In particular, dysfunctional HDL is postulated to contribute to CVD risk in part via loss of HDL-associated sphingosine-1-phosphate (S1P). Insulin resistance is a core pathophysiologic mechanism that causes abnormal carbohydrate metabolism and atherogenic changes in circulating lipoprotein quantity and function. The prevalence of metabolic syndrome continues to increase globally and heightens the risk for cardiovascular disease (CVD). These insights warrant in-depth analysis of TI in responsive versus nonresponsive immune cells and suggest that modulating TI may provide a strategy for treating infections and inflammatory diseases. The authors also identified a similar population of monocytes in various human diseases or in individuals vaccinated with bacillus Calmette-Guérin. Interestingly, the presence of lymphocytes in the microenvironment of monocytes substantially impacted TI. The induction of TI in human monocytes resulted in three populations with distinct transcriptomic profiles. In this issue of the JCI, Zhang, Moorlag, and colleagues tackle this question by combining an in vitro model system of TI with single-cell RNA sequencing. One fundamental question is whether the induction of TI generates a homogeneous or heterogeneous population of trained cells. Finally, we share insights as to how NGS might further be integrated into and advance microbiome research and clinical care in the coming years.Īlthough the memory capacity of innate immune cells, termed trained immunity (TI), is a conserved evolutionary trait, the cellular and molecular mechanisms involved are incompletely understood. We further present examples of how NGS studies of the human microbiome have advanced our understanding of human disease pathophysiology across diverse clinical contexts, including the development of diagnostics and therapeutics. We also discuss pros and cons of each methodology as well as important concepts in data variability, study design, and clinical metadata collection. In this Review, we describe the fundamentals of NGS, with a focus on 16S rRNA and shotgun metagenomic sequencing. The choice of which NGS methodology to pursue for a given purpose is often unclear for clinicians and researchers.
Key NGS methods include 16S rRNA gene sequencing, shotgun metagenomic sequencing, and RNA sequencing.
Next-generation sequencing (NGS) technology has advanced our understanding of the human microbiome by allowing for the discovery and characterization of unculturable microbes with prediction of their function. As genetic testing becomes increasingly a part of all areas of medicine, familiarity with genetic testing approaches and result interpretation is vital to decrease the burden of undiagnosed disease.
We provide background on commonly used genetic testing approaches, give examples of phenotypes in which the various genetic testing approaches are used, describe types of genetic and genomic variations, cover challenges in variant identification, provide examples in which next-generation sequencing (NGS) failed to uncover the variant responsible for a disease, and discuss opportunities for continued improvement in the application of NGS clinically. As genetic testing is increasingly used in primary care, many subspecialty clinics, and various inpatient settings, it is important that non-genetics providers have a fundamental understanding of the strengths and weaknesses of various genetic testing strategies as well as develop an ability to interpret genetic testing results. The goal of this Review is to increase the familiarity of genetic testing strategies for non-genetics providers. Despite advancements in genetics and genomics technologies as well as increased understanding of genomic function and dysfunction, many genetic diseases continue to be difficult to diagnose. Rare genetic disorders, when considered together, are relatively common.
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