The Diversity and Evolutionary Relationships of Ticks and Tick-borne Bacteria in China

Background Ticks (order Ixodida) are ectoparasites of vertebrates that transmit diverse pathogens to humans and domestic animals. However, information on the genomic diversity of ticks in China is currently limited to a small number of common species, leaving substantial knowledge gap in the evolution of both ticks and their associated bacterial. Results We collected more than 20,000 contemporary and historical (up to 60 years of preservation) tick samples representing a wide range of tick biodiversity across diverse geographic regions in China, including 18 common species, nine rare species, and two undetermined species. Metagenomic sequencing was performed on individual ticks to obtain the complete or near-complete mitochondrial (mt) genome sequences from 46 tick species, among which 30 species were revealed for the first time. These new mt genomes data greatly expanded the diversity of many tick groups and revealed five cryptic species. Utilizing the same metagenomic sequence data we identified divergent and abundant bacteria in Haemaphysalis, Ixodes, Dermacentor and Carios ticks, including nine species of pathogenetic bacteria and potentially new species within the genus Borrelia. We also used these data to explore the evolutionary relationship between ticks and their associated bacteria, revealing a pattern of long-term co-divergence relationship between ticks and Rickettsia and Coxiella bacteria. Conclusions In sum, our study provides important new information on the prevalence of tick-borne pathogens in China and sheds new light on the long-term evolutionary and ecological relationships between ticks and their associated bacteria.

Shotgun metagenomics of soil invertebrate communities reflects taxonomy, biomass and reference genome properties

Metagenomics - shotgun sequencing of all DNA fragments from a community DNA extract - is routinely used to describe the composition, structure and function of microorganism communities. Advances in DNA sequencing and the availability of genome databases increasingly allow the use of shotgun metagenomics on eukaryotic communities. Metagenomics offers major advances in the recovery of biomass relationships, in comparison to taxonomic marker gene based approaches (metabarcoding). However, little is known about the factors that influence metagenomics data from eukaryotic communities, such as differences among organism groups, properties of reference genomes and genome assemblies. We evaluated how shotgun metagenomics records composition and biomass in artificial soil invertebrate communities. We generated mock communities of controlled biomass ratios from 28 species from all major soil mesofauna groups: mites, springtails, nematodes, tardigrades and potworms. We shotgun-sequenced these communities and taxonomically assigned them with a database of over 270 soil invertebrate genomes. We recovered 90% of the species, and observed relatively high false positive detection rates. We found strong differences in reads assigned to different taxa, with some groups consistently attracting more hits than others. Biomass could be predicted from read counts after considering taxon-specific differences. Larger genomes more complete assemblies consistently attracted more reads than genomes. The GC content of the genome assemblies had no effect on the biomass-read relationships. The results show considerable differences in taxon recovery and taxon specificity of biomass recovery from metagenomic sequence data. Properties of reference genomes and genome assemblies also influence biomass recovery, and they should be considered in metagenomic studies of eukaryotes. We provide a roadmap for investigating factors which influence metagenomics-based eukaryotic community reconstructions. Understanding these factors is timely as accessibility of DNA sequencing, and momentum for reference genomes projects show a future where the taxonomic assignment of DNA from any community sample becomes a reality.

Comparative Metagenomic Analysis of Two Hot Springs From Ourense (Northwestern Spain) and Others Worldwide

With their circumneutral pH and their moderate temperature (66 and 68°C, respectively), As Burgas and Muiño da Veiga are two important human-use hot springs, previously studied with traditional culture methods, but never explored with a metagenomic approach. In the present study, we have performed metagenomic sequence-based analyses to compare the taxonomic composition and functional potential of these hot springs. Proteobacteria, Deinococcus-Thermus, Firmicutes, Nitrospirae, and Aquificae are the dominant phyla in both geothermal springs, but there is a significant difference in the abundance of these phyla between As Burgas and Muiño da Veiga. Phylum Proteobacteria dominates As Burgas ecosystem while Aquificae is the most abundant phylum in Muiño da Veiga. Taxonomic and functional analyses reveal that the variability in water geochemistry might be shaping the differences in the microbial communities inhabiting these geothermal springs. The content in organic compounds of As Burgas water promotes the presence of heterotrophic populations of the genera Acidovorax and Thermus, whereas the sulfate-rich water of Muiño da Veiga favors the co-dominance of genera Sulfurihydrogenibium and Thermodesulfovibrio. Differences in ammonia concentration exert a selective pressure toward the growth of nitrogen-fixing bacteria such as Thermodesulfovibrio in Muiño da Veiga. Temperature and pH are two important factors shaping hot springs microbial communities as was determined by comparative analysis with other thermal springs.

