Explore the vast range of titles available.

Background Populations of closely related microbial strains can be simultaneously present in bacterial communities such as the human gut microbiome. We recently developed a de novo genome assembly approach that uses read cloud sequencing to provide more complete microbial genome drafts, enabling precise differentiation and tracking of strain-level dynamics across metagenomic samples. In this case study, we present a proof-of-concept using read cloud sequencing to describe bacterial strain diversity in the gut microbiome of one hematopoietic cell transplantation patient over a 2-month time course and highlight temporal strain variation of gut microbes during therapy. The treatment was accompanied by diet changes and administration of multiple immunosuppressants and antimicrobials. Methods We conducted short-read and read cloud metagenomic sequencing of DNA extracted from four longitudinal stool samples collected during the course of treatment of one hematopoietic cell transplantation (HCT) patient. After applying read cloud metagenomic assembly to discover strain-level sequence variants in these complex microbiome samples, we performed metatranscriptomic analysis to investigate differential expression of antibiotic resistance genes. Finally, we validated predictions from the genomic and metatranscriptomic findings through in vitro antibiotic susceptibility testing and whole genome sequencing of isolates derived from the patient stool samples. Results During the 56-day longitudinal time course that was studied, the patient’s microbiome was profoundly disrupted and eventually dominated by Bacteroides caccae . Comparative analysis of B. caccae genomes obtained using read cloud sequencing together with metagenomic RNA sequencing allowed us to identify differences in substrain populations over time. Based on this, we predicted that particular mobile element integrations likely resulted in increased antibiotic resistance, which we further supported using in vitro antibiotic susceptibility testing. Conclusions We find read cloud assembly to be useful in identifying key structural genomic strain variants within a metagenomic sample. These strains have fluctuating relative abundance over relatively short time periods in human microbiomes. We also find specific structural genomic variations that are associated with increased antibiotic resistance over the course of clinical treatment.

Background The assembly of large and complex genomes can be costly since it typically requires the utilization of multiple sequencing technologies and access to high-performance computing, while creating a dependency on external service providers. The aim of this study was to independently generate draft genomes for the cattle ticks Rhipicephalus microplus and R. appendiculatus using Oxford Nanopore sequencing technology. Results Exclusively, Oxford Nanopore sequence data were assembled with Shasta and finalized on the Amazon Web Services cloud platform, capitalizing on the availability of up to 90% discounted Spot instances. The assembled and polished R. microplus and R. appendiculatus genomes from our study were comparable to published tick genomes where multiple sequencing technologies and costly bioinformatic resources were utilized that are not readily accessible to low-resource environments. We predicted 52,412 genes for R. appendiculatus , with 31,747 of them being functionally annotated. The R. microplus annotation consisted of 60,935 predicted genes, with 32,263 being functionally annotated in the final file. The sequence data were also used to assemble and annotate genetically distinct Coxiella -like endosymbiont genomes for each tick species. The results indicated that each of the endosymbionts exhibited genome reductions. The Nanopore Q20 + library kit and flow cell were used to sequence the > 80% AT-rich mitochondrial DNA of both tick species. The sequencing generated accurate mitochondrial genomes, encountering imperfect base calling only in homopolymer regions exceeding 10 bases. Conclusion This study presents an alternative approach for smaller laboratories with limited budgets to enter the field and participate in genomics without capital intensive investments, allowing for capacity building in a field normally exclusively accessible through collaboration and large funding opportunities.

