Dominance network analysis provides a new framework for studying the diversity–stability relationship

Critical complex network structures in animal gastrointestinal tract microbiomes

BACKGROUND

Living things from microbes to their hosts (plants, animals and humans) interact with each other, and their relationships may be described with complex network models. The present study focuses on the critical network structures, specifically the core/periphery nodes and backbones (paths of high-salience skeletons) in animal gastrointestinal microbiomes (AGMs) networks. The core/periphery network (CPN) mirrors nearly ubiquitous nestedness in ecological communities, particularly dividing the network as densely interconnected core-species and periphery-species that only sparsely linked to the core. Complementarily, the high-salience skeleton network (HSN) mirrors the pervasive asymmetrical species interactions (strictly microbial species correlations), particularly forming heterogenous pathways in AGM networks with both "backbones" and "rural roads" (regular or weak links). While the cores and backbones can act as critical functional structures, the periphery nodes and weak links may stabilize network functionalities through redundancy.

RESULTS

Here, we build and analyze 36 pairs of CPN/HSN for the AGMs based on 4903 gastrointestinal-microbiome samples containing 473,359 microbial species collected from 318 animal species covering all vertebrate and four major invertebrate classes. The network analyses were performed at host species, order, class, phylum, kingdom scales and diet types with selected and comparative taxon pairs. Besides diet types, the influence of host phylogeny, measured with phylogenetic (evolutionary) timeline or "age", were integrated into the analyses. For example, it was found that the evolutionary trends of three primary microbial phyla (Bacteroidetes/Firmicutes/Proteobacteria) and their pairwise abundance-ratios in animals do not mirror the patterns in modern humans phylogenetically, although they are consistent in terms of diet types.

CONCLUSIONS

Overall, the critical network structures of AGMs are qualitatively and structurally similar to those of the human gut microbiomes. Nevertheless, it appears that the critical composition (the three phyla of Bacteroidetes, Firmicutes, and Proteobacteria) in human gut microbiomes has broken the evolutionary trend from animals to humans, possibly attributable to the Anthropocene epoch and reflecting the far-reaching influences of agriculture and industrial revolution on the human gut microbiomes. The influences may have led to the deviations between modern humans and our hunter-gather ancestors and animals.

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BACKGROUND

Metagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists' (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies-amplicon sequencing and whole-genome (shotgun sequencing)-respectively generate two contrastingly different categories of metagenomic reads (data)-OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.

RESULTS

The ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan's near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.

CONCLUSIONS

All of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

A new hypothesis on BV etiology: dichotomous and crisscrossing categorization of complex versus simple on healthy versus BV vaginal microbiomes

IMPORTANCE

BV may influence as many as one-third of women, but its etiology remains unclear. A traditional view is that dominance by Lactobacillus is the hallmark of a healthy vaginal microbiome and lack of dominance may make women BV-prone. Recent studies show that the human VMs can be classified into five major types, four of which possess type-specific dominant species of Lactobacillus. The remaining one (type IV) is not dominated by Lactobacillus and contains a handful of strictly anaerobic bacteria. Nevertheless, exceptions to the first hypothesis have been noticed from the very beginning, and there is not a definite relationship, suggested yet, between the five VM types and BV status. Here, we propose and test a novel hypothesis that assumes the existence of four VM types from dichotomous crisscrossing of "complex versus simple (high diversity or low dominance versus low diversity or high dominance)" on "healthy versus BV." Consequently, there are simple BV versus complex BV.