Shotgun metagenomics of soil invertebrate communities reflects taxonomy, biomass and reference genome properties

Metagenomics - shotgun sequencing of all DNA fragments from a community DNA extract - is routinely used to describe the composition, structure and function of microorganism communities. Advances in DNA sequencing and the availability of genome databases increasingly allow the use of shotgun metagenomics on eukaryotic communities. Metagenomics offers major advances in the recovery of biomass relationships in a sample, in comparison to taxonomic marker gene based approaches (metabarcoding). However, little is known about the factors which influence metagenomics data from eukaryotic communities, such as differences among organism groups, the properties of reference genomes and genome assemblies. We evaluated how shotgun metagenomics records composition and biomass in artificial soil invertebrate communities. We generated mock communities of controlled biomass ratios from 28 species from all major soil mesofauna groups: mites, springtails, nematodes, tardigrades and potworms. We shotgun-sequenced these communities and taxonomically assigned them with a database of over 270 soil invertebrate genomes. We recovered 90% of the species, and observed relatively high false positive detection rates. We found strong differences in reads assigned to different taxa, with some groups (e.g. springtails) consistently attracting more hits than others (e.g. enchytraeids). Original biomass could be predicted from read counts after considering these taxon-specific differences. Species with larger genomes, and with more complete assemblies consistently attracted more reads than species with smaller genomes. The GC content of the genome assemblies had no effect on the biomass-read relationships. The results show considerable differences in taxon recovery and taxon specificity of biomass recovery from metagenomic sequence data. The properties of reference genomes and genome assemblies also influence biomass recovery, and they should be considered in metagenomic studies of eukaryotes. We provide a roadmap for investigating factors which influence metagenomics-based eukaryotic community reconstructions. Understanding these factors is timely as accessibility of DNA sequencing, and momentum for reference genomes projects show a future where the taxonomic assignment of DNA from any community sample becomes a reality.

Development of a metatranscriptomic analysis method for multiple intestinal sites and its application to the common marmoset intestine

Background: The intestinal microbiome is closely related to host health, and metatranscriptomic analysis can assess the functional activity of microbiomes by quantifying the bacterial gene expression level, which helps to elucidate the interaction between the microbiome and the environment. However, functional changes in the microbiome along the host intestinal tract remain unknown, and previous analytical methods have limitations, such as potentially overlooking unknown genes due to dependence on existing databases and being unable to take full advantage of metatranscriptome to reveal the functional change among multiple environments. Result: To close these gaps, we develop a novel method that integrates metagenome and metatranscriptome to analyze the functional activity of microbiomes between intestinal sites. This method reconstructs a reference metagenomic sequence across multiple intestinal sites, allowing the gene expression levels of microbiome including unknown bacterial genes to be compared between multiple sites. As a result of applying this method to metatranscriptomic analysis in the intestinal tract of common marmoset, the reconstructed metagenome covered most of the expressed genes and it revealed that the changes in bacterial gene expressions among the caecum, transverse colon, and faeces were more dynamic and sensitive to environmental shifts than its abundance. In typical, the coenzyme synthesis gene and antibacterial resistance gene were more highly expressed in the caecum and transverse colon than in faeces, while there was no significant change in abundance of these genes. Conclusion: Our findings demonstrate that an analytical method that integrates metagenome and metatranscriptome in multiple intestinal sites captures functional changes in the microbiomes at the gene resolution level.

BinChecker: a new algorithm for quality assessment of microbial draft genomes

In the reconstruction of microbial genomes from metagenomic sequence data, the estimation of the final completeness and possible contamination is crucial for quality control. In metagenomics candidate genomes are usually obtained from a metagenome assembly and a subsequent binning of the assembled contigs. BinChecker provides a novel approach to quality assessment that is based on a fast protein domain search and a clustering approach for identification of marker domain (“feature”) sets. The feature sets that are used for estimation are not pre-computed for a given database of reference genomes, but are individually found for each bin by adaptive clustering and feature selection. In particular, the adaptivity facilitates the creation and extension of the underlying database, which just requires to add protein feature profiles of reference genomes. Tests with simulated bins indicate that the prediction accuracy of BinChecker meets the current state of the art while providing significant advantages in terms of speed and flexibility.