Background Low diversity of the gut microbiome, often progressing to the point of intestinal domination by a single species, has been linked to poor outcomes in patients undergoing hematopoietic cell transplantation (HCT). Our ability to understand how certain organisms attain intestinal domination over others has been restricted in part by current metagenomic sequencing technologies that are typically unable to reconstruct complete genomes for individual organisms present within a sequenced microbial community. We recently developed a metagenomic read cloud sequencing and assembly approach that generates improved draft genomes for individual organisms compared to conventional short-read sequencing and assembly methods. Herein, we applied metagenomic read cloud sequencing to four stool samples collected longitudinally from an HCT patient preceding treatment and over the course of heavy antibiotic exposure. Results Characterization of microbiome composition by taxonomic classification of reads reveals that that upon antibiotic exposure, the subject’s gut microbiome experienced a marked decrease in diversity and became dominated by Escherichia coli . While diversity is restored at the final time point, this occurs without recovery of the original species and strain-level composition. Draft genomes for individual organisms within each sample were generated using both read cloud and conventional assembly. Read clouds were found to improve the completeness and contiguity of genome assemblies compared to conventional assembly. Moreover, read clouds enabled the placement of antibiotic resistance genes present in multiple copies both within a single draft genome and across multiple organisms. The occurrence of resistance genes associates with the timing of antibiotics administered to the patient, and comparative genomic analysis of the various intestinal E. coli strains across time points as well as the bloodstream isolate showed that the subject’s E. coli bloodstream infection likely originated from the intestine. The E. coli genome from the initial pre-transplant stool sample harbors 46 known antimicrobial resistance genes, while all other species from the pre-transplant sample each contain at most 5 genes, consistent with a model of heavy antibiotic exposure resulting in selective outgrowth of the highly antibiotic-resistant E. coli . Conclusion This study demonstrates the application and utility of metagenomic read cloud sequencing and assembly to study the underlying strain-level genomic factors influencing gut microbiome dynamics under extreme selective pressures in the clinical context of HCT.

Genomic data for the closest relatives of house mice (Mus musculus species complex) are surprisingly limited. Here, we present the first complete genome for a behaviorally and ecologically unique member of the sister clade to house mice, the mound-building mouse, Mus spicilegus. Using read cloud sequencing and de novo assembly we produced a 2.50 Gbp genome with a scaffold N50 of 2.27 Mbp. We constructed >25 000 gene models, of which the majority had high homology to other Mus species. To evaluate the utility of the M. spicilegus genome for behavioral and ecological genomics, we extracted 196 vomeronasal receptor (VR) sequences from our genome and analyzed phylogenetic relationships between M. spicilegus VRs and orthologs from M. musculus and the Algerian mouse, M. spretus. While most M. spicilegus VRs clustered with orthologs in M. musculus and M. spretus, 10 VRs with evidence of rapid divergence in M. spicilegus are strong candidate modulators of species-specific chemical communication. A high quality assembly and genome for M. spicilegus will help to resolve discordant ancestry patterns in house mouse genomes, and will provide an essential foundation for genetic dissection of phenotypes that distinguish commensal from non-commensal species, and the social and ecological characteristics that make M. spicilegus unique.

Recent advances in long fragment read (LFR, also known as linked-read technologies or read-cloud) technologies, such as single tube long fragment reads (stLFR), 10X Genomics Chromium reads, and TruSeq synthetic long-reads, have enabled efficient haplotyping and genome assembly. However, in the case of stLFR and 10X Genomics Chromium reads, the long fragments of a genome are covered sparsely by reads in each barcode and most barcodes are contained in multiple long fragments from different regions, which results in inefficient assembly when using long-range information. Thus, methods to address these shortcomings are vital for capitalizing on the additional information obtained using these technologies. We therefore designed IterCluster, a novel, alignment-free clustering algorithm that can cluster barcodes from the same target region of a genome, using -mer frequency-based features and a Markov Cluster (MCL) approach to identify enough reads in a target region of a genome to ensure sufficient target genome sequence depth. The IterCluster method was validated using BGI stLFR and 10X Genomics chromium reads datasets. IterCluster had a higher precision and recall rate on BGI stLFR data compared to 10X Genomics Chromium read data. In addition, we demonstrated how IterCluster improves the de novo assembly results when using a divide-and-conquer strategy on a human genome data set (scaffold/contig N50 = 13.2 kbp/7.1 kbp vs. 17.1 kbp/11.9 kbp before and after IterCluster, respectively). IterCluster provides a new way for determining LFR barcode enrichment and a novel approach for de novo assembly using LFR data. IterCluster is OpenSource and available on https://github.com/JianCong-WENG/IterCluster .