Read Cloud Research Articles

Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale

BackgroundPopulations 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.MethodsWe 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.ResultsDuring 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.ConclusionsWe 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.

Intestinal microbiota domination under extreme selective pressures characterized by metagenomic read cloud sequencing and assembly

BackgroundLow 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.ResultsCharacterization 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.ConclusionThis 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.

Minerva: an alignment- and reference-free approach to deconvolve Linked-Reads for metagenomics.

Emerging Linked-Read technologies (aka read cloud or barcoded short-reads) have revived interest in short-read technology as a viable approach to understand large-scale structures in genomes and metagenomes. Linked-Read technologies, such as the 10x Chromium system, use a microfluidic system and a specialized set of 3′ barcodes (aka UIDs) to tag short DNA reads sourced from the same long fragment of DNA; subsequently, the tagged reads are sequenced on standard short-read platforms. This approach results in interesting compromises. Each long fragment of DNA is only sparsely covered by reads, no information about the ordering of reads from the same fragment is preserved, and 3′ barcodes match reads from roughly 2–20 long fragments of DNA. However, compared to long-read technologies, the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads. In this paper, we formally describe a particular algorithmic issue common to Linked-Read technology: the deconvolution of reads with a single 3′ barcode into clusters that represent single long fragments of DNA. We introduce Minerva, a graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results, improving the specificity of taxonomic assignments and of k-mer-based clustering. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics.

Read cloud sequencing elucidates microbiome dynamics in a hematopoietic cell transplant patient.

Low intestinal microbial diversity, often leading to domination of the intestine by a single organism, is associated with poor outcomes following hematopoietic cell transplantation (HCT). Understanding how certain organisms achieve domination in the intestine is limited by current metagenomic sequencing technologies, which are typically unable to reconstruct complete genome drafts without bacterial isolation and culture. Recently, we developed a metagenomic read cloud sequencing approach that provides significantly improved genome drafts for individual organisms compared to conventional short-read sequencing methods. Here, we apply read cloud sequencing to four longitudinal stool samples collected from an HCT patient before and after heavy antibiotic exposure. During this time period, the patient experienced Escherichia coli gut domination and an E. coli bloodstream infection. We find that read clouds enable the placement of multiple copies of antibiotic resistance genes both within and across genomes, and the presence of resistance genes correlates with the timing of antibiotics administered to the patient. Comparative genomic analysis reveals that the E. coli bloodstream infection likely originated from the gut. The pre-transplant E. coli genome harbors 46 known resistance genes, whereas all other organisms from the pre-transplant time point contain 5 or fewer resistance genes, supporting a model in which the E. coli outgrowth was a result of selection by heavy antibiotic exposure. This case study highlights the application of metagenomic read cloud sequencing in a clinical context to elucidate the genomic underpinnings of microbiome dynamics under extreme selective pressures.

A High Quality Genome for Mus spicilegus, a Close Relative of House Mice with Unique Social and Ecological Adaptations.

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.

Genome-wide reconstruction of complex structural variants using read clouds.

Microfluidic partitioning of long genomic DNA fragments, and barcoding of shorter fragments derived from them, retains long-range information in short sequencing reads. Such read cloud approaches represent a powerful and cost-effective alternative to single-molecule long-read sequencing. We developed GROC-SVs, which uses read clouds for structural variant detection and assembly, and apply it to Illumina-sequenced 10× Genomics sarcoma and breast cancer data sets. Validation demonstrates substantial improvement in specificity of breakpoint detection compared to short-fragment sequencing, at comparable sensitivity, and vice versa. The long-range information also facilitates sequence assembly of breakpoints; importantly, consecutive breakpoints closer than the average length of the input DNA molecules can be assembled, with some events exhibiting remarkable complexity. We show that chromothriptic rearrangements occurred before copy number amplifications and that single-nucleotide and structural variants are not correlated. We predict significant advances in structural variant science using 10×/GROC-SVs and other read cloud-specific methods.

From Pine Cones to Read Clouds: Rescaffolding the Megagenome of Sugar Pine (Pinus lambertiana).

We investigate the utility and scalability of new read cloud technologies to improve the draft genome assemblies of the colossal, and largely repetitive, genomes of conifers. Synthetic long read technologies have existed in various forms as a means of reducing complexity and resolving repeats since the outset of genome assembly. Recently, technologies that combine subhaploid pools of high molecular weight DNA with barcoding on a massive scale have brought new efficiencies to sample preparation and data generation. When combined with inexpensive light shotgun sequencing, the resulting data can be used to scaffold large genomes. The protocol is efficient enough to consider routinely for even the largest genomes. Conifers represent the largest reference genome projects executed to date. The largest of these is that of the conifer Pinus lambertiana (sugar pine), with a genome size of 31 billion bp. In this paper, we report on the molecular and computational protocols for scaffolding the P. lambertiana genome using the library technology from 10× Genomics. At 247,000 bp, the NG50 of the existing reference sequence is the highest scaffold contiguity among the currently published conifer assemblies; this new assembly’s NG50 is 1.94 million bp, an eightfold increase.

Read clouds uncover variation in complex regions of the human genome.

Although an increasing amount of human genetic variation is being identified and recorded, determining variants within repeated sequences of the human genome remains a challenge. Most population and genome-wide association studies have therefore been unable to consider variation in these regions. Core to the problem is the lack of a sequencing technology that produces reads with sufficient length and accuracy to enable unique mapping. Here, we present a novel methodology of using read clouds, obtained by accurate short-read sequencing of DNA derived from long fragment libraries, to confidently align short reads within repeat regions and enable accurate variant discovery. Our novel algorithm, Random Field Aligner (RFA), captures the relationships among the short reads governed by the long read process via a Markov Random Field. We utilized a modified version of the Illumina TruSeq synthetic long-read protocol, which yielded shallow-sequenced read clouds. We test RFA through extensive simulations and apply it to discover variants on the NA12878 human sample, for which shallow TruSeq read cloud sequencing data are available, and on an invasive breast carcinoma genome that we sequenced using the same method. We demonstrate that RFA facilitates accurate recovery of variation in 155 Mb of the human genome, including 94% of 67 Mb of segmental duplication sequence and 96% of 11 Mb of transcribed sequence, that are currently hidden from short-read technologies.