The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from mul-tiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes

Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/human​n. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.National Institutes of Health (U.S.) (U54HG004968

Enigmatic persistence of dissolved organic matter in the ocean

Marine dissolved organic matter (DOM) contains more carbon than the combined stocks of Earth’s biota. Organisms in the ocean continuously release a myriad of molecules that become food for microheterotrophs, but, for unknown reasons, a residual fraction persists as DOM for millennia. In this Perspective, we discuss and compare two concepts that could explain this persistence. The long-standing ‘intrinsic recalcitrance’ paradigm attributes DOM stability to inherent molecular properties. In the ‘emergent recalcitrance’ concept, DOM is continuously transformed by marine microheterotrophs, with recalcitrance emerging on an ecosystems level. Both concepts are consistent with observations in the modern ocean, but they imply very different responses of the DOM pool to climate-related changes. To better understand DOM persistence, we propose a new overarching research strategy — the ecology of molecules — that integrates the concepts of intrinsic and emergent recalcitrance with the ecological and environmental context

Temporal and Causal Inference with Longitudinal Multi-omics Microbiome Data

Microbiomes are communities of microbes inhabiting an environmental niche. Thanks to next generation sequencing technologies, it is now possible to study microbial communities, their impact on the host environment, and their role in specific diseases and health. Technology has also triggered the increased generation of multi-omics microbiome data, including metatranscriptomics (quantitative survey of the complete metatranscriptome of the microbial community), metabolomics (quantitative profile of the entire set of metabolites present in the microbiome\u27s environmental niche), and host transcriptomics (gene expression profile of the host). Consequently, another major challenge in microbiome data analysis is the integration of multi-omics data sets and the construction of unified models. Finally, since microbiomes are inherently dynamic, to fully understand the complex interactions that take place within these communities, longitudinal studies are critical. Although the analysis of longitudinal microbiome data has been attempted, these approaches do not attempt to probe interactions between taxa, do not offer holistic analyses, and do not investigate causal relationships. In this work we propose approaches to address all of the above challenges. We propose novel analysis pipelines to analyze multi-omic longitudinal microbiome data, and to infer temporal and causal relationships between the different entities involved. As a first step, we showed how to deal with longitudinal metagenomic data sets by building a pipeline, PRIMAL, which takes microbial abundance data as input and outputs a dynamic Bayesian network model that is highly predictive, suggests significant interactions between the different microbes, and proposes important connections from clinical variables. A significant innovation of our work is its ability to deal with differential rates of the internal biological processes in different individuals. Second, we showed how to analyze longitudinal multi-omic microbiome datasets. Our pipeline, PALM, significantly extends the previous state of the art by allowing for the integration of longitudinal metatranscriptomics, host transcriptomics, and metabolomics data in additional to longitudinal metagenomics data. PALM achieves prediction powers comparable to the PRIMAL pipeline while discovering a web of interactions between the entities of far greater complexity. An important innovation of PALM is the use of a multi-omic Skeleton framework that incorporates prior knowledge in the learning of the models. Another major innovation of this work is devising a suite of validation methods, both in silico and in vitro, enhancing the utility and validity of PALM. Finally, we propose a suite of novel methods (unrolling and de-confounding), called METALICA, consisting of tools and techniques that make it possible to uncover significant details about the nature of microbial interactions. We also show methods to validate such interactions using ground truth databases. The proposed methods were tested using an IBD multi-omics dataset

An integrative approach to understanding microbial diversity: from intracellular mechanisms to community structure

Trade-offs have been put forward as essential to the generation and maintenance of diversity. However, variation in trade-offs is often determined at the molecular level, outside the scope of conventional ecological inquiry. In this study, we propose that understanding the intracellular basis for trade-offs in microbial systems can aid in predicting and interpreting patterns of diversity. First, we show how laboratory experiments and mathematical models have unveiled the hidden intracellular mechanisms underlying trade-offs key to microbial diversity: (i) metabolic and regulatory trade-offs in bacteria and yeast; (ii) life-history trade-offs in bacterial viruses. Next, we examine recent studies of marine microbes that have taken steps toward reconciling the molecular and the ecological views of trade-offs, despite the challenges in doing so in natural settings. Finally, we suggest avenues for research where mathematical modelling, experiments and studies of natural microbial communities provide a unique opportunity to integrate studies of diversity across multiple scales