The Upper Respiratory Tract Microbiome Network Impacted by SARS-CoV-2

The microbiome of upper respiratory tract (URT) acts as a gatekeeper to respiratory health of the host. However, little is still known about the impacts of SARS-CoV-2 infection on the microbial species composition and co-occurrence correlations of the URT microbiome, especially the relationships between SARS-CoV-2 and other microbes. Here, we characterized the URT microbiome based on RNA metagenomic-sequencing datasets from 1737 nasopharyngeal samples collected from COVID-19 patients. The URT-microbiome network consisting of bacteria, archaea, and RNA viruses was built and analyzed from aspects of core/periphery species, cluster composition, and balance between positive and negative interactions. It is discovered that the URT microbiome in the COVID-19 patients is enriched with Enterobacteriaceae, a gut associated family containing many pathogens. These pathogens formed a dense cooperative guild that seemed to suppress beneficial microbes collectively. Besides bacteria and archaea, 72 eukaryotic RNA viruses were identified in the URT microbiome of COVID-19 patients. Only five of these viruses were present in more than 10% of all samples, including SARS-CoV-2 and a bat coronavirus (i.e., BatCoV BM48-31) not detected in humans by routine means. SARS-CoV-2 was inhibited by a cooperative alliance of 89 species, but seems to cooperate with BatCoV BM48-31 given their statistically significant, positive correlations. The presence of cooperative bat-coronavirus partner of SARS-CoV-2 (BatCoV BM48-31), which was previously discovered in bat but not in humans to the best of our knowledge, is puzzling and deserves further investigation given their obvious implications. Possible microbial translocation mechanism from gut to URT also deserves future studies.

Niche-Neutral Continuum Seems to Explain the Global Niche Differentiation and Local Drift of the Human Digestive Tract Microbiome

The human digestive tract (DT) is differentiated into diverse niches and harbors the greatest microbiome diversity of our bodies. Segata et al. (2012) found that the microbiome of diverse habitats along the DT may be classified as four categories or niches with different microbial compositions and metabolic potentials. Nonetheless, few studies have offered theoretical interpretations of the observed patterns, not to mention quantitative mechanistic parameters. Such parameters should capture the essence of the fundamental processes that shape the microbiome distribution, beyond simple ecological metrics such as diversity or composition descriptors, which only capture the manifestations of the mechanisms. Here, we aim to get educated guesses for such parameters by adopting an integrated approach with multisite neutral (MSN) and niche-neutral hybrid (NNH) modeling, via reanalyzing Segata’s 16s-rRNA samples covering 10 DT-sites from over 200 healthy individuals. We evaluate the relative importance of the four essential processes (drift, dispersal, speciation, and selection) in shaping the microbiome distribution and dynamics along DT, which are assumed to form a niche-neutral continuum. Furthermore, the continuum seems to be hierarchical: the selection or niche differentiations seem to play a predominant role (> 90% based on NNH) at the global (the DT metacommunity) level, but the neutral drifts seem to be prevalent (> 90% based on MSN/NNH) at the local sites except for the gut site. An additional finding is that the DT appears to have a fifth niche for the DT microbiome, namely, Keratinized gingival (KG), while in Segata’s original study, only four niches were identified. Specifically, in Segata’s study, KG was classified into the same niche type including buccal mucosa (BM), hard palate (HP), and KG. However, it should be emphasized that the proposal of the fifth niche of KG requires additional verification in the future studies.

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

Evaluating the Assembly Dynamics in the Human Vaginal Microbiomes With Niche-Neutral Hybrid Modeling

Using 2,733 longitudinal vaginal microbiome samples (representing local microbial communities) from 79 individuals (representing meta-communities) in the states of healthy, BV (bacterial vaginosis) and pregnancy, we assess and interpret the relative importance of stochastic forces (e.g., stochastic drifts in bacteria demography, and stochastic dispersal) vs. deterministic selection (e.g., host genome, and host physiology) in shaping the dynamics of human vaginal microbiome (HVM) diversity by an integrated analysis with multi-site neutral (MSN) and niche-neutral hybrid (NNH) modeling. It was found that, when the traditional “default” P-value = 0.05 was specified, the neutral drifts were predominant (≥50% metacommunities indistinguishable from the MSN prediction), while the niche differentiations were moderate (<20% from the NNH prediction). The study also analyzed two challenging uncertainties in testing the neutral and/or niche-neutral hybrid models, i.e., lack of full model specificity – non-unique fittings of same datasets to multiple models with potentially different mechanistic assumptions – and lack of definite rules for setting the P-value thresholds (also noted as P_t-value when referring to the threshold of P-value in this article) in testing null hypothesis (model). Indeed, the two uncertainties can be interdependent, which further complicates the statistical inferences. To deal with the uncertainties, the MSN/NNH test results under a series of P-values ranged from 0.05 to 0.95 were presented. Furthermore, the influence of P-value threshold-setting on the model specificity, and the effects of woman’s health status on the neutrality level of HVM were examined. It was found that with the increase of P-value threshold from 0.05 to 0.95, the overlap (non-unique) fitting of MSN and NNH decreased from 29.1 to 1.3%, whereas the specificity (uniquely fitted to data) of MSN model was kept between 55.7 and 82.3%. Also with the rising P-value threshold, the difference between healthy and BV groups become significant. These findings suggested that traditional single P-value threshold (such as the de facto standard P-value = 0.05) might be insufficient for testing the neutral and/or niche neutral hybrid models.

