE-T evaluation methodology, Snowflake was positively received. Experts in microbiome research found the visualizations to be user-friendly and detailed and liked the possibility of including and relating additional information to the microbiome's composition. Exploring the topological structure of the microbiome abundance table allows them to quickly identify which taxa are unique to specific samples and which are shared among multiple samples (i.e., separating sample-specific taxa from the core microbiome), and see the compositional differences between samples. An R package for constructing and visualizing Snowflake microbiome composition graphs is available at https://gitlab.com/vda-lab/snowflake.

Preparatory immunity: Seasonality of mucosal skin defences and Batrachochytrium infections in Southern leopard frogs

Accurately predicting the impacts of climate change on wildlife health requires a deeper understanding of seasonal rhythms in host-pathogen interactions. The amphibian pathogen, Batrachochytrium dendrobatidis (Bd), exhibits seasonality in incidence; however, the role that biological rhythms in host defences play in defining this pattern remains largely unknown. The aim of this study was to examine whether host immune and microbiome defences against Bd correspond with infection risk and seasonal fluctuations in temperature and humidity. Over the course of a year, five populations of Southern leopard frogs (Rana [Lithobates] sphenocephala) in Tennessee, United States, were surveyed for host immunity, microbiome and pathogen dynamics. Frogs were swabbed for pathogen load and skin bacterial diversity and stimulated to release stored antimicrobial peptides (AMPs). Secretions were analysed to estimate total hydrophobic peptide concentrations, presence of known AMPs and effectiveness of Bd growth inhibition in vitro. The diversity and proportion of bacterial reads with a 99% match to sequences of isolates known to inhibit Bd growth in vitro were used as an estimate of predicted anti-Bd function of the skin microbiome. Batrachochytrium dendrobatidis dynamics followed the expected seasonal fluctuations-peaks in cooler months-which coincided with when host mucosal defences were most potent against Bd. Specifically, the concentration and expression of stored AMPs cycled synchronously with Bd dynamics. Although microbiome changes followed more linear trends over time, the proportion of bacteria that can function to inhibit Bd growth was greatest when risk of Bd infection was highest. We interpret the increase in peptide storage in the fall and the shift to a more anti-Bd microbiome over winter as a preparatory response for subsequent infection risk during the colder periods when AMP synthesis and bacterial growth is slow and pathogen pressure from this cool-adapted fungus is high. Given that a decrease in stored AMP concentrations as temperatures warm in spring likely means greater secretion rates, the subsequent decrease in prevalence suggests seasonality of Bd in this host may be in part regulated by annual immune rhythms, and dominated by the effects of temperature.

Stool sampling and DNA isolation kits affect DNA quality and bacterial composition following 16S rRNA gene sequencing using MiSeq Illumina platform

Many studies correlate changes in human gut microbiome with the onset of various diseases, mostly by 16S rRNA gene sequencing. Setting up the optimal sampling and DNA isolation procedures is crucial for robustness and reproducibility of the results. We performed a systematic comparison of several sampling and DNA isolation kits, quantified their effect on bacterial gDNA quality and the bacterial composition estimates at all taxonomic levels. Sixteen volunteers tested three sampling kits. All samples were consequently processed by two DNA isolation kits. We found that the choice of both stool sampling and DNA isolation kits have an effect on bacterial composition with respect to Gram-positivity, however the isolation kit had a stronger effect than the sampling kit. The proportion of bacteria affected by isolation and sampling kits was larger at higher taxa levels compared to lower taxa levels. The PowerLyzer PowerSoil DNA Isolation Kit outperformed the QIAamp DNA Stool Mini Kit mainly due to better lysis of Gram-positive bacteria while keeping the values of all the other assessed parameters within a reasonable range. The presented effects need to be taken into account when comparing results across multiple studies or computing ratios between Gram-positive and Gram-negative bacteria.

