Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.

Archaea: A Goldmine for Molecular Biologists and Evolutionists

The rebuttal of the prokaryote-eukaryote dichotomy and the elaboration of the three domains concept by Carl Woese and colleagues has been a breakthrough in biology. With the methodologies available at this time, they have shown that a single molecule, the 16S ribosomal RNA, could reveal the global organization of the living world. Later on, mining archaeal genomes led to major discoveries in archaeal molecular biology, providing a third model for comparative molecular biology. These analyses revealed the strong eukaryal flavor of the basic molecular fabric of Archaea and support rooting the universal tree between Bacteria and Arcarya (the clade grouping Archaea and Eukarya). However, in contradiction with this conclusion, it remains to understand why the archaeal and bacterial mobilomes are so similar and so different from the eukaryal one. These last years, the number of recognized archaea lineages (phyla?) has exploded. The archaeal nomenclature is now in turmoil and debates about the nature of the last universal common ancestor, the last archaeal common ancestor, and the topology of the tree of life are still going on. Interestingly, the expansion of the archaeal eukaryome, especially in the Asgard archaea, has provided new opportunities to study eukaryogenesis. In recent years, the application to Archaea of the new methodologies described in the various chapters of this book have opened exciting avenues to study the molecular biology and the physiology of these fascinating microorganisms.

MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples

Background

High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.

Results

Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.

Conclusions

MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation are publicly available on GitHub at: https://github.com/smangul1/MiCoP.