Microbiome Projects in the Baltimore Longitudinal Study of Aging

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In 2008, National Institutes of Health (NIH) launched the NIH Human Microbiome Project (HMP) with the mission of generating research resources enabling comprehensive characterization of the human microbiota and analysis of their role in human health and disease. For the HMP project, 300 healthy individuals between the ages of 18 to 40 years old were enrolled and specimens from the oral cavity, nares, skin, gastrointestinal tract, and vagina (15 specimens from men and 18 from women) were obtained from these individuals. Data from HMP have fueled the interest in understanding how communities of microbes that harbor in bodies affect our health. This project revealed that the human body is occupied by a diversity of microbes including bacteria, fungus, and viruses. There are different communities of microbes on different body locations. Further various environmental and biological factors contribute to the diversity of microbes, however these relationships are yet to be fully explored. To date, bacteria are the microbes that have been widely studies, and several interesting discoveries with respect to health status have been reported. Several studies have shown that the gut microbiome composition change significantly with age. Several differences in bacterial number, enzymatic or pathway abundance by age through different stages of lifespan have been reported. After birth, infants acquire bacterial communities that are adult-like within three years. Children, compared to adults, also have greater beta diversity. However, the bacterial diversity within an individual, or alpha diversity, appears to increase with age. In the skin behind the ear, there was an enrichment of the metagenomically derived glyoxylate cycle pathway as well as a decrease in abundance of bacteria firmicutes with increasing age. In the gut there is also an increase in firmicutes with age and a decrease in actinobacteria and bacteriodetes. In most cases the magnitude and the amount of variance explained by these associations were low suggesting that there are other factors that contribute to the variability. In older adults, studies have observed that there are greater inter-individual variability compared to younger individuals. This may be explained by the decline in various aspects that influence dietary status from decline in dentition, salivary function, digestion, absorption and intestinal transit times which can all contribute to microbial composition. In addition, there are external factors can also contribute to the variability in elderly including environmental differences and health status. In this respect, studies have shown that bacterial communities can differ based on geographical location, diet, inflammatory and frailty status. These preliminary studies suggest that bacterial communities in the gut are intimately associated with aging phenotypes. To further understand the relationship between aging and microbial communities, we propose to investigate gut microbiome communities in subjects of Baltimore Longitudinal Study on Aging (BLSA). In the time since the FY2015 submission, we have identified fecal samples from 930 individuals between 24 101 years of age. For 513 subjects, there are repeated samples available with number of repeat visits of up to 6 visits. The follow up period ranged from 0.96 and 5.96 years, with the average follow up being 3.08 years. We propose to conduct metagenomics sequencing rather than the 16S sequencing that has been the standard for most epidemiological microbiome projects. The strength of metagenomics is that the whole-genome shotgun sequence approach captures eukaryotes (fungus) and viruses in addition to the prokaryotes (bacteria, archae). Furthermore, metagenomics sequencing provides more details at the species levels as compared to the 16S sequencing. Another advantage of using BLSA samples is that we can study both cross sectional and longitudinal trajectories of microbiome communities. This will allow us to examine causal association between microbiome and diseases to identify potential target for therapeutic purposes.