Exploring the evolutionary dynamics of Rhizobium plasmids through bipartite network analysis

The genus Rhizobium usually has a multipartite genome architecture with a chromosome and several plasmids, making these bacteria a perfect candidate for plasmid biology studies. As there are no universally shared genes among typical plasmids, network analyses can complement traditional phylogenetics in a broad-scale study of plasmid evolution. Here, we present an exhaustive analysis of 216 plasmids from 49 complete genomes of Rhizobium by constructing a bipartite network that consists of two classes of nodes, the plasmids and homologous protein families that connect them. Dissection of the network using a hierarchical clustering strategy reveals extensive variety, with 34 homologous plasmid clusters. Four large clusters including one cluster of symbiotic plasmids and two clusters of chromids carrying some truly essential genes are widely distributed among Rhizobium. In contrast, the other clusters are quite small and rare. Symbiotic clusters and rare accessory clusters are exogenetic and do not appear to have co-evolved with the common accessory clusters; the latter ones have a large coding potential and functional complementarity for different lifestyles in Rhizobium. The bipartite network also provides preliminary evidence of Rhizobium plasmid variation and formation including genetic exchange, plasmid fusion and fission, exogenetic plasmid transfer, host plant selection, and environmental adaptation.

Host Ecology Rather Than Host Phylogeny Drives Amphibian Skin Microbial Community Structure in the Biodiversity Hotspot of Madagascar

Host-associated microbiotas of vertebrates are diverse and complex communities that contribute to host health. In particular, for amphibians, cutaneous microbial communities likely play a significant role in pathogen defense; however, our ecological understanding of these communities is still in its infancy. Here, we take advantage of the fully endemic and locally species-rich amphibian fauna of Madagascar to investigate the factors structuring amphibian skin microbiota on a large scale. Using amplicon-based sequencing, we evaluate how multiple host species traits and site factors affect host bacterial diversity and community structure. Madagascar is home to over 400 native frog species, all of which are endemic to the island; more than 100 different species are known to occur in sympatry within multiple rainforest sites. We intensively sampled frog skin bacterial communities, from over 800 amphibians from 89 species across 30 sites in Madagascar during three field visits, and found that skin bacterial communities differed strongly from those of the surrounding environment. Richness of bacterial operational taxonomic units (OTUs) and phylogenetic diversity differed among host ecomorphs, with arboreal frogs exhibiting lower richness and diversity than terrestrial and aquatic frogs. Host ecomorphology was the strongest factor influencing microbial community structure, with host phylogeny and site parameters (latitude and elevation) explaining less but significant portions of the observed variation. Correlation analysis and topological congruency analyses revealed little to no phylosymbiosis for amphibian skin microbiota. Despite the observed geographic variation and low phylosymbiosis, we found particular OTUs that were differentially abundant between particular ecomorphs. For example, the genus Pigmentiphaga (Alcaligenaceae) was significantly enriched on arboreal frogs, Methylotenera (Methylophilaceae) was enriched on aquatic frogs, and Agrobacterium (Rhizobiaceae) was enriched on terrestrial frogs. The presence of shared bacterial OTUs across geographic regions for selected host genera suggests the presence of core microbial communities which in Madagascar, might be driven more strongly by a species’ preference for specific microhabitats than by the physical, physiological or biochemical properties of their skin. These results corroborate that both host and environmental factors are driving community assembly of amphibian cutaneous microbial communities, and provide an improved foundation for elucidating their role in disease resistance.

Functional hypergraph uncovers novel covariant structures over neurodevelopment

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously-described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823-3835, 2017. © 2017 Wiley Periodicals, Inc.

Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics

One of main steps in a study of microbial communities is resolving their composition, diversity and function. In the past, these issues were mostly addressed by the use of amplicon sequencing of a target gene because of reasonable price and easier computational postprocessing of the bioinformatic data. With the advancement of sequencing techniques, the main focus shifted to the whole metagenome shotgun sequencing, which allows much more detailed analysis of the metagenomic data, including reconstruction of novel microbial genomes and to gain knowledge about genetic potential and metabolic capacities of whole environments. On the other hand, the output of whole metagenomic shotgun sequencing is mixture of short DNA fragments belonging to various genomes, therefore this approach requires more sophisticated computational algorithms for clustering of related sequences, commonly referred to as sequence binning. There are currently two types of binning methods: taxonomy dependent and taxonomy independent. The first type classifies the DNA fragments by performing a standard homology inference against a reference database, while the latter performs the reference-free binning by applying clustering techniques on features extracted from the sequences. In this review, we describe the strategies within the second approach. Although these strategies do not require prior knowledge, they have higher demands on the length of sequences. Besides their basic principle, an overview of particular methods and tools is provided. Furthermore, the review covers the utilization of the methods in context with the length of sequences and discusses the needs for metagenomic data preprocessing in form of initial assembly prior to binning.