Cross-Scale Analyses of Animal and Human Gut Microbiome Assemblies from Metacommunity to Global Landscape

Animal (human) gut microbiomes have been coevolving with their hosts for many millions of years. Understanding how the coevolution shapes the processes of microbiome assembly and diversity maintenance is important but rather challenging. An effort may start with the understanding of how and why animals and humans may differ in their microbiome neutrality (stochasticity) levels. Here, we attempted to perform layered comparative stochasticity analyses across animal species (including humans), class, and kingdom scales, corresponding to microbial metacommunity, landscape, and global-landscape scales. By analyzing 4,903 microbiome samples from 274 animal species covering 4 major invertebrate classes and all 6 vertebrate classes and including 1,787 human gut microbiome samples, we discovered the following: (i) at the microbial metacommunity (animal species) scale, although the general trend of stochasticity (measured in the relationships between fundamental biodiversity/dispersal numbers of Hubbell’s neutral theory and host species phylogenetic timeline) seems continuous, there seems to be a turning point from animals to humans in the passing rate of neutrality tests (12% to 45% versus 100%). We postulate that it should be the human experiences from agricultural/industrial activities (e.g., diet effects) and frequent social/familial contacts that are responsible for the dramatically rising stochastic neutrality in human gut microbiomes. (ii) At the microbial landscape (animal class) and global landscape (animal kingdom) scales, neutrality is not detectable, suggesting that the landscape is niche differentiated—animal species may possess “home niches” for their coadapted microbiomes. We further analyze the reliabilities of our findings by using variable P value thresholds (type I error) and performing power analysis (type II error) of neutrality tests.

IMPORTANCE Understanding how the coevolution (evolutionary time scale) and/or the interactions (ecological time scale) between animal (human) gut microbiomes and their hosts shape the processes of the microbiome assembly and diversity maintenance is important but rather challenging. An effort may start with the understanding of how and why animals and humans may differ in their microbiome neutrality (stochasticity) levels. Here, we attempted to perform layered comparative stochasticity analyses across animal species (including humans), class, and kingdom scales, corresponding to microbial metacommunity, landscape, and global-landscape scales by analyzing 4,903 microbiome samples from 274 animal species covering 4 major invertebrate classes and all 6 vertebrate classes, and including 1,787 human gut microbiome samples. The analyses were implemented by fitting the multisite neutral model and further augmented by checking false-positive and false-negative errors, respectively. It appears that there is a turning (tipping) point in the neutrality level from animal to human microbiomes.

In silico trio-biomarkers for bacterial vaginosis revealed by species dominance network analysis

BV (bacterial vaginosis) influences 20%–40% of women but its etiology is still poorly understood. An open question about the BV is which of the hundreds of bacteria found in the human vaginal microbiome (HVM) are the major force driving the vaginal microbiota dysbiosis. Here, we recast the question of microbial causality of BV by asking if there are any prevalent ‘signatures’ (network motifs) in the vaginal microbiome networks associated with it? We apply a new framework [species dominance network analysis by Ma & Ellison (2019): Ecological Monographs) to detect critical structures in HVM networks associated with BV risks and etiology. We reanalyzed the 16 s-rRNA gene sequencing datasets of a mixed-cohort of 25 BV patients and healthy women. In these datasets, we detected 15 trio-motifs that occurred exclusively in BV patients. We failed to find any of these 15 trio-motifs in three additional cohorts of 1535 healthy women. Most member-species of the 15 trio motifs are BV-associated anaerobic bacteria (BVAB), Ravel’s community-state type indicators, or the most dominant species; virtually all species interactions in these trios are high-salience skeletons, suggesting that those trios are strongly connected ‘cults’ associated with the occurrence of BV. The presence of the trio motifs unique to BV may act as indicators for its personalized diagnosis and could help elucidate a more mechanistic interpretation of its risks and etiology. We caution that scarcity of large longitudinal datasets of HVM also limited further verifications of our findings, and these findings require further clinical tests to launch their applications.

