“Sustainable solutions based on innovation can create a more resilient world only if that innovation is focused on the health and well-being of its inhabitants. And it is at that point – where technology and human needs intersect – that we will find meaningful innovation.” – Franz Van Houten
Culture-based techniques were revolutionary when they first started use in diagnostics settings. Finally, doctors had methods to detect the microorganisms causing tuberculosis, pneumonia, and other deadly diseases. In the subsequent years, more impressive methods such as antigen testing and PCR became effective tools in pathogen identification.
Excellent as these methods were initially, all three methods have serious limitations in the scope of their detection. In 1953 Watson and Crick made the discovery of DNA and to date we still have difficulty taking full advantage of this information. In 1977, Carl Woese wrote a paper shattering the known tree of life into 3 pieces looking at the DNA sequences of Eukaryotes, Archaea, and Bacteria. Unfortunately, this information could not be used in a shotgun approach finding EVERY species at once. Every test had to be individually tailored towards a couple species at most for any given test. NGS is the answer to doctors and researcher’s guesswork and frustration. In 1992, the very first NGS like technology was created by Lynx Therapeutics, which would later be acquired by Solexa and eventually Illumina.
This technology was not actually useful in research until the early 2000s and, as shown above, cost hundreds of millions of dollars…
In the last few years, this technology is finally at our disposal, instead of $20-$300 per species using culture we can search the totality of the sample to find the pathogens for pennies per tested species as shown below. It is this cusp of the cutting edge that Aperiomics technology stands.
Shotgun metagenomic sequencing is a process by which all the genetic information contained within the cells of an organism are processed and turned into a file containing combinations of the four nucleotides, A T C G, that make up all living organisms DNA. The DNA is chopped into pieces of some length depending on the type of sequencing. In some sequencing methods like Illumina these sections are then split and recombined with nucleotides that have a letter specific tag on them. When these millions of sequence reads are recombined letter by letter the tag is removed and creates a fluorescent signal which is read by the machine. Some other sequencing technologies like IonTorrent use a digital chip for reading the particles that come off in recombination. This data is then sent to our lab for the hard part.
One of the hardest parts of this step is trying to eliminate any kind of foreign DNA from being introduced into the sample. For fecal samples this is less an issue due to the high amounts of bacterial DNA present. All samples types must have a specific collection procedure and DNA library kit preparation in order to maximize efficacy and reduce contaminate DNA.
The uniqueness of Aperiomics services lies not just in the sequencing, but in our analytic algorithms and software Xplore-PATHO℠. In order to make sense of the sequencing information, there needs to be a way to compare every single one of the millions of DNA fragments to tens of thousands of organismal or viral genomes. Software that does this is commonly referred to as a bioinformatic metagenomic pipeline. There are many different approaches to this problem such as the k-mer approach, which a few of the prominent experimental pipelines use. Xplore-PATHO℠ uses a unique method, but you can see some of the similarities and differences in the result in the benchmark. Due to the complexity and common elements shared between different species, the alignment is not straightforward and very computationally taxing.
Aperiomics software has been concentrating resources on this problem with some of the brightest minds in bioinformatics since our inception and continues to make strides towards the highest quality service to the health benefit of our patients.
The Aperiomics analytic algorithms and software Xplore-PATHO℠ must run on a curated database in order to run smoothly. The database formation is integral to running the software and the detailed maintenance gives our services the precision and depth you can’t find anywhere else. If you have a genetic library full of tens of thousands of broken, disorganized, or simply incorrect genomes, you won’t be able to find out what series your collection of DNA belong to. It is not enough to simply have the largest library, you need to have the largest high-quality library.
If you do not have a properly curated database, you can bias the algorithms and obscure the true microbial representation in the sample. Trying to accurately pull out the most efficient, correct, yet comprehensive database takes a lot of thorough testing. The Aperiomics Microbial Database™ has been taken from a variety of sources and is constantly being scoured for accuracy.
PCR, 16S NGS, or Deep Shotgun Metagenomic NGS . . .
Not all NGS are created equal
There are many different methods clinicians have at their disposal these days in the diagnosis of pathogens. Culture and antigen-based methods are a fraction of the cost yet far less sensitive than methods using PCR. PCR, despite its sensitivity and accuracy, is typically limited to 8-20 species based on the panel size. Doctors, patients, and insurance providers deserve more widely applicable, cost-effective, and reliable testing.
Next Generation Sequencing is the future, but the different NGS types are near incomparable. While 16S is handy for discovering present bacteria by their 16S ribosomal sections, there is more to research and diagnosis. If the test claims to use “NGS” and only does bacteria, this is most likely 16S sequencing technology. 16S sections of the genome are less than .01% of the total genome, which can lead to a lot of information missed. Some species have 16S sections too similar to determine the differences between them. In order to analyze sample specific genes and appropriate treatment, you’ll need to look deeper.
Shotgun metagenomic sequencing is able to look at all that 16S is able to find in addition to viral, human, fungi, and parasite DNA. If you have high amounts of Zika virus, a brain-eating amoeba, or rare fungi in the sample you won’t be able to find it using 16S. That isn’t to say it is not useful, but the field is slowly shifting away due to a lack of complete information provided as better technology gets cheaper. Shotgun metagenomics is the future of pathogen detection in both research and medical diagnosis due to the amount of information gained. Instead of looking at one small section of one type of microorganism, shotgun sequencing looks at a sampling of all DNA present. The greatest strength and drawback to Shotgun Metagenomics is its sensitivity. If there is a small amount of E. coli contamination from seemingly insignificant mishandling of the sample like a doctor or researchers finger touching the top of the vial, that bacterial species is going to throw off the analysis. This also means that if kept without any contamination, the power to determine the sample contents is very strong.
