Paradigm shift in infection testing: identifying all pathogens using NGS.
Not all NGS is equal
The NGS technology that is most commonly used by clinical labs is called 16S sequencing. 16S sequencing is an amplicon-based method that is similar to polymerase chain reaction (PCR) testing. It creates many copies of a small portion of DNA, in this case a ~400bp region of the 16S gene. This gene is present in all bacteria and can be used to identify bacteria based upon sequencing differences in the amplified region. Comparing the sequenced fragments to a database of 16S genes can identify the types of bacteria present. This method can identify thousands of bacteria to the genus level and some to the species level, and a similar strategy can also be applied to 18S genes to identify fungi. (6-9)
Another type of NGS being used for infection testing is deep shot-gun metagenomic sequencing. This more comprehensive method creates genetic fingerprints of every living creature in a sample. Because of the complexity of the sequencing data, a robust data analytic method and a comprehensive microbial whole genome database is required for data analysis. The benefit of metagenomic sequencing is that every known bacteria, virus, parasite, and fungus can be identified in this manner. Additionally, DNA is sequenced across many regions of the genome instead of a single portion of one gene, giving better specificity than 16S sequencing.
Some labs use 16S sequencing because it is less expensive than shot-gun metagenomic sequencing, but it is crucial that the limitations of its use be understood. Because 16S sequencing relies on PCR amplification of the 16S gene, it is subject to the inherent biases of PCR. For example, bacteria that are rich in adenine and thymine (AT-rich) will amplify more easily than bacteria with more guanine and cytosine (GC-rich). This bias can result in very different bacteria identifications if the sample contains GC-rich bacteria, providing patient results that may not accurately reflect the bacterial populations and resulting in an ineffective antibiotic choice. Meanwhile, sequence diversities can cause different efficiencies on primer affinity during amplification, which will also introduce bias in the result. Additionally, 16S sequencing cannot identify viruses, parasites, or fungi. In clinical cases, it is important to have an unbiased view of all microbes present in a patient sample to give the best possible information for clinicians to develop the best possible treatment plans.
Opening Pandora's box
With shot-gun metagenomic sequencing gaining traction in clinical testing, much is being learned about human microbiomes, as well as infectious disease. There is much to learn about the intimate relationship between the human body and microbes, which comprise--six pounds of the body weight. Humans rely upon microbes to synthesize vitamins, digest food, and protect us from infection. It is becoming increasingly clear that infections are far more complex and dynamic than ever before appreciated. It is not uncommon to identify high levels of numerous pathogenic bacterial species in a single clinical sample. In many respects, we are entering uncharted territory and we are on the verge of huge discoveries that will have a profound impact on human health.
Many of the clinical cases being explored by firms utilizing deep shotgun metagenomic sequencing are patients who have suffered from chronic infection for many years. It is common to identify microbes at high levels that have never been reported as pathogens in humans. And microbes responsible for relatively rare conditions such as Mycobacterium leprae and Rickettsia felis can also be identified. The beauty of metagenomic approaches to pathogen identification is that one is not required to know what is being sought, but to simply identify what is present.
Data analytics for metagenomic sequencing require high complexity tools and deep expertise in bioinformatics. Once the raw data are analyzed, the next challenge is in interpreting the clinical relevance of the microorganisms identified in the sample. The mere presence of a microorganism in a clinical sample does not mean that there is infection; context is needed to determine the clinical significance of all findings. For example, E. coli is a normal bacterium found in fecal samples. But if the fecal sample is composed of 80 percent E. coli, and if the patient is experiencing clinical symptoms consistent with bacterial infection, this finding is likely to be clinically significant. If E. coli is identified circulating in blood, the mere presence of this bacteria at any level could be clinically significant.
Possibilities for healing
As more is learned about microbes and how they interact with the human body, the possibilities for healing patients with chronic illnesses grow. What if many autoimmune conditions are actually undiagnosed infection? What if ailments such as Alzheimer's and multiple sclerosis are caused by infection? What if the human condition and aging are heavily driven by colonization/infection by microorganisms? What if we can dramatically improve the quality of life by modulating the micro-biomes of the human body?
While the challenges of meta-genomic sequencing may seem intimidating, engaging strong strategic partners with expertise in NGS and high-complexity data analysis can ease these challenges. Pathogen testing is entering a revolutionary phase through the adoption of meta-genomic sequencing. As clinical laboratories embrace NGS, patient care will dramatically improve.
Please visit mlo-online.com for references.
By Crystal R. Icenhour, PhD
Crystal R. Icenhour, PhD, is CEO of Aperiomics, which provides metagenomic sequencing services to doctors and labs. She is an expert in infectious diseases and is committed to improving world health through better infection testing.
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|Title Annotation:||SPECIAL FEATURE :: NGS; next-generation sequencing|
|Author:||Icenhour, Crystal R.|
|Publication:||Medical Laboratory Observer|
|Date:||Sep 1, 2018|
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