A Comparative Analysis of Short Read Sequencing Platforms for Metagenomic Applications

Inquiries about the best sequencer for metagenomic Next-Generation Sequencing (mNGS) are common among our customers. While the answer depends on specific requirements, we’ve conducted an in-depth analysis to aid in your decision-making process.

A Brief Historical Context: Sanger sequencing, known as the first-generation method, sequences DNA post-synthesis. Despite the label “Next-generation Sequencing (NGS),” the advent of sequencing by synthesis technology for commercial use emerged around 2005. For two decades, the market was dominated by Thermo Fisher with Ion Torrent and Illumina, offering a variety of machines. Illumina utilizes a fluorescent tag to identify nucleotide bases added to the DNA strand, while Ion Torrent detects hydrogen release during nucleotide base addition, conducting both sequencing and synthesis concurrently.

Today, there are more short read manufacturers on the market, from Element Biosciences, Complete Genomics/MGI, to Singular Genomics and Ultima Genomics. First, we provide a very brief overview of the new entrants then we compare the options for a variety of mNGS applications. 

Complete Genomics/MGI, with over a decade of global market presence, employs rolling circle replication (RCR) to mitigate clonal errors and index hopping, thereby enhancing accuracy. Conceptually, their rolling nanoball technology enables dual sequencing by synthesis. Their product range and instruments offer diverse outputs suitable for various applications, with enhanced speed due to DNBSeq technology.

Element Biosciences‘ Aviti, a benchtop sequencer, tackles pre-facing or facing errors through specialized chemistry. The platform’s patented technology and unique chemistry bolster sequencing read accuracy and quality.

The G4 from Singular Genomics addresses flexibility and scalability needs with the capability to accommodate up to four flow cells simultaneously. This versatility enables users to tailor sequencing setups for different projects or output requirements, optimizing resource allocation.

Ultima Genomics‘ UG 100, a high-output, large-scale instrument, boasts a distinctive feature—no flow cells. Sequencing reactions, synthesis, and signal detection occur on a wafer, accompanied by automation to minimize manual intervention. Their PDM sequencing chemistry enhances accuracy, particularly in detecting single nucleotide variants at low frequencies, beneficial for oncological applications, especially utilizing cell-free DNA with minimal input and mutation frequencies.

Metagenomic Applications

For research and for clinical applications the priority of the features are slightly different. For clinical use accuracy, flexibility and turnaround times may be more important than in research settings. We evaluated the benchtop sequencers with medium output that are most appropriate for metagenomic analysis along several dimensions: 

  • Quality of the sequencing reads
  • Mapping quality
  • Total output
  • Flexibility
  • Speed
  • Cost (capital expenditure and operating expenses)

 

However, it’s essential to note that while selecting the sequencer is pivotal, achieving metagenomic goals also hinges on assay selection and the utilization of automation instrumentation. At Micronbrane Medical, we focused on developing technologies that overcome the barriers to ubiquitous adoption of mNGS in all settings.  Our Pathogen Real-Time Identification by Sequencing (PaRTI-Seq) assay and our PaRTI-Cular bioinformatic pipeline is revolutionizing infectious disease study, diagnosis and tracking from sample collection to definitive action.

Current Insights into Antimicrobial Resistance: Mechanisms, Origins, and Metagenomic Approaches

Infectious diseases have posed significant health challenges throughout human history. While the advent of antibiotics and other antimicrobials was aimed to address this concern, the persistent and widespread use of these agents has led to the emergence of antimicrobial resistance (AMR) and numerous multiple drug-resistant organisms (MDROs).

AMR occurs when bacteria, virus, fungi and parasites change over time and no longer respond to existing treatments.  In 2019 the World Health Organization (WHO) described it as one of the top ten global threats to public health – a threat to which science is playing catch-up in its efforts to mitigate.

