Ariadne: synthetic long read deconvolution using assembly graphs

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

322. Clinical-Grade Metagenomics in Urinary Tract Infections: Improving Performance of Next-Generation Sequencing Assays Using Internal Controls and Machine Learning

Background

Shotgun sequencing-based metagenomics is a useful approach to profiling microbiomes in environmental and patient samples. In a clinical setting, metagenomic techniques have the advantage of identifying organisms, which cannot be readily cultured or confirmed by other techniques. We have developed a clinical-grade, streamlined metagenomics-based pipeline that includes regulatory compliant method considerations, such as an internal control followed by a machine-based learning (ML) process to identify pathogens in urine samples.

Methods

We built an optimized novel end-to-end NGS assay pipeline that harnesses pathogen-specific genome data to detect bacterial species. We processed de-identified clinical urine specimens, collected from patients symptomatic for urinary tract infection (UTI). This workflow includes an IPC, QIACube-MDx extraction, library preparation and Illumina NextSeq 550 sequencing and a novel interpretable ML based analytic approach, Biotia-DX. Clinical culture results and qPCR were used as a baseline for the assay to train the ML model and to establish accuracy relative to the clinical standard of care.

Results

We clinically validated over 40 key uropathogens and conducted clinical studies of specificity, intra/inter reproducibility, accuracy in urine specimens (n=300), and limit of detection in E. coli, K. pneumoniae, P. mirabilis, S. aureus, E. faecalis and Candida. Additionally, the implementation of an internal control coupled with our Biotia-DX software provides an accurate (F1 score 94.3%) and highly sensitive clinical grade diagnostic tool.

Conclusion

Urine has historically presented a challenge for diagnostics via culturing, with a high rate of culture-negative results (∼30% on average). We improved the clinical utility of an NGS urine assay by leveraging an IPC and ML software. This decreased the rate of false positive species called in a sample relative to other NGS techniques and allows for greater sensitivity and taxonomic specificity. This assay may be especially useful for low colony-count or negative-culture samples to diagnose and guide patient treatment.

Disclosures

Mara Couto-Rodriguez, MS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company David C. Danko, PhD, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Xavier O. Jirau Serrano, MS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Taylor Paisie, MS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company John C. Papciak, BS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Eszter Szollosi, BS, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company Christopher E. Mason, PhD, Biotia Inc.: Advisor/Consultant|Biotia Inc.: Board Member|Biotia Inc.: Ownership Interest Caitlin Otto, PhD, D(ABMM), Biotia Inc.: Advisor/Consultant Niamh B. O'Hara, PhD, Biotia Inc.: Board Member|Biotia Inc.: Ownership Interest Dorottya Nagy-Szakal, MD PhD, Biotia Inc.: Employee of Biotia Inc. a for-profit biotechnology company|Biotia Inc.: Stocks/Bonds.

System-wide transcriptome damage and tissue identity loss in COVID-19 patients

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections.

Sequencing of Circulating Microbial Cell-Free DNA Can Identify Pathogens in Periprosthetic Joint Infections

BACKGROUND

Over 1 million Americans undergo joint replacement each year, and approximately 1 in 75 will incur a periprosthetic joint infection. Effective treatment necessitates pathogen identification, yet standard-of-care cultures fail to detect organisms in 10% to 20% of cases and require invasive sampling. We hypothesized that cell-free DNA (cfDNA) fragments from microorganisms in a periprosthetic joint infection can be found in the bloodstream and utilized to accurately identify pathogens via next-generation sequencing.

METHODS

In this prospective observational study performed at a musculoskeletal specialty hospital in the U.S., we enrolled 53 adults with validated hip or knee periprosthetic joint infections. Participants had peripheral blood drawn immediately prior to surgical treatment. Microbial cfDNA from plasma was sequenced and aligned to a genome database with >1,000 microbial species. Intraoperative tissue and synovial fluid cultures were performed per the standard of care. The primary outcome was accuracy in organism identification with use of blood cfDNA sequencing, as measured by agreement with tissue-culture results.

RESULTS

Intraoperative and preoperative joint cultures identified an organism in 46 (87%) of 53 patients. Microbial cfDNA sequencing identified the joint pathogen in 35 cases, including 4 of 7 culture-negative cases (57%). Thus, as an adjunct to cultures, cfDNA sequencing increased pathogen detection from 87% to 94%. The median time to species identification for cases with genus-only culture results was 3 days less than standard-of-care methods. Circulating cfDNA sequencing in 14 cases detected additional microorganisms not grown in cultures. At postoperative encounters, cfDNA sequencing demonstrated no detection or reduced levels of the infectious pathogen.

CONCLUSIONS

Microbial cfDNA from pathogens causing local periprosthetic joint infections can be detected in peripheral blood. These circulating biomarkers can be sequenced from noninvasive venipuncture, providing a novel source for joint pathogen identification. Further development as an adjunct to tissue cultures holds promise to increase the number of cases with accurate pathogen identification and improve time-to-speciation. This test may also offer a novel method to monitor infection clearance during the treatment period.