Ecological and network analyses identify four microbial species with potential significance for the diagnosis/treatment of ulcerative colitis (UC)

BACKGROUND

Ulcerative colitis (UC) is one of the primary types of inflammatory bowel disease (IBD), the occurrence of which has been increasing worldwide. Although IBD is an intensively studied human microbiome-associated disease, research on Chinese populations remains relatively limited, particularly on the mucosal microbiome. The present study aimed to analyze the changes in the mucosal microbiome associated with UC from the perspectives of medical ecology and complex network analysis.

RESULTS

In total, 56 mucosal microbiome samples were collected from 28 Chinese UC patients and their healthy family partners, followed by amplicon sequencing. Based on sequencing data, we analyzed species diversity, shared species, and inter-species interactions at the whole community, main phyla, and core/periphery species levels. We identified four opportunistic "pathogens" (i.e., Clostridium tertium, Odoribacter splanchnicus, Ruminococcus gnavus, and Flavonifractor plautii) with potential significance for the diagnosis and treatment of UC, which were inhibited in healthy individuals, but unrestricted in the UC patients. In addition, we also discovered in this study: (i) The positive-to-negative links (P/N) ratio, which measures the balance of species interactions or inhibition effects in microbiome networks, was significantly higher in UC patients, indicating loss of inhibition against potentially opportunistic "pathogens" associated with dysbiosis. (ii) Previous studies have reported conflicting evidence regarding species diversity and composition between UC patients and healthy controls. Here, significant differences were found at the major phylum and core/periphery scales, but not at the whole community level. Thus, we argue that the paradoxical results found in existing studies are due to the scale effect.

CONCLUSIONS

Our results reveal changes in the ecology and network structure of the gut mucosal microbiome that might be associated with UC, and these changes might provide potential therapeutic mechanisms of UC. The four opportunistic pathogens that were identified in the present study deserve further investigation in future studies.

Spatial heterogeneity analysis of the human virome with Taylor's power law

Spatial heterogeneity is a fundamental characteristic of organisms from viruses to humans. Measuring heterogeneity is challenging, especially for naked-eye invisible viruses, but of obvious importance. For example, spatial heterogeneity of virus distribution may strongly influence infection spreading and outbreaks in the case of pathogenic viruses; the spatial distribution (i.e., the inter-subject heterogeneity) of commensal viruses within/on our bodies can influence the competition, coexistence, and dispersal of viruses within or between our bodies. Taylor's power law (TPL) was first discovered in the 1960s to describe the spatial distributions of plant and/or animal populations, and since then it has been verified by numerous experimental and theoretical studies. Recently, TPL has been extended from population to community level and applied to bacterial communities. Here we report the first comprehensive testing of the TPL fitted to human virome datasets. It was found that the human virome follows the TPL as bacterial communities do. Furthermore, the TPL heterogeneity scaling parameter of human virome is virtually the same as that of the human bacterial microbiome (1.916 vs. 1.926). We postulate that the extreme closeness of human viruses and bacteria in heterogeneity scaling coefficients could be attributed to the fact that most of the viruses that were annotated in this study actually belong to bacteriophages (86% viral OTUs) that "piggyback" on their bacterial hosts, and their distributions are likely host-dependent. The scaling parameter, which measures the inter-subject heterogeneity changes, should be an innate property of human microbiomes including both bacteria and viruses. It is similar to the acceleration coefficient of the gravity (g = 9.8) as specified by Newton's law, which is invariant on the earth. Nevertheless, we caution that our postulation is contingent on an implicit assumption that the proportion of bacteriophages to total virome may not change significantly when more virus species can be identified in future.