Close to 5 million lives are lost annually due to drug-resistant infections.2 Projections suggest that without intervention, global deaths attributable to AMR could reach 10 million by 2050 (the same as cancer deaths).3 In addition to high mortality and morbidity, the economic toll of AMR is substantial. In fact, the World Bank estimates that AMR could result in US$ 1 trillion additional healthcare costs by 2050, and US$ 1 – $3.4 trillion gross domestic product (GDP) losses per year by 2030.4

 

Mechanisms of Antimicrobial Resistance

The biochemical and genetic mechanisms that cause AMR, fall into four categories: inactivation of the antimicrobial molecule; target modification; active drug efflux; and drug uptake limitation. These mechanisms are summarized below and in Table 1.

  • Inactivation of Antimicrobial Molecules: Microorganisms produce enzymes that deactivate drugs by either destroying them or adding specific chemical components, rendering the antimicrobial ineffective at its target site. These modifying enzymes, catalyzing reactions like acetylation, phosphorylation, and adenylation, induce steric hindrance, diminishing the drug’s affinity for its target and raising the bacterial Minimum Inhibitory Concentration (MIC). β-lactam resistance exemplifies this, employing β-lactamases to break amide bonds in the β-lactam ring, rendering the antimicrobial ineffective. Over 1000 β-lactamases have been identified, with more expected as bacterial evolution continues.
  • Target Modification is a key resistance mechanism involving alterations to the antimicrobial target site, impeding proper binding of the antimicrobial molecule. These sites are crucial for cellular functions during antimicrobial action. Mutational changes on the target site can reduce inhibition susceptibility while preserving essential cellular functions. In some cases, inducing resistance through modifying target structures may require additional cellular changes. An example is the acquisition of penicillin-binding transpeptidase (PBP2a) in methicillin-resistant Staphylococcus aureus.
  • Active Drug Efflux: Efflux pumps, found in families like the major facilitator superfamily (MFS), small multidrug resistance family (SMR), resistance-nodulation cell division family (RND), ATP-binding cassette family (ABC), and multidrug and toxic compound extrusion family (MATE), have the capacity to expel antimicrobial agents rapidly from the bacterial cell. This expulsion mechanism significantly contributes to multidrug resistance.
  • Drug Uptake Limitation: Bacteria vary in their ability to limit drug uptake. The outer membrane composition in organisms like gram-negative bacteria slows antimicrobial penetration. Mycobacteria’s lipid-rich outer membrane hinders hydrophilic drug entry. Organisms lacking a cell wall, such as Mycoplasma, are inherently resistant to cell wall-targeting agents. Biofilm formation protects against immune system attacks and provides defense against antimicrobial agents.

 

Origins of Antimicrobial Resistance

Microorganisms demonstrate genetic plasticity, enabling them to evolve resistance mechanisms against environmental threats, including antimicrobial agents. The development of antimicrobial resistance involves various processes:

  1. Mutational Resistance: Susceptible microbial populations undergo mutations in genes affecting drug activity, facilitating cell survival in the presence of antimicrobial agents.
  2. Spontaneous Mutations: Random mutation events, arising from replication errors or incorrect DNA strand repairs, contribute to antimicrobial resistance.
  3. Hypermutations: Certain bacterial populations enter transient states of elevated mutation rates under prolonged non-lethal antibiotic pressure. Mutators in this context confer selective advantages.
  4. Adaptive Mutagenesis: Mutations occur in slowly dividing or non-dividing cells under non-lethal selective pressure, leading to the development of resistant mutants.
  5. Horizontal Gene Transfer (HGT): Bacterial evolution is influenced by the acquisition of foreign materials through HGT mechanisms, such as transformation, conjugation, and integrons. These processes contribute to the development of antimicrobial resistance.

 

Addressing Antimicrobial Resistance through Metagenomic Next Generation Sequencing 

Metagenomic Next-Generation Sequencing (mNGS) provides unbiased, culture-independent diagnosis and surveillance of resistance mechanisms. Recognized as an indispensable tool, mNGS provides a complete genomic sequence and unparalleled structural detail on individual traits within a population, which contributes to more reliable microbial identification, definitive phylogenetic relationships, and a comprehensive catalog of traits. Additionally, mNGS can also be used for outbreak investigations, microorganism-agnostic infectious disease diagnosis, especially for novel pathogens and appropriate treatment selection.   

