The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

While recent research indicates that human health is affected by the gut microbiome, the functional mechanisms that underlie host-microbiome interactions remain poorly resolved. Metagenomic clinical studies can address this problem by revealing specific microbial functions that stratify healthy and diseased individuals. To improve our understanding of the relationship between the gut microbiome and health, we conducted the first integrative functional analysis of nearly 2,000 publicly available fecal metagenomic samples obtained from eight clinical studies. We identified characteristics of the gut microbiome that associate generally with disease, including functional alpha-diversity, beta-diversity, and beta-dispersion. Using regression modeling, we identified specific microbial functions that robustly stratify diseased individuals from healthy controls. Many of these functions overlapped multiple diseases, suggesting a general role in host health, while others were specific to a single disease and may indicate disease-specific etiologies. Our results clarify potential microbiome-mediated mechanisms of disease and reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics. IMPORTANCE The composition of the gut microbiome associates with a wide range of human diseases, but the mechanisms underpinning these associations are not well understood. To shift toward a mechanistic understanding, we integrated distinct metagenomic data sets to identify functions encoded in the gut microbiome that associate with multiple diseases, which may be important to human health. Additionally, we identified functions that associate with specific diseases, which may elucidate disease-specific etiologies. We demonstrated that the functions encoded in the microbiome can be used to classify disease status, but the inclusion of additional patient covariates may be necessary to obtain sufficient accuracy. Ultimately, this analysis advances our understanding of the gut microbiome functions that constitute a healthy microbiome and identifies potential targets for microbiome-based diagnostics and therapeutics.

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IBD patients harbor distinct microbial communities with functional capabilities different from those seen with healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading to disease onset. By performing weekly metagenomic sequencing and mixed-effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomarkers that may either enable microbes to live inside an inflamed gut or contribute to immune activation in IBD mice. Future work will validate the potential roles of these microbial pathways in host-microbe interactions and human disease. This study was novel in its longitudinal design and focus on microbial pathways, which provided new mechanistic insights into the role of gut microbes in IBD development.

24Gut microbiome research increasingly utilizes zebrafish (Danio rerio) given their amenability to 25 high-throughput experimental designs. However, the utility of zebrafish for discerning 26 translationally relevant host-microbiome interactions is constrained by a paucity of knowledge 27 about the biological functions that zebrafish gut microbiota can execute, how these functions 28 associate with zebrafish physiology, and the degree of homology between the genes encoded by 29 the zebrafish and human gut microbiomes. To address this knowledge gap, we generated a 30 foundational catalog of zebrafish gut microbiome genomic diversity consisting of 1,569,102 non-31 redundant genes from twenty-nine individual fish. We identified hundreds of novel microbial 32 genes as well as dozens of biosynthetic gene clusters of potential clinical interest. The genomic 33 diversity of the zebrafish gut microbiome varied significantly across diets and this variance 34 associated with altered expression of intestinal genes involved in inflammation and immune 35 activation. Zebrafish, mouse, and human fecal microbiomes shared > 50% of their total genomic 36 diversity and the vast majority of gene family abundance for each individual metagenome 37 (~99%) was accounted for by genes that comprised this shared fraction. These results indicate 38 that the zebrafish gut houses a functionally diverse microbial community that manifests 39 extensive homology to that of humans and mice despite substantial disparities in taxonomic 40 composition. We anticipate that the gene catalog developed here will enable future mechanistic 41 study of host-microbiome interactions using the zebrafish model. 42 43 Keywords: microbiome, metagenome, intestine, vertebrate, zebrafish, diet 44 45 46 also identify substantial homology between zebrafish, human, and mouse metagenomic diversity, 55 indicating that these microbiomes may operate similarly. 56 57 58 59 60 61 62 63 64 65 66 67 68 69 Nextera XT kit (Illumina, San Diego, CA USA) and sequenced on and Illumina HiSeq 3000 130 using a 150bp PE sequencing kit (Illumina). 131 132 Metagenome Assembly and Functional Annotation 133Each metagenomic library was demultiplexed and quality filtered with shotcleaner 134 (https://github.com/sharpton/shotcleaner) using the zebrafish genome (GRCz10). The cleaned 135 sequence files were then assembled simultaneously using MEGAHIT 15 with a minimum kmer 136 size of 27 and a kmer increment size of 10. The resulting contigs were used to predict genes 137 using prodigal in metagenome mode. CD-HIT 16 was used to collapse redundant genes (95% 138