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#Introduction  The human  gut microbiome (the total collection of microbes found in in the human gut)  mediates many key biological functions and its imbalance, termed dysbiosis, is associated with a number of inflammatory and metabolic diseases from inflammatory bowel disease to asthma to obesity and insulin resistance \cite{Costello\_2012}. How to effectively shift the microbiome and restore balance, with all the interrelated immune-modulating and metabolic consequences, consequences (FEW READERS WILL KNOW WHAT YOU MEAN REGARDING THESE CONSEEQUENCES SO I WOULD DELETE OR CHANGE THIS),  is a key question for disease prevention. prevention AND TREATMENT.  The gut microbiome is influenced by a number of factors including the nature of the initial colonization at birth (i.e. (e.g.,  vaginal vs. C-section), C-section delivery),  host genotype, age  and age, however diet. As  diet isemerging as a key driver of microbiome composition and function, and is  a readily  modifiable factor,making  it an obvious target for intervention. interventions.  The Human Microbiome Project confirmed (I WOULD REPHRASE AS IT WAS NOT (1) THE PROJECT THAT DID THIS BUT SPECIFIC PAPERS AND (2) THERE WERE LOTS OF PAPERS FROM NON-HMP PROJECTS)confirmed  high inter-individual variability in the bacterial composition of the gut microbiome in healthy individuals \cite{Brownawell\_2012, Vrese\_2008}. Despite this high variability at the lower taxonomic (DEFINE)  levels, enterotypes, or distinct clusters at the genus level, were described as core community compositions that are independent of age, gender, nationality, or BMI \cite{Roberfroid\_2007}. Diet, in particular, plays a key role in determining enterotype \cite{German\_2008, Zivkovic\_2011, Claesson\_2012}. Although the core microbiota (i.e. abundant taxa) taxa (THIS SEEMS TO BE AN INCORRECT DEFINITION OF CORE - DOESNT CORE MICROBIOTA REFER TO THOSE SHARED ACROSS PEOPLE?)  are stable over longer time scales (e.g. 5 years), community composition is highly dynamic on shorter time scales (e.g. 0–50 weeks) \cite{Stella\_2006}. In fact, major shifts occur within 1 day of a significant dietary change \cite{German\_2008, Manichanh\_2010}. “Blooms” in specific bacterial groups were observed in response to controlled feeding of different fermentable fibers \cite{Ubeda\_2012}. Dietary changes affect both the structure and function of the gut microbiome in animals \cite{Bien\_2013}, and humans under controlled feeding conditions \cite{German\_2008}. Rapid shifts in microbiome composition are observed in response to change from a vegetarian to an animal based diet \cite{Claesson\_2012, Stella_2006}. An ecological perspective helps to delineate the complexity and multi-layered nature of the relationships between the microbiota, the human host, and both the nutritive and non-nutritive compounds we ingest \cite{Costello\_2012}. The concept of the human gut microbiome as a distinct ecosystem allows us to identify and characterize the components of the system, including its inputs and outputs. In this case, the inputs of the system include all of the various ingested compounds that can either serve as food substrates (e.g. complex sugars) or that can be metabolized by or that affect the metabolism of the microbiota (e.g. polyphenolic compounds, environmental chemicals, medications). Some of these inputs, such as the microbial food substrates (i.e. prebiotics) have been studied somewhat extensively. extensively (TOO OXYMORONONC).  It has been well documented that certain sugars such as galactooligosaccharides, fructooligosaccharides, and oligosaccharides found in milk act as prebiotics that support the establishment and growth of certain commensal microbial species \cite{Brownawell\_2012,Vrese\_2008, Roberfroid\_2008, German\_2008, Zivkovic\_2011}. Research has also documented the effects of antibiotics, and pathogens on the microbiota composition, its recovery or lack of recovery to baseline following resolution, and the various immunological and physiological effects of these \cite{Manichanh\_2010, Ubeda\_2012,Bien\_2013}. Yet there is little information on the effects of ingested microorganisms that are present in our diets, diets (EFFECTS ON WHAT?),  and in fact, even the basic questions of which microbes, how many of them, and how much they vary from diet to diet and meal to meal, have not been answered. We know next to nothing about the microbes we eat. eat (I GENERALLY AGREE BUT I WOULD TONE ALL THIS DOWN BECAUSE IF I WERE AN EDITOR I WOULD TRY TO FIND SOMEONE WHO WORKS ON MICROBES IN FOOD AND WHAT WE WRITE HERE SAYS BASICALLY THAT THEIR WORK SUCKS).  