Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g. mammals and microbes) using diverse types of data

Species-level functional profiling of metagenomes and metatranscriptomes.

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types

Depth-dependent mycoplankton glycoside hydrolase gene activity in the open ocean—evidence from the Tara Oceans eukaryote metatranscriptomes

6 months embargo applie

Co-circulation of West Nile virus and distinct insect-specific flaviviruses in Turkey

Background: Active vector surveillance provides an efficient tool for monitoring the presence or spread of emerging or re-emerging vector-borne viruses. This study was undertaken to investigate the circulation of flaviviruses. Mosquitoes were collected from 58 locations in 10 provinces across the Aegean, Thrace and Mediterranean Anatolian regions of Turkey in 2014 and 2015. Following morphological identification, mosquitoes were pooled and screened by nested and real-time PCR assays. Detected viruses were further characterised by sequencing. Positive pools were inoculated onto cell lines for virus isolation. Next generation sequencing was employed for genomic characterisation of the isolates. Results: A total of 12,711 mosquito specimens representing 15 species were screened in 594 pools. Eleven pools (2%) were reactive in the virus screening assays. Sequencing revealed West Nile virus (WNV) in one Culex pipiens (s.l.) pool from Thrace. WNV sequence corresponded to lineage one clade 1a but clustered distinctly from the Turkish prototype isolate. In 10 pools, insect-specific flaviviruses were characterised as Culex theileri flavivirus in 5 pools of Culex theileri and one pool of Cx. pipiens (s.l.), Ochlerotatus caspius flavivirus in two pools of Aedes (Ochlerotatus) caspius, Flavivirus AV-2011 in one pool of Culiseta annulata, and an undetermined flavivirus in one pool of Uranotaenia unguiculata from the Aegean and Thrace regions. DNA forms or integration of the detected insect-specific flaviviruses were not observed. A virus strain, tentatively named as “Ochlerotatus caspius flavivirus Turkey”, was isolated from an Ae. caspius pool in C6/36 cells. The viral genome comprised 10,370 nucleotides with a putative polyprotein of 3,385 amino acids that follows the canonical flavivirus polyprotein organisation. Sequence comparisons and phylogenetic analyses revealed the close relationship of this strain with Ochlerotatus caspius flavivirus from Portugal and Hanko virus from Finland. Several conserved structural and amino acid motifs were identified. Conclusions: We identified WNV and several distinct insect-specific flaviviruses during an extensive biosurveillance study of mosquitoes in various regions of Turkey in 2014 and 2015. Ongoing circulation of WNV is revealed, with an unprecedented genetic diversity. A probable replicating form of an insect flavivirus identified only in DNA form was detected

Tracing animal genomic evolution with the chromosomal-level assembly of the freshwater sponge Ephydatia muelleri

Abstract The genomes of non-bilaterian metazoans are key to understanding the molecular basis of early animal evolution. However, a full comprehension of how animal-specific traits such as nervous systems arose is hindered by the scarcity and fragmented nature of genomes from key taxa, such as Porifera. Ephydatia muelleri is a freshwater sponge found across the northern hemisphere. Here we present its 326 Mb genome, assembled to high contiguity (N50: 9.88 Mb) with 23 chromosomes on 24 scaffolds. Our analyses reveal a metazoan-typical genome architecture, with highly shared synteny across Metazoa, and suggest that adaptation to the extreme temperatures and conditions found in freshwater often involves gene duplication. The pancontinental distribution and ready laboratory culture of E. muelleri make this a highly practical model system, which with RNAseq, DNA methylation and bacterial amplicon data spanning its development and range allows exploration of genomic changes both within sponges and in early animal evolution

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials