Inflammatory bowel diseases, which include Crohn’s disease and ulcerative colitis, affect several million individuals worldwide. Crohn’s disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study’s infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi’omics Database ( http://ibdmdb.org ), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases.

To the Editor: MetaPhlAn (metagenomic phylogenetic analysis) 1 is a method for characterizing the taxonomic profiles of whole-metagenome shotgun (WMS) samples that has been used successfully in large-scale microbial community studies 2,3 . This work complements the original species-level profiling method with a system for eukaryotic and viral quantitation, strain-level identification and strain tracking. These and other extensions make the MetaPhlAn2 computational package (http://segatalab. cibio.unitn.it/tools/metaphlan2/ and Supplementary Software) an efficient tool for mining WMS samples.Our method infers the presence and read coverage of cladespecific markers to unequivocally detect the taxonomic clades present in a microbiome sample and estimate their relative abundance 1 . MetaPhlAn2 includes an expanded set of ~1 million markers (184 ± 45 for each bacterial species) from >7,500 species (Supplementary Tables 1-3), based on the approximately tenfold increase in the number of sequenced genomes in the past 2 years. Subspecies markers enable strain-level analyses, and quasi-markers improve accuracy and allow the detection of viruses and eukaryotic microbes (a full list of additions is provided in Supplementary Notes 1-3 and Supplementary Fig. 1).We validated MetaPhlAn2 using 24 synthetic metagenomes comprising 656 million reads and 1,295 species (Supplementary Note 4 and Supplementary Table 4). MetaPhlAn2 proved more accurate (average correlation: 0.95 ± 0.05) than mOTU 4 and Kraken 5 (0.80 ± 0.21 and 0.75 ± 0.22, respectively) ( Fig. 1a, Supplementary Figs. 2-9 and Supplementary Tables 5-11),with fewer false positives (an average of 10, compared with 22 and 23 for mOTU and Kraken, respectively) and false negatives (an average of 12, compared with 27 for the other two methods), even when including genomes that were absent from the reference database (Supplementary Note 4). With the adoption of the BowTie2 fast mapper and support for parallelism, MetaPhlAn2 is more than ten times faster than MetaPhlAn, and its speed is comparable to that of other tested approaches ( Supplementary Fig. 10).We applied MetaPhlAn2 to four elbow-skin samples that we sequenced from three subjects (Fig. 1b, Supplementary Note 5 and Supplementary Table 12). Our data showed that Propionibacterium acnes and Staphylococcus epidermidis dominated these sites, in agreement with expected genus-level results 6 , while providing species-level resolution. Together with these core species, we found Malassezia globosa in 93.65% of samples and confirmed it by coverage analysis (Supplementary Fig. 11). Although M. globosa is a known colonizer of the skin, its metagenomic characterization highlights the ability of MetaPhlAn2 to identify non-prokaryotic species. Phages (e.g., for Propionibacterium) and double-stranded DNA viruses of the Polyomavirus genus were also consistently detected. We subsequently profiled the whole set of 982 samples from other body sites from the Human Microbiome Project (HMP), including 219 samples sequenced after the initi...