Understanding the mechanisms and origins of antimicrobial resistance is crucial in developing effective strategies to mitigate its impact.  Learn how our Pathogen Real-Time Identification by Sequencing (PaRTI-Seq™) complete mNGS assay provides rapid and accurate identification of resistant pathogens and invaluable insights for the management of AMR.  (https://micronbrane.com/products/)

 

Sources

  1. Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet; 399(10325): P629-655. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0
  2. Dadgostar P. Antimicrobial Resistance: Implications and Costs. Infect Drug Resist. 2019;12:3903-3910 https://doi.org/10.2147/IDR.S234610
  3. Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet; 399(10325): P629-655. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0
  4. The World Bank. 2024 Drug-Resistant Infections: A Threat to Our Economic future. Accessed March 10, 2024 https://www.worldbank.org/en/topic/health/publication/drug-resistant-infections-a-threat-to-our-economic-future
  5. D’Costa, V. M., McGrann, K. M., Hughes, D. W., and Wright, G. D. Sampling the antibiotic resistome. Science. 2006;311,374–377.
  6. Jim O’Neill. Tackling drug-resistant infections globally: final report and recommendations the review on antimicrobial resistance chaired. [Online] 2016. Available from: https://amr-review.org/sites/default/files/160518_Final paper_with cover.pdf. 
  7. Founou RC, Founou LL, Essack SY. Clinical and economic impact of antibiotic resistance in developing countries: a systematic review and meta-analysis. PLoS One. [Online]. 2017;12:e0189621. Available from: https//doi.org/10.1371/journal.pone.0189621
  8. Shrestha P, Cooper BS, Coast J, et al. Enumerating the economic cost of antimicrobial resistance per antibiotic consumed to inform the evaluation of interventions affecting their use. Antimicrob Resist Infect Control. [Online]. 2018;7(1):98. Available from: doi:10.1186/s13756-018-0384-3
  9. Martinez JL. General principles of antibiotic resistance in bacteria. Drug Discov Today. 2014;11:33–39.
  10. Cox G, Wright GD. Intrinsic antibiotic resistance: mechanisms, origins, challenges and solutions. Int J Med Microbiol. 2013;303:287–292.
  11. Davies J. Where have all the antibiotics gone? Can J Infect Dis Med Microbiol. 2006;17:287–90.
  12. Wilson DN. Ribosome-targeting antibiotics and mechanisms of bacterial resistance. Nat Rev Microbiol. 2014;12:35–48.
  13. G. Spratt. Resistance to antibiotics mediated by target alterations. Science. 1994;264:388–393.
  14. Bush K. The ABCD’s of β-lactamase nomenclature. [Internet]. 2013;19:549–559. Available from: https://doi.org/10.1007/s10156-013-0640-7
  15. Bush K, Jacoby GA. Updated functional classification of β-lactamases. Antimicrob Agents Chemother. [Internet]. 2010;54:969–976. Available from: https://doi.org/10.1128/AAC.01009-09
  16. Warinner, C., Rodrigues, J. F., Vyas, R., Trachsel, C., Shved, N., Grossmann, J., et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 2014;46:336–344.
  17. J. Piddock, R. Wise. Induction of the SOS response in Escherichia coli by 4-quinolone antimicrobial agents. FEMS Microbiol. Lett. 1987;41:289–294.
  18. Coculescu BI. Antimicrobial resistance induced by genetic changes. J Med Life. 2009;2:114–123.
  19. Aminov, R.I. Mackie. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol. Lett. 2007;271:147– 161.
  20. Angers A., Petrillo M., Patak A., Querci M., Van den Eede G. The role and implementation of next-generation sequencing technologies in the coordinated action plan against antimicrobial resistance. Joint Research Center. 2017;4-7.
  21. Schmidt, K., Mwaigwisya, S., Crossman, L.C., Doumith, M., Munroe, D., Pires, C., Khan, A.M., Woodford, N., Saunders, N.J., Wain, J., et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J. Antimicrob. Chemother. 2017;72:104–114.
  22. Köser et al. (2014). Whole-genome sequencing to control antimicrobial resistance. Trends Genet. 30(9):401–7.
  23. Han et al. (2019). mNGS in clinical microbiology laboratories: on the road to maturity. Crit Rev Microbiol 45(5-6):668-85.
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Advanced Strategies for Metagenomic Data Analysis

Nucleic acid contamination compromises mNGS data analysis which can have alarming repercussions.  In fact, a study published in the ASM Journal, “Major data analysis errors invalidate cancer microbiome findings” joins a growing body of evidence illuminating another troubling phenomenon in metagenomic Next-Generation Sequencing (mNGS) — database contamination. 