LEVEL OF EVIDENCE

Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Characterization of the public transit air microbiome and resistome reveals geographical specificity

BACKGROUND

The public transit is a built environment with high occupant density across the globe, and identifying factors shaping public transit air microbiomes will help design strategies to minimize the transmission of pathogens. However, the majority of microbiome works dedicated to the public transit air are limited to amplicon sequencing, and our knowledge regarding the functional potentials and the repertoire of resistance genes (i.e. resistome) is limited. Furthermore, current air microbiome investigations on public transit systems are focused on single cities, and a multi-city assessment of the public transit air microbiome will allow a greater understanding of whether and how broad environmental, building, and anthropogenic factors shape the public transit air microbiome in an international scale. Therefore, in this study, the public transit air microbiomes and resistomes of six cities across three continents (Denver, Hong Kong, London, New York City, Oslo, Stockholm) were characterized.

RESULTS

City was the sole factor associated with public transit air microbiome differences, with diverse taxa identified as drivers for geography-associated functional potentials, concomitant with geographical differences in species- and strain-level inferred growth profiles. Related bacterial strains differed among cities in genes encoding resistance, transposase, and other functions. Sourcetracking estimated that human skin, soil, and wastewater were major presumptive resistome sources of public transit air, and adjacent public transit surfaces may also be considered presumptive sources. Large proportions of detected resistance genes were co-located with mobile genetic elements including plasmids. Biosynthetic gene clusters and city-unique coding sequences were found in the metagenome-assembled genomes.

CONCLUSIONS

Overall, geographical specificity transcends multiple aspects of the public transit air microbiome, and future efforts on a global scale are warranted to increase our understanding of factors shaping the microbiome of this unique built environment.

A comprehensive metagenomics framework to characterize organisms relevant for planetary protection

BACKGROUND

Clean rooms of the Space Assembly Facility (SAF) at the Jet Propulsion Laboratory (JPL) at NASA are the final step of spacecraft cleaning and assembly before launching into space. Clean rooms have stringent methods of air-filtration and cleaning to minimize microbial contamination for exoplanetary research and minimize the risk of human pathogens, but they are not sterile. Clean rooms make a selective environment for microorganisms that tolerate such cleaning methods. Previous studies have attempted to characterize the microbial cargo through sequencing and culture-dependent protocols. However, there is not a standardized metagenomic workflow nor analysis pipeline for spaceflight hardware cleanroom samples to identify microbial contamination. Additionally, current identification methods fail to characterize and profile the risk of low-abundance microorganisms.

RESULTS

A comprehensive metagenomic framework to characterize microorganisms relevant for planetary protection in multiple cleanroom classifications (from ISO-5 to ISO-8.5) and sample types (surface, filters, and debris collected via vacuum devices) was developed. Fifty-one metagenomic samples from SAF clean rooms were sequenced and analyzed to identify microbes that could potentially survive spaceflight based on their microbial features and whether the microbes expressed any metabolic activity or growth. Additionally, an auxiliary testing was performed to determine the repeatability of our techniques and validate our analyses. We find evidence that JPL clean rooms carry microbes with attributes that may be problematic in space missions for their documented ability to withstand extreme conditions, such as psychrophilia and ability to form biofilms, spore-forming capacity, radiation resistance, and desiccation resistance. Samples from ISO-5 standard had lower microbial diversity than those conforming to ISO-6 or higher filters but still carried a measurable microbial load.

CONCLUSIONS

Although the extensive cleaning processes limit the number of microbes capable of withstanding clean room condition, it is important to quantify thresholds and detect organisms that can inform ongoing Planetary Protection goals, provide a biological baseline for assembly facilities, and guide future mission planning. Video Abstract.

The Microbiomes of Seven Lichen Genera Reveal Host Specificity, a Reduced Core Community and Potential as Source of Antimicrobials

The High Andean Paramo ecosystem is a unique neotropical mountain biome considered a diversity and evolutionary hotspot. Lichens, which are complex symbiotic structures that contain diverse commensal microbial communities, are prevalent in Paramos. There they play vital roles in soil formation and mineral fixation. In this study we analyzed the microbiomes of seven lichen genera in Colombian Paramos using 16S rRNA gene amplicon sequencing and provide the first description of the bacterial communities associated with Cora and Hypotrachyna lichens. Paramo lichen microbiomes varied in diversity indexes and number of OTUs, but were composed predominantly by the phyla Acidobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Proteobacteria, and Verrucomicrobia. In the case of Cora and Cladonia, the microbiomes were distinguished based on the identity of the lichen host. While the majority of the lichen-associated microorganisms were not present in all lichens sampled, sixteen taxa shared among this diverse group of lichens suggest a core lichen microbiome that broadens our concept of these symbiotic structures. Additionally, we identified strains producing compounds active against clinically relevant microbial strains. These results indicate that lichen microbiomes from the Paramo ecosystem are diverse and host-specific but share a taxonomic core and can be a source of new bacterial taxa and antimicrobials.

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.