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The human microbiome consists of five major regional biomes distributed in or on our five body sites including skin, oral, lung, gut, and reproductive tract. Its biogeography (the spatial and temporal distribution of its biodiversity) has far‐reaching implications to our health and diseases. Nevertheless, we currently have very limited understanding on the mechanisms shaping the biogeography, since it is often rather difficult to determine the relative importance of drift, dispersal, speciation, and selection, the four processes (mechanisms) determining the patterns of microbial biogeography and community dynamics according to a recent synthesis in community ecology and biogeography. To disentangle these mechanisms, I utilize multisite neutral (MSN) model and niche‐neutral hybrid (NNH) model to analyze large number of truly multisite microbiome samples covering all five major human microbiome habitats, including 699 metacommunities and 5,420 local communities. Approximately 89% of metacommunities and 92% local communities exhibit patterns indistinguishable from neutral, and 20% indistinguishable from niche‐neutral hybrid model, indicating the relative significance of stochastic neutral forces versus deterministic niche selection in shaping the biogeography of human microbiome. These findings cast supporting evidence to van der Gast's revision to classic Bass‐Becking doctrine of microbial biogeography: "Some things are everywhere and some things are not. Sometimes the environment selects and sometimes it doesn't," offering the first educated guess for "some" and "sometimes" in the revised doctrine. Furthermore, the logistic/Cox regression models describing the relationships among community neutrality, niche differentiation, and key community/species characteristics (including community diversity, community/species dominance, speciation, and migration rates) were constructed to quantitatively describe the niche‐neutral continuum and the influences of community/species properties on the continuum. [ABSTRACT FROM AUTHOR]

There are two major sequencing technologies for investigating the microbiome: the amplicon sequencing that generates the OTU (operational taxonomic unit) tables of marker genes (e.g., bacterial 16S-rRNA), and the metagenomic shotgun sequencing that generates metagenomic gene abundance (MGA) tables. The OTU table is the counterpart of species abundance tables in macrobial ecology of plants and animals, and has been the target of numerous ecological and network analyses in recent gold rush for microbiome research and in great efforts for establishing an inclusive theoretical ecology. Nevertheless, MGA analyses have been largely limited to bioinformatics pipelines and ad hoc statistical methods, and systematic approaches to MGAs guided by classic ecological theories are still few. Here, we argue that, the difference between "gene kinds" and "gene species" are nominal, and the metagenome that a microbiota carries is essentially a 'community' of metagenomic genes (MGs). Each row of a MGA table represents a metagenome of a microbiota, and the whole MGA table represents a 'meta-metagenome' (or an assemblage of metagenomes) of N microbiotas (microbiome samples). Consequently, the same ecological/network analyses used in OTU analyses should be equally applicable to MGA tables. Here we choose to analyze the heterogeneity of metagenome by introducing classic Taylor's power law (TPL) and its recent extensions in community ecology. Heterogeneity is a fundamental property of metagenome, particularly in the context of human microbiomes. Recent studies have shown that the heterogeneity of human metagenomes is far more significant than that of human genomes. Therefore, without deep understanding of the human metagenome heterogeneity, personalized medicine of the human microbiome-associated diseases is hardly feasible. The TPL extensions have been successfully applied to measure the heterogeneity of human microbiome based on amplicon-sequencing reads of marker genes (e.g., 16s-rRNA). In this article, we demonstrate the analysis of the metagenomic heterogeneity of human gut microbiome at whole metagenome scale (with type-I power law extension) and metagenomic gene scale (type-III), as well as the heterogeneity of gene clusters, respectively. We further examine the influences of obesity, IBD and diabetes on the heterogeneity, which is of important ramifications for the diagnosis and treatment of human microbiome-associated diseases. [ABSTRACT FROM AUTHOR]

Classic concepts of genetic (gene) diversity (heterozygosity) such as Nei & Li's nucleotide diversity were defined within a population context. Although variations are often measured in population context, the basic carriers of variation are individuals. Hence, measuring variations such as SNP of an individual against a reference genome, which has been ignored previously, is certainly in its own right. Indeed, similar practice has been a tradition in community ecology, where the basic unit of diversity measure is individual community sample. We propose to use Renyi's-entropy-based Hill numbers to define individual-level genetic diversity and similarity and demonstrate the definitions with the SNP (single nucleotide polymorphism) datasets from the 1000-Genomes Project. Hill numbers, derived from Renyi's entropy (of which Shannon's entropy is a special case), have found widely applications including measuring the quantum information entanglement and ecological diversity. The demonstrated individual-level SNP diversity not only complements the existing population-level genetic diversity concepts, but also offers building blocks for comparative genetic analysis at higher levels. The concept of individual covers, but is not limited to, individual chromosome, region of chromosome, gene cluster(s), or whole genome. Similarly, the SNP can be replaced by other structural variants or mutation types such as indels. [ABSTRACT FROM AUTHOR]

Microgenderome or sexual dimorphism in microbiome refers to the bidirectional interactions between microbiotas, sex hormones, and immune systems, and it is highly relevant to disease susceptibility. A critical step in exploring microgenderome is to dissect the sex differences in key community ecology properties, which has not been systematically analyzed. This study aims at filling the gap by reanalyzing the Human Microbiome Project datasets with two objectives: (i) dissecting the sex differences in community diversity and their intersubject scaling, species composition, core/periphery species, and high‐salience skeletons (species interactions); (ii) offering mechanistic interpretations for (i). Conceptually, the Vellend–Hanson synthesis of community ecology that stipulates selection, drift, speciation, and dispersal as the four processes driving community dynamics is followed. Methodologically, seven approaches reflecting the state‐of‐the‐art research in medical ecology of human microbiomes are harnessed to achieve the objectives. It is postulated that the revealed microgenderome characteristics (categorized as seven aspects of differences/similarities) exert far reaching influences on disease susceptibility, and are primarily due to the sex difference in selection effects (deterministic fitness differences in microbial species and/or species interactions with each other or with their hosts), which are, in turn, shaped/modulated by host physiology (immunity, hormones, gut–brain communications, etc.). [ABSTRACT FROM AUTHOR]

The diversity–stability relationship is a long‐standing, central focus of community ecology. Two major challenges have impeded studies of the diversity–stability relationship (DSR): the difficulty in obtaining high‐quality longitudinal data sets; and the lack of a general theoretical framework that can encompass the enormous complexity inherent in "diversity," "stability," and their many interactions. Metagenomic "Big Data" now provide high quality longitudinal data sets, and the human microbiome project (HMP) offers an unprecedented opportunity to reinvigorate investigations of DSRs. We introduce a new framework for exploring DSRs that has three parts: (1) a cross‐scale measure of dominance with a simple mathematical form that can be applied simultaneously to individual species and entire communities and can be used to construct species dominance networks (SDNs); (2) analysis of SDNs based on special trio motifs, core‐periphery, rich‐club, and nested structures, and high salience skeletons; and (3) a synthesis of coarse‐scale core/periphery/community‐level stability modeling with fine‐scale analysis of SDNs that further reveals the stability properties of the community structures. We apply this new approach to data from the human vaginal microbiome of the HMP, simultaneously illustrating its utility in developing and testing theories of diversity and stability while providing new insights into the underlying ecology and etiology of a human microbiome‐associated disease. [ABSTRACT FROM AUTHOR]