Aaron W Miller1,2. 1. Department of Urology, Cleveland Clinic, Cleveland, OH, USA. 2. Department of Cardiovascular and Metabolic Sciences, Cleveland, OH, USA.
Abstract
Entities:
Keywords:
Antibiotics; Dysbiosis; High fat; High sugar
Over the last decade, research into the microbiome has blossomed due in part to advances in high throughput analytical technologies
(collectively considered “multi-omics”) that allow for the inquiry of highly complex biological systems such as the
host-associated microbiome[1]. During this time, considerable evidence has emerged for the
important role that the microbiome plays in maintaining health[2,3]. The importance of the microbiome in health and disease is derived in part from the fact that the number of
bacteria that inhabits our bodies is roughly equal to the number of our own human cells and that the number of microbial genes present in
and on our bodies outnumbers our own genes by a factor of 20–30[4,5]. Thus, while most of the attention on the microbiome has historically focused on infectious
disease, given recent evidence of the ubiquity and health benefits of the microbiome, infectious disease should be considered the
exception rather than the rule.With the discovery of antibiotics nearly 100 years ago, we experienced a cultural shift that changed the medical field and made
previously lethal infections manageable[1]. Additionally, the industrial revolution brought
about agricultural advances that led to both an increase in per capita caloric intake and a shift in the sources of those
calories[2]. While the advent of antibiotics and a shift towards greater
availability of high caloric foods has considerably reduced deaths due to infectious disease and malnutrition, these cultural trends
coincide both spatially and temporally with the emergence of non-communicable, chronic diseases such as obesity, diabetes, allergies, and
other inflammatory disorders[3]. The spatiotemporal link between antibiotic use, changes in
diet, and the rise of numerous chronic diseases led to the hypothesis that changes to the microbiome was driving these disease
processes[4,5]. However, testing this
hypothesis has been difficult, due in part to a lack of an underlying framework to determine cause and effect for such a complex
system[6]. One way to determine causality between microbiome and disease states has
been to perform microbial transplants into germ-free mice. Such studies have been conducted for obesity, liver disease, and
encephalomyelitis among other disease states[7-9].Many clinical microbiome studies have been microbiome-wide association studies (MWAS)[10]. In these studies, authors will typically link a difference in microbiome composition between a healthy and disease
population as ‘dysbiosis’[11]. However, two relatively unique aspects of the
microbiome make this definition of dysbiosis problematic. First, given the high levels of diversity within the microbiome, the data from
the high-throughput studies often used to define dysbiosis are necessarily hypervariable. Second, there is considerable inter-individual
and temporal variability in microbiome composition[12,13]. Taken together, this means that when you examine two populations with completely stochastic metadata and no
distinguishing phenotypes to define the populations, there will be significant differences in at least some components of the microbiome,
even though no common form of dysbiosis exists within any one of the populations. Thus, a more concrete definition of dysbiosis is
needed.In Miller et al. 2019[14], we specifically defined two different
types of dysbiosis. Gain of function dysbiosis is that associated with infectious disease, whereby a shift in the host microbiome leads to
the emergence of pathogenic bacteria and associated functions that then give rise to disease processes. Loss of function dysbiosis is
defined by a shift in the host microbiome whereby bacteria that normally provided beneficial functions are lost and disease processes
arise as a result. Additionally, a combination of loss and gain of function dysbiosis may be required for disease. Such is the case of
chronic infection by Clostridioides difficile, whereby chronic antibiotic use can lead to the eventual overgrowth by
pathogenic C. difficile[15].To move beyond simple MWAS studies and determine if the microbiome actually contributes to a disease or disease process is
relatively straightforward. If antibiotics or some other bactericidal factor, such as those from dietary sources[16], eliminates a disease or disease process or if microbial transplants cause a disease or disease
process, then gain of function dysbiosis contributes to the disease. If the bactericidal factor leads to the emergence of a disease or
disease process, or microbial transplants eliminate the disease or disease process, then loss of function dysbiosis contributes to the
disease. Finally, if neither bactericidal treatments nor microbial transplants impact disease processes, then microbial dysbiosis does not
contribute in any way.In Miller et al.[14], we focused on a specific microbial function,
oxalate metabolism, that is exclusive to bacteria in the gut. Specific oxalate-degrading bacteria have been negatively correlated to both
antibiotic use and urinary stone disease (USD) as determined by PCR or culture-based means and suggests that these bacteria are important
for the inhibition of USD[17,18]. For cause-effect
studies of dysbiosis, it is important to identify the specific microbial functions associated with disease in order to determine if
changes in the microbiome actually causes disease or is the result of the disease. Thus, in Miller et al.[14], we confirmed in animal studies that both antibiotics (Cefazolin) and a high fat, high
sugar diet leads to a persistent loss of oxalate-degrading bacteria and their function. Interestingly, oxalate metabolism did not return
in our antibiotic-treated animals after nine days, even though much of the gut microbiota composition did, which suggests that necessary
microbe-microbe interactions were not restored.Once it has been determined that dysbiosis does contribute to a disease or disease process, then it is important to further
determine the specific location where dysbiosis is most important for the disease. Given the density and diversity of microbes in the gut,
many studies focus solely on dysbiosis associated with the gut microbiota, giving rise to terms such as the ‘gut-brain
axis’, ‘gut-liver axis’, or ‘gut-kidney axis’. However, in a recent clinical study, we found that even
though patients with USD exhibited dysbiosis associated with the loss of the oxalate-degrading microbial network in the gut, all
quantified metrics including association with antibiotic use, integration with metabolomic data, and association with other disease risk
factors pointed to the urinary tract microbiome as having a greater level of dysbiosis than the gut microbiome[18]. Oral antibiotics can drive the loss of microbial diversity at many sites and lead to dysbiosis.
Thus, for the development of bacteriotherapy designed to restore microbial functions, it is important to understand what forms of
dysbiosis are most relevant for a disease.As the biomedical microbiome field matures, it is important to move towards a mechanistic understanding of dysbiosis. This
understanding can be achieved through a systematic process that asks the questions: 1) Does dysbiosis contribute to the disease?; 2) What
locality of dysbiosis is most relevant to disease processes?; and 3) What microbial functions are lost or gained that contributes to the
emergence of disease? Beyond MWAS studies, these questions must be addressed through a combination of bactericidal treatments/associations
and microbial transplants of whole communities. However, to refine the specific microbial mechanisms associated with disease, specifically
defined consortia of bacteria must be used for transplants ideally in combination with genetic knock-outs or knock-ins. It is only through
these types of studies that the microbiome can be definitively linked to specific disease processes.
Authors: Vanessa K Ridaura; Jeremiah J Faith; Federico E Rey; Jiye Cheng; Alexis E Duncan; Andrew L Kau; Nicholas W Griffin; Vincent Lombard; Bernard Henrissat; James R Bain; Michael J Muehlbauer; Olga Ilkayeva; Clay F Semenkovich; Katsuhiko Funai; David K Hayashi; Barbara J Lyle; Margaret C Martini; Luke K Ursell; Jose C Clemente; William Van Treuren; William A Walters; Rob Knight; Christopher B Newgard; Andrew C Heath; Jeffrey I Gordon Journal: Science Date: 2013-09-06 Impact factor: 47.728
Authors: Gilberto E Flores; J Gregory Caporaso; Jessica B Henley; Jai Ram Rideout; Daniel Domogala; John Chase; Jonathan W Leff; Yoshiki Vázquez-Baeza; Antonio Gonzalez; Rob Knight; Robert R Dunn; Noah Fierer Journal: Genome Biol Date: 2014-12-03 Impact factor: 13.583