What is known is the microbial ecology of various specialty foods where fermentation, colonization, ripening, and/or aging are part of the preparation of these foods, for example pancetta \cite{Britten\_2013} and of course cheese \cite{2012}. The microbial ecology of the surfaces of raw plant-derived foods such as fruits and vegetables has also been characterized \cite{Leff\_2013}. There is a large base of literature on food-borne pathogens (REF). Furthermore, it is known that the microbial ecology of endemic microbes found on food surfaces can affect mechanisms by which pathogens colonize these foods (REF). A recent article showed that certain ingested microbes found in foods such as cheese and deli meats were detected in the stool of individuals who consumed them, and that furthermore they were culturable and thus survived transit through the upper intestinal tract (REF). However, the basic microbial ecology of different meals and diets, as well as the total number of live microorganisms ingested in these meals and diets are largely unknown. In this study we began the process of characterizing the microbes we eat. eat (AGAIN, I WOULD RECOMMEND TONIGHT THIS DOWN - FOCUS ON WHAT WE DID - NOT ON HOW NOVL WE THINK IT IS - THAT CN BE (BRIEFLY) MENTIONED IN THE CONCLUSION AND ABSTRACT).  The hypothesis is (AWKWARD --- OUR HYPOTHESIS?)  that the microbes we eat vary both quantitatively and compositionally in a significant way according to dietary pattern. We have selected to characterize the microbiota of three different dietary patterns in order to determine: the average total amount (  of daily microbes ingested via food and beverages and their composition in the average American adult across three different dietary patterns: 1) the Average American dietary pattern (AA) focused on convenience foods, 2) USDA recommended dietary pattern (USDA) emphasizing fruits and vegetables, lean meat, dairy, and whole grains, and 3) Vegan (VEG) dietary pattern, which excludes all animal products. Because of the vast, historical effort to make the 16S rRNA gene sequence available for hundreds of thousands of organisms, we are typically able to do a good job of characterizing the taxonomic diversity of most microbial communities. We assume that these organisms have important functional roles to play, and the most straightforward way to predict what these roles are is to use metagenomic sequencing to interrogate the entire genomes of all members of the community. Unfortunately, in many cases, the amount of microbial DNA relative to host or other environmental DNA is small enough to make metagenomic sequencing infeasible. This is the case here, where the plant and animal DNA present in the food we eat is most often much more abundant than the microbial DNA. Some exceptions may exist with respect to fermented foods, but we are equally interested the microbiota associated with a wide variety of food types.  In a case like this for which metagenomic sequencing is infeasible, another approach suggests itself. There is good evidence that a correlation exists between the evolutionary relatedness of two organisms and the similarity of their genomic content. This means that we can leverage the information obtained by sequencing the genome of one organism to predict the functional potential of another, even if that other genome is represented by a single 16S rRNA sequence. The power of this approach is increased when very many, very closely-related genome sequences are available. This predictive approach has recently been implemented in the software package PICRUSt. PICRUSt uses the phylogenetic placement of a 16S rRNA sequence within a phylogeny of sequenced genomes to “reconstruct” what the genome of the organism containing that 16S rRNA sequence might look like. (I THINK A LOT IN THESE LAST TWO PARAGRAPHS SHOULD BE MOVED TO RESULTS AND DISCUSSION - IT IS A BIT TOO MUCH DETAIL FOR AN INTRDOCUTION FROM MY POINT OF VIEW. I WOULD JUST SAY "HERE WE USED DNA SEQUENCING, CULTURE COUNTING, AND INFORMATICS METHODS TO CHARACTERIZE MICROBES IN THESE DIETS")