 

Types of database contamination

More must be done to understand and mitigate the issues with metagenomic data analysis such as:

  • Mis-labeling human or host DNA as microbial genomes1
  • Sequencing reads mis-aligned to other microbial species2
  • Incomplete reference genomes, especially for emerging species
  • Errors in MAG construction (QC, assembly, binning or annotations)
  • Other computational errors

In metagenomic research, database contamination can ruin study outcomes as in the above paper and many more, it also slows the adoption of mNGS use in clinical settings.  What characteristics would high-quality databases have?

 

High-Fidelity Metagenomic Data Analysis

To avoid the errors that may invalidate findings and allow mNGS to be used in the first line in clinical microbiology, analysis programs, need the following:

  • State-of-the-art computational and QC algorithms
  • Extensive and ongoing curation of the taxonomic assignments of individual pathogens
  • Proper benchmarking of microbes with unusual sequence homologies and/or close taxonomic relationships 
  • Quick and effective tracking of potentially mis-annotated and/or misrepresented pathogenic species

PaRTI-Cular™ is a bioinformatics web app four years in the making from Micronbrane Medical. The company’s strategic emphasis is on removing technical and cost barriers for the widespread use of mNGS in microbiology.  The web application was specifically developed to avoid genome mis-classification, taxonomic irregularities, erroneous variant calls and other fatal errors. 

PaRTI-Cular streamlines the analysis of metagenomic Next-Generation Sequencing (mNGS) data using our Pathogen Real-Time Identification by Sequencing (PaRTI-Seq) assay. The web app is a deeply curated, up-to-date genome database for over 1400 microorganisms. The software also automatically conducts data quality checks, removal of host genome reads, background noise cut-offs, pathogen identification.  We also developed a proprietary reference database so the platform can deliver abundance distributions and other types of statistical analyses.

With PaRTI-Cular you do not need any programming or special computers to run analyses. In fact, you can upload sample data and get results, from any computer, anywhere in the world. With the RUO version the following functionality is available:

  • PaRTI-Cular analysis applies preset parameters developed by Micronbrane Medical using Burrow-Wheeler Aligner (BWA) mapping tools. Mapping parameters such as read length, identity percentages, etc., were tested in various low biomass human clinical samples.
  • Data is only mapped to the curated database of 1400 pathogens (no other species are supported at this point.)
  • Sequencing output is transfer and PaRTI-Cular analysis uses Micronbrane Medical’s web services hosted by AWS in Singapore

The process is simple:

The next PaRTI-Cular launch will include expanded functionality for clinical settings, including:

  1. Various QC check for run validity and data quality
  2. Validated cutoff value will be implemented in the pipeline to report potential positives identified from the samples
  3. Approval process for the final report
  4. Data and analysis can be housed in user’s own AWS account and data center of selection

Please let us know if you would like to try PaRTI-Cular and be part of its development for both research and clinical use.  

 

Sources

  1. Breitwieser FP, Pertea M, Zimin AV, Salzberg SL. 2019. Human contamination in bacterial genomes has created thousands of spurious proteins. Genome Res 29:954–960
  2. Steinegger M, Salzberg SL. 2020. Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in genbank. Genome Biol 21:115.

Metagenomic Innovations Driving Culture-Independent Pathogen Identification

Communicable diseases are leading causes of mortality worldwide. Because infectious diseases have a profound impact on large populations, as experienced with the SARS COV2 pandemic, surveillance, rapid diagnosis and appropriate treatment are paramount to global health. Yet, our understanding of microorganisms is nascent, and conventional pathogen identification techniques are limited. 

