Much of our knowledge of the human microbiome comes from association studies that use either a cross-sectional or case–control design. Well-designed case–control studies are critical to demonstrate a potential relationship between microbes and a disease of interest. However, these studies cannot establish causality, and are often subject to confounding variables such as differences in diet or medication between cases and controls. Most studies are conducted at a single time point in a population with the disease, and no long-term follow-up is performed. Consequently, these studies can only identify microbes that differentiate individuals with the disease and the control population. Although these microbes identified might have been causative agents, it is nearly impossible to separate this association from secondary effects associated with the condition. For example, medication plays a major partin shaping the microbiome; a study of patients with type II diabetes mellitus found that treatment with metformin had a larger effect on the microbiome than the disease . Similarly, we hypothesize the physiology of the disease might also contribute to changes in community structure. Association studies are also often confounded by the selection of poor controls. The microbiome is dynamic , and cumulative exposures over an individual’s life, shaped by their diet , lifestyle , medical history , genetics and other factors create a unique community. Thus, if cases and controls are not correctly selected,blueberry container association studies might detect differences due to confounding factors. Matching cases and controls based on age and sex is often not sufficient. In cases in which this matching to control for confounding variables is not possible, it is critically important to collect information about potential confounding factors.
Comparisons across current cross-sectional studies are also challenging due to large effects caused by inter-study differences in technical parameters, including sample collection, storage, primer selection and analysis techniques . Differences across studies increase the challenge of meta-analysis and make identifying causative clades more difficult . Some of these problems can be ameliorated by using consistent methodology between . Efforts like the Microbiome Quality Control Project are exploring sources of technical variation , while analysis platforms like Qiita provide a database of consistently annotated studies for comparison.Twin studies provide a potential antidote to some of the problems with association studies. Twin pairs are naturally controlled for age and some early life exposures. Monozygotic twin pairs also share the same genetic background, further limiting potential confounders . Twin studies can be leveraged in two ways. First, identifying differences between discordant and concordant twin pairs represent more powerful association studies, due to the partial internal control. Although these studies are particularly useful in young children due to shared environment, the approach can also be used with adults . Second, twin studies are critical to examine genetic control of the microbiome. A study published in 2016 of the UK Twins cohort suggested strong association of the microbiome and genes, including those associated with dietary preference and serum lipids . Twin studies provide a unique opportunity to assess if the familial risk factors are either genetic or environmental in nature. These studies have been applied to study heritability for studying hepatic steatosis and fibrosis now that advanced magnetic resonance imaging based assessment can be used to phenotype individuals . However, the sample size requirements for microbiome assessment in twins is large compared to the sample sizes heeded to study heritability may making recruitment for such a study challenging .
Twin studies may not be appropriate for other rare causes of liver diseases e.g. alpha-1 antitrypsin deficiency, cirrhosis, primary biliary cholangitis, and for such low prevalence diseases a trio family design would be more appropriate and would provide the highest power with the most efficient study design to detect association of a trait such as the role of microbiome on the risk of liver diseases .As the cost of microbiome analysis decreases, longitudinal studies are becoming more common. Understanding temporal fluctuation in the microbiome, and the role of microbes in contributing to disease etiology, will rely on studies over time. Work suggests that community instability might, in and of itself, be a characteristic of an unhealthy ecosystem . Prospective studies, such as an investigation examining death from HCC in individuals with NAFLD, have helped identify the role of exposures and etiological factors in contributing to disease outcomes . Currently, the appropriate sampling frequency for understanding the microbiome in prospective studies is unknown, in part due to an overall lack of long term follow up with microbiome studies. Initially, sample collection during standard clinic visits may provide information about the population-scale changes in the population. However, incorporating microbiome samples into these long-term studies will help examine the role of microbial communities—either at a single time point or the community dynamics—as a contributing factor to complex conditions .Model animals also have an important role in shaping our understanding of the microbiome in disease . Although rodent microbial communities are distinct from the human microbiota, there are some shared physiological and microbial traits . Both rodent and human communities are dominated by the same set of bacterial phyla, although a smaller percentage of genera are shared .
