The Challenge and Potential of Metagenomics in the Clinic

Abstract

The bacteria, fungi, and viruses that live on and in us have a tremendous impact on our day-to-day health and are often linked to many diseases, including autoimmune disorders and infections. Diagnosing and treating these disorders relies on accurate identification and characterization of the microbial community. Current sequencing technologies allow the sequencing of the entire nucleic acid complement of a sample providing an accurate snapshot of the community members present in addition to the full genetic potential of that microbial community. There are a number of clinical applications that stand to benefit from these data sets, such as the rapid identification of pathogens present in a sample. Other applications include the identification of antibiotic-resistance genes, diagnosis and treatment of gastrointestinal disorders, and many other diseases associated with bacterial, viral, and fungal microbiomes. Metagenomics also allows the physician to probe more complex phenotypes such as microbial dysbiosis with intestinal disorders and disruptions of the skin microbiome that may be associated with skin disorders. Many of these disorders are not associated with a single pathogen but emerge as a result of complex ecological interactions within microbiota. Currently, we understand very little about these complex phenotypes, yet clearly they are important and in some cases, as with fecal microbiota transplants in Clostridium difficile infections, treating the microbiome of the patient is effective. Here, we give an overview of metagenomics and discuss a number of areas where metagenomics is applicable in the clinic, and progress being made in these areas. This includes (1) the identification of unknown pathogens, and those pathogens particularly hard to culture, (2) utilizing functional information and gene content to understand complex infections such as Clostridium difficile, and (3) predicting antimicrobial resistance of the community using genetic determinants of resistance identified from the sequencing data. All of these applications rely on sophisticated computational tools, and we also discuss the importance of skilled bioinformatic support for the implementation and use of metagenomics in the clinic.

A New Age in Molecular Diagnostics for Invasive Fungal Disease: Are We Ready?

“…Sequences obtained from clinical samples through NGS can then be identified to the genus and species level by using sequence alignment tools such as BLAST or WIMP (Camacho et al, 2009;Juul et al, 2015) against appropriate publicly available quality-controlled reference sequence databases, e.g., ISHAM barcoding database UNITE, RefSeq, and BOLD (Hebert et al, 2003;Kõljalg et al, 2013;Schoch et al, 2014;Meyer et al, 2019). However, there are currently a number of major limitations in this technology which may lead to inaccurate or insufficient identification of the fungal pathogen, including: (i) pre-PCR biases, such as sample handling, contamination introduced during sample collection, aliquoting, nucleic acid extraction, library preparation, or pooling (Salter et al, 2014;Strong et al, 2014), DNA extraction methods including the choice of storage buffer and extraction kit (Hallmaier-Wacker et al, 2018), the quantity of host DNA, which could be reduced by using various depletion methods (Hasan et al, 2016), and the issues related to the extraction of DNA of adequate quality (high purity, high molecular weight, and high concentration) (Hasan et al, 2016;Hallmaier-Wacker et al, 2018;Sanderson et al, 2018;Nicholls et al, 2019); (ii) PCR biases, including primer mismatches and variable length of the obtained amplicon (Schloss and Westcott, 2011;Boers et al, 2019); (iii) high sequencing error rate of the current NGS technologies, especially long read sequencing (Bakker et al, 2012;Schirmer et al, 2015;Tyler et al, 2018); (iv) the lack of complete and quality-controlled reference sequence databases with correct taxonomic assignment (Irinyi et al, 2016;Greninger, 2018); and (v) lack of appropriate bioinformatic tools, including alignment algorithms and cross-talk between fungal sequences (Mulcahy-O'Grady and Workentine, 2016;Chiu and Miller, 2019). As such, any DNA metabarcoding-based pathogen identification should be interpreted and reviewed in the clinical context of the disease symptoms.…”

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

“…Metagenomics allows for identification and in-depth studies of unknown viral pathogens by using shotgun sequencing [42][43][44], which has been extensively applied in clinical and environmental research [45,46]. Nucleic acids from a sample undergo virtually unbiased sequencing, i.e., with minimum prejudice towards specific organisms [47,48]; in theory, the method can be used to analyze a potentially unlimited range of targets [49,50].…”

Translational metagenomics and the human resistome: confronting the menace of the new millennium

“…Since only a minority of bacterial organisms can be cultured [15–17], and since most of these non-pathogenic commensals are thought to constitute an important resistance gene pool that can be transmitted to human pathogens [18], we need to extend the methodological spectrum for detection of ARGs. Non-culture-based approaches such as metagenomics can overcome these common limitations [19, 20].…”