Pathogens per test*
*Percentage calculated looking at an older, but highly cited information. True numbers are difficult to ascertain with fluctuating NGS databases.
Core Algorithm Comparisons
A comparative analysis looking at how Aperiomics’ core Algorithm Xplore-PATHO℠ stacks up to other leading metagenomic pipeline software in the field. Some of these programs did not calculate an adjusted rate based on bacterial genome size and have been manually recalculated for fair comparison.
- Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, Luo S, McCurdy S, Foy M, Ewan M, Roth R, George D, Eletr S, Albrecht G, Vermaas E, Williams SR, Moon K, Burcham T, Pallas M, DuBridge RB, Kirchner J, Fearon K, Mao J, Corcoran K (2000). Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nature Biotechnology. 18 (6): 630–34. doi:10.1038/76469
- Caliendo, A. M., Gilbert, D. N., Ginocchio, C. C., Hanson, K. E., May, L., Quinn, T. C., Tenover, F. C., Alland, D., Blaschke, A. J., Bonomo, R. A., Carroll, K. C., Ferraro, M. J., Hirschhorn, L. R., Joseph, W. P., Karchmer, T., MacIntyre, A. T., Reller, L. B., Jackson, A. F., Infectious Diseases Society of America (IDSA) (2013). Better tests, better care: improved diagnostics for infectious diseases. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 57 Suppl 3(Suppl 3), S139-70.
- Carlos, N.,Tang, Y.,Pei, Z. (2012) Pearls and pitfalls of genomics-based microbiome analysis. Emerging Microbes & Infections 1(e45).
- Chen, Derrick J et al. “Utility of PCR, Culture, and Antigen Detection Methods for Diagnosis of Legionellosis” Journal of clinical microbiology vol. 53,11 (2015): 3474-7.
- Compeau, P. E., Pevzner, P. A., & Tesler, G. (2011). How to apply de Bruijn graphs to genome assembly. Nature biotechnology, 29(11), 987-91. doi:10.1038/nbt.2023
- Couto, Natacha et al. “Critical steps in clinical shotgun metagenomics for the concomitant detection and typing of microbial pathogens” Scientific reports vol. 8,1 13767. 13 Sep. 2018, doi:10.1038/s41598-018-31873-w
- Florian P. Breitwieser, Jennifer Lu, Steven L. Salzberg(2017); A review of methods and databases for metagenomic classification and assembly, Briefings in Bioinformatics, bbx120.
- Haft, D. H., DiCuccio, M., Badretdin, A., Brover, V., Chetvernin, V., O’Neill, K., Li, W., Chitsaz, F., Derbyshire, M. K., Gonzales, N. R., Gwadz, M., Lu, F., Marchler, G. H., Song, J. S., Thanki, N., Yamashita, R. A., Zheng, C., Thibaud-Nissen, F., Geer, L. Y., Marchler-Bauer, A., … Pruitt, K. D. (2017). RefSeq: an update on prokaryotic genome annotation and curation. Nucleic acids research, 46(D1), D851-D860
- Head, S. R., Komori, H. K., LaMere, S. A., Whisenant, T., Van Nieuwerburgh, F., Salomon, D. R., & Ordoukhanian, P. (2014). Library construction for next-generation sequencing: overviews and challenges. BioTechniques, 56(2), 61-4, 66, 68, passim. doi:10.2144/000114133
- Khot, Prasanna D and David N Fredricks. “PCR-based diagnosis of human fungal infections” Expert review of anti-infective therapy vol. 7,10 (2009): 1201-21.
- Mancini, N., Carletti, S., Ghidoli, N., Cichero, P., Burioni, R., & Clementi, M. (2010). The era of molecular and other non-culture-based methods in diagnosis of sepsis. Clinical microbiology reviews, 23(1), 235-51.
- Rhodes, J., Beale, M. A., & Fisher, M. C. (2014). Illuminating choices for library prep: a comparison of library preparation methods for whole genome sequencing of Cryptococcus neoformans using Illumina HiSeq. PloS one, 9(11), e113501. doi:10.1371/journal.pone.0113501
- Shelton, S.(2018) 16S sequencing vs. Shotgun metagenomics: Which one to use when it comes to microbiome studies. https://blog.genohub.com/2018/04/12/16s-sequencing-vs-shotgun-metagenomics-which-one-to-use-when-it-comes-to-microbiome-studie/ Accessed 11/29/2018
- Uruburu F. History and services of culture collections. 2003. International Microbiol. 6:101–103
- Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: www.genome.gov/sequencingcostsdata. Accessed 11/27/2018
- Woese, C. R., & Fox, G. E. (1977). Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proceedings of the National Academy of Sciences of the United States of America, 74(11), 5088-90
- Zhou, Jizhong et al. “High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats” mBio vol. 6,1 e02288-14. 27 Jan. 2015, doi:10.1128/mBio.02288-14