Culturing for bacteria is a century-old method that takes days to weeks and has low diagnostic yield. In an effort to gain more microbial knowledge, molecular modalities, including polymerase chain reaction (PCR) and serology, evolved. Yet, even these advancements are constrained in their ability to detect mixed infections, novel pathogens, and exhibit variable sensitivity and specificity. The absence of a single, comprehensive way to identify pathogens necessitates the use of multiple types of tests, which can lead to delays and escalated healthcare costs. 

Metagenomic Next-Generation Sequencing (mNGS) provides an unbiased, culture-independent method to explore microbial dynamics in-depth. Despite the immense potential to accelerate our understanding of microorganisms, mNGS’s slow adoption, especially  in clinical settings, is due to technical and cost challenges. 

Micronbrane Medical systematically addresses these barriers, with a complete suite of products spanning five categories: specimen collection, host depletion, enrichment and purification, library preparation, and bioinformatic analysis. Our mNGS-enabling technologies together create a fast, efficient and cost-effective assay called PaRTI-Seq to revolutionize infectious disease study, diagnosis, tracking, and treatment.

 
Overcoming the challenges of mNGS

A primary issue with mNGS is host interference due to the significantly larger size of eukaryotic host genomes compared to prokaryotic genomes. Without any way to remove host cells, only a small proportion of sequencing reads correspond to potential pathogens. The abundance of host genetic material in samples reduces microbiome profile accuracy, leading to negative downstream consequences such as increasing the cost of mNGS and data analysis errors. 

However, Micronbrane Medical’s patented Devin™ host depletion filter removes greater than 99 percent of host nucleated cells from up to 10 mL of biological fluid in just five minutes. Devin uses a novel Zwitterionic Interface Ultra-Self-assemble Coating (ZISC) technology not size exclusion to deplete host cells. The Zwitterionic material is a cross-linked polymer with alternating positive and negative ions, which creates a tight hydration layer. The positive and negative ions interact with the hydrophilic proteins of host cells, retaining them in the filter, while allowing microorganisms to pass through unaltered.  

By depleting host nucleated cells, microbial DNA can then be enriched. Specifically manufactured with mNGS-grade reagents, the kit minimizes reagent contamination and enhances microbial reads by 10 – 1000 fold, depending on the pathogen load and sample type.

Pre-extraction host depletion necessitates a library kit especially made for low-input DNA, Micronbrane Medical developed the Unison Ultralow Library Preparation Kit. This kit, capable of generating libraries from as little as 10 pg DNA extract, accelerates DNA library preparations through a proprietary tagmentation-based method, condensing fragmentation, adapter ligation, and normalization into a single step. 

By effectively reducing host contamination and utilizing the Unison Library Kit, the sequencing depth required for a representative microbiome sample can be significantly decreased, thereby reducing sequencing costs and shortening overall workflow time. Both the nucleic acid purification and NGS library construction steps can be automated for clinical settings.

To streamline the entire mNGS workflow, our PaRTI-Cular Bioinformatic Web App ensures a complete report within 30 minutes per sample. The software automates data quality checks, removal of host genome reads, pathogen identification, and statistical analyses while retaining the data in their own cloud account or a hosted solution.

According to a recent pre-print, the PaRTI-Seq assay streamlined the sample-to-result processes, reducing the overall turn around time and cost per sample. Moreover, the gDNA-based PaRTI-Seq assay performed even better than cell-free DNA-based mNGS, showing an average reads-per-million (RPM) of 2,359 compared to only 95 by a cfDNA-based method. The study concluded that mNGS with the Devin filter was able to recover most of the pathogens identified by clinical BC and achieved the highest diagnostic yield. With the clinical implementation to complete the workflow within 24 hours, it has the potential to overcome slow turnaround and low diagnostic yield issues of traditional microbiology tests.

Utilized as a comprehensive mNGS solution, the combination of PaRTI-Seq and PaRTI-Cular yields cost-effective, actionable results within an unprecedented 24 hour timeframe.  By removing the barriers to mNGS use we hope to help expand the use of mNGS in research, clinical, and industrial applications globally.