PAM-repeat associations and spacer selection preferences in single and co-occurring CRISPR-Cas systems

Background The adaptive CRISPR-Cas immune system stores sequences from past invaders as spacers in CRISPR arrays and thereby provides direct evidence that links invaders to hosts. Mapping CRISPR spacers has revealed many aspects of CRISPR-Cas biology, including target requirements such as the protospacer adjacent motif (PAM). However, studies have so far been limited by a low number of mapped spacers in the database. Results By using vast metagenomic sequence databases, we map approximately one-third of more than 200,000 unique CRISPR spacers from a variety of microbes and derive a catalog of more than two hundred unique PAM sequences associated with specific CRISPR-Cas subtypes. These PAMs are further used to correctly assign the orientation of CRISPR arrays, revealing conserved patterns between the last nucleotides of the CRISPR repeat and PAM. We could also deduce CRISPR-Cas subtype-specific preferences for targeting either template or coding strand of open reading frames. While some DNA-targeting systems (type I-E and type II systems) prefer the template strand and avoid mRNA, other DNA- and RNA-targeting systems (types I-A and I-B and type III systems) prefer the coding strand and mRNA. In addition, we find large-scale evidence that both CRISPR-Cas adaptation machinery and CRISPR arrays are shared between different CRISPR-Cas systems. This could lead to simultaneous DNA and RNA targeting of invaders, which may be effective at combating mobile genetic invaders. Conclusions This study has broad implications for our understanding of how CRISPR-Cas systems work in a wide range of organisms for which only the genome sequence is known.

MINTIA: a metagenomic INserT integrated assembly and annotation tool

The earth harbors trillions of bacterial species adapted to very diverse ecosystems thanks to specific metabolic function acquisition. Most of the genes responsible for these functions belong to uncultured bacteria and are still to be discovered. Functional metagenomics based on activity screening is a classical way to retrieve these genes from microbiomes. This approach is based on the insertion of large metagenomic DNA fragments into a vector and transformation of a host to express heterologous genes. Metagenomic libraries are then screened for activities of interest, and the metagenomic DNA inserts of active clones are extracted to be sequenced and analysed to identify genes that are responsible for the detected activity. Hundreds of metagenomics sequences found using this strategy have already been published in public databases. Here we present the MINTIA software package enabling biologists to easily generate and analyze large metagenomic sequence sets, retrieved after activity-based screening. It filters reads, performs assembly, removes cloning vector, annotates open reading frames and generates user friendly reports as well as files ready for submission to international sequence repositories. The software package can be downloaded from https://github.com/Bios4Biol/MINTIA.

SeqDistK: a Novel Tool for Alignment-free Phylogenetic Analysis

Algorithms for constructing phylogenetic trees are fundamental to study the evolution of viruses, bacteria, and other microbes. Established multiple alignment-based algorithms are inefficient for large scale metagenomic sequence data because of their high requirement of inter-sequence correlation and high computational complexity. In this paper, we present SeqDistK, a novel tool for alignment-free phylogenetic analysis. SeqDistK computes the dissimilarity matrix for phylogenetic analysis, incorporating seven k-mer based dissimilarity measures, namely d2, d2S, d2star, Euclidean, Manhattan, CVTree, and Chebyshev. Based on these dissimilarities, SeqDistK constructs phylogenetic tree using the Unweighted Pair Group Method with Arithmetic Mean algorithm. Using a golden standard dataset of 16S rRNA and its associated phylogenetic tree, we compared SeqDistK to Muscle - a multi sequence aligner. We found SeqDistK was not only 38 times faster than Muscle in computational efficiency but also more accurate. SeqDistK achieved the smallest symmetric difference between the inferred and ground truth trees with a range between 13 to 18, while that of Muscle was 62. When measures d2, d2star, d2S, Euclidean, and k-mer size k=5 were used, SeqDistK consistently inferred phylogenetic tree almost identical to the ground truth tree. We also performed clustering of 16S rRNA sequences using SeqDistK and found the clustering was highly consistent with known biological taxonomy. Among all the measures, d2S (k=5, M=2) showed the best accuracy as it correctly clustered and classified all sample sequences. In summary, SeqDistK is a novel, fast and accurate alignment-free tool for large-scale phylogenetic analysis. SeqDistK software is freely available at https://github.com/htczero/SeqDistK.

Skin Metagenomic Sequence Analysis of Early Candida auris Outbreaks in U.S. Nursing Homes

Candida auris is a human pathogen of high concern due to its extensive antifungal drug resistance and high mortality rates associated with invasive infections. Candida auris skin colonization and persistence on environmental surfaces make this pathogen difficult to control once it enters a health care facility. Residents in long-term care hospitals and nursing homes are especially vulnerable.