As such, experimental findings implicating individual organisms or genera in rodents should be taken with caution until they are validated in humans. Instead, rodent models can show phenotypic consequences of microbiome manipulation. This aspect makes rodent models a useful model system to investigate causality, explore interactions and test early interventions. Both antibiotics and probiotics have been used to study the effect of changing the conventional mouse microbiome on a phenotypic outcome. Broad spectrum antibiotics decrease the total bacterial load, as well as causing major perturbations in the microbial communities . In some cases, such as in liver disease models, this approach can demonstrate the role of bacterial products like LPS in modulating inflammation . In other cases, like a reported addiction model, it can be used to demonstrate the importance of an intact microbiome in regulating behavior . Probiotics can also be used to investigate the effect of a specific bacteria or bacterial cocktail within a controlled environment. A study of alcoholic fatty liver disease demonstrated an attenuation of the microbiome-mediated inflammation when a probiotic was used . Gnotobiotic or germ-free mice can be used in multiple contexts. Comparisons of specific pathogen-free laboratory mice and germ-free mice can be used to examine the role of the microbiome in modulating an expressed or induced phenotype . More importantly, gnotobiotic mice can be humanized with a donor’s stool. This approach creates a system in which an individual’s microbiota can be tested, either for its ability to modulate a disease phenotype or as a target for intervention . For instance, in a small study, mice received their microbiome from a donor with either severe alcoholic hepatitis or no liver disease. Following alcohol treatment, the mice with the microbiome from the patient with alcoholic hepatitis showed greater liver damage than mice that received stool from the healthy donor . Well-designed mouse models that combine our current understanding of liver disease with humanized microbiomes offer some of the greatest potential for preclinical interventions. Avatar, or sometimes called patient-derived xenograft mice, are widely used in the cancer community to test the efficacy of chemotherapeutics for individual tumors, including HCC . This model better re-capitulates the complexity of a tumor than cell culture. Avatar mice can be further personalized by introducing a human immune system into an immuno compromised mouse, along with the tumor . Generating this model in germ-free mice with a humanized microbiome and immune system expands our capacity to understand the role of the microbiome in modulating cancer. For example,growing blueberries in containers this model could be used to study whether the microbiome of a patient with ALD leads to more tumor growth than the microbiome from a healthy individual as control. An accumulating body of research suggests that the disparate observations in liver-disease related studies can be unified and explained by the microbiome. It is now widely accepted that liver damage can result from extensive interplay between gut microbiota via specialized molecules and host-immune system via Kupffer-cell-mediated liver inflammation. However, a comprehensive understanding of the interactions between the microbiome and the liver still evades us. Animal models, particularly rodents, have been instrumental in elucidating many important mechanistic pathways in liver disease etiology. The introduction of the microbiome into these models will provide a more complete view of the cancer ecosystem. Because microbiome research is sensitive to technical variability that often masks underlying biological signals, there is a need for consistency in technical platforms and standardized protocols, so that findings from different laboratories can be replicated and validated. Additionally, it is critical to use an animal model that mimics human disease as closely as possible in all its physiological and metabolic manifestations. We are slowly advancing from observation-based studies in humans as research establishes grounds for microbiome-based therapeutic modalities such as FMT and probiotic interventions. However, effectively translating and applying findings accrued through animal models to humans requires well-designed, large-scale clinical trials spanning multiple disease etiologies and patient characteristics.
As the role of microbiota in liver disease development, prognosis and treatment is increasingly recognized, we emphasize the need for focused, microbiome-aware efforts to efficiently tackle the socioeconomic burden of this spectrum of liver diseases. The authors would like to thank D. McDonald, T. Kościółek, Z. Xu and A. Plymoth for their helpful discussions. M.K. is supported by the NIH grants R01 AI043477 and R01 CA118165. R.L. is supported in part by the grant R01-DK106419-03. Research reported in this publication was supported in part by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number P42ES010337. B.S. is supported by NIH grants R01 AA020703, U01 AA021856, U01AA24726, and by Award Number I01BX002213 from the Biomedical Laboratory Research & Development Service of the VA Office of Research and Development. J.D. is supported by the Robert Wood Johnson Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Primary biliary cholangitis is characterized by inflammation-mediated damage to the small bile ducts inside the liver, which gradually progresses to liver fibrosis and cirrhosis . Previously considered a typical autoimmune disorder, the modified etiological understanding of PBC considers proinflammatory changes in the gut-microbiota, intestinal bile acid disruptions and gut-barrier dysfunction . Consequently, microbe-associated molecular patterns ascend the biliary duct, perpetuating infection. An immune attack against the biliary epithelial cells is mediated by antibodies that recognize E2 subunit of pyruvate dehydrogenase complex due to cross-reactivity with conserved proteins in Escherichia coli, Lactobacillus delbrueckiiand Novosphingobium aromaticivoransIn fact, genetically susceptible mouse strains developed liver lesions mimicking PBC when infected with Novosphingobium aromaticivoransm, which further implicates a role for the microbiome in this disease . Ursodeoxycholic acid, a tertiary bile acid produced by Ruminococcus, has been approved for PBC treatment . Thus, microbiome-based treatment modalities hold promise for managing PBC and should be studied further. Primary sclerosing cholangitis is also an immune-mediated disease of the bile ducts . However, unlike PBC, PSC can affect bile ducts, both inside and outside of the liver. Gut dysbiosis-mediated bile dysregulation, intestinal permeability and translocation of proinflammatory molecules in the portal vein characterizes PSC . The immune reaction in PSC is mediated by autoantibodies, including perinuclear antineutrophil cytoplasmic antibody, that recognize the ubiquitously expressed bacterial antigen FtsZ . Furthermore, increase in microbe-associated Toll-like receptor expression and T helper type 17 cells has been reported in PSC, which strongly suggests microbiome involvement in disease pathogenesis . PSC is closely associated with IBD , in particular ulcerative colitis and shares some of its characteristic features . Thus, a common disease mechanism might be at play, and novel treatment avenues by targeting microbe associated immune pathways can be explored.Limited data exist concerning the diagnostic accuracy of gut-microbiome-derived signatures for detecting NAFLD-cirrhosis. Here we report 16S gut-microbiome compositions of 203 uniquely well-characterized participants from a prospective twin and family cohort, including 98 probands encompassing the entire spectrum of NAFLD and 105 of their first-degree relatives , assessed by advanced magnetic-resonanceimaging . We show strong familial correlation of gut-microbiome profiles, driven by shared housing.