A theoretic approach to the mode of gut microbiome translocation in SIV-infected Asian macaques

Human gut microbiome could translocate to other tissues, and the relocation triggered by HIV/SIV infection has received increasing attention. However, the underlying mode of this translocation, whether it is deterministic or random (passive) process, is not clear, not to mention quantitative estimation of the relocation probability and rates. Using multi-tissue microbiome datasets collected from SIV-infected macaques, originally reported by Klase et al. (2015), we apply Hubbell's unified neutral theory of biodiversity (UNTB) implemented by Harris et al. (2017) in the form of multi-site neutral (MSN) model to explore the translocation mode and rates of the gut microbiome. We found that (i) The translocation from gastrointestinal tract to tissues was driven by stochastic (neutral) forces as revealed by 100% neutrality-passing rates with MSN testing; (ii) The translocation probability from gastrointestinal tract to tissues is significantly larger than the baseline dispersal rates occurring within gastrointestinal tract (0.234 vs. 0.006 at the phylum level, P< 0.001). (iii) Approximately, 23% of phyla and 55% of genera were migrated from gastrointestinal tract to the tissues (liver and mesenteric lymph nodes). Our findings offer the first interpretation of the microbial translocation mode from gastrointestinal tract to tissues, and the first estimates of the translocation probability and level.

Niche-neutral theoretic approach to mechanisms underlying the biodiversity and biogeography of human microbiomes

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.

Heterogeneity-disease relationship in the human microbiome-associated diseases

Space is a critical and also challenging frontier in human microbiome research. It has been found that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the spatial heterogeneity of human microbiome. Assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, but implicit approaches such as Taylor's power law (TPL) can be rather effective. Based on TPL, which achieved a rare status of ecological laws, we introduce a general methodology for characterizing the spatial heterogeneity of microbiome (i.e. characterization of microbial spatial distribution) and further apply it for investigating the heterogeneity-disease relationship (HDR) via analyzing a big dataset of 26 MAD (microbiome-associated disease) studies covering nearly all high-profile MADs including obesity, diabetes and gout. It was found that in majority of the MAD cases, the microbiome was sufficiently resilient to endure the disease disturbances. Specifically, in ∼10-16% cases, disease effects were significant-the healthy and diseased cohorts exhibited statistically significant differences in the TPL heterogeneity parameters. We further compared HDR with classic diversity-disease relationship (DDR) and explained their mechanistic differences. Both HDR and DDR cross-verified remarkable resilience of the human microbiomes against MADs.

Dominance network analysis of the healthy human vaginal microbiome not dominated by Lactobacillus species

Although Lactobacillus dominance is one of the commonest characteristics of many healthy vaginal microbiomes, a significant proportion of healthy women lack an appreciable amount of Lactobacillus in their microbiome. Indeed, the vaginal microbiomes of many BV (bacterial vaginosis) patients lack the dominance by Lactobacillus. One would wonder what are special with those healthy non-Lactobacillus dominated vaginal microbiomes (nLDVM)? Here we re-analyzed the vaginal microbiome datasets of 1107 postpartum women in rural Malawi Doyle et al. (2018) using species dominance network (SDN) analysis. We discovered that: (i) The DN of the nLDVM is predominantly mutualistic, where most competitive (negative) relationships were from bacterial vaginosis-associated bacteria (BVAB), >60% occurred between BVAB and non-BVAB genera. Gardnerella was inhibited by a mutualistic combination of 23 genera, and Lactobacillus by 15 genera. These may be possible mechanisms by which the microbiome maintains high diversity but avoids dominance by Gardnerella or Lactobacillus. Gardnerella and Lactobacillus were only cooperated with a few genera, but they were positively connected with each other. The suppressed Lactobacillus species positively associated with Gardnerella was Lactobacillus iners, indicating that L. iners might act as an “enemy” in the Lactobacillus-poor vaginal microbiome, and inhibition of Gardnerella and L. iners might be a self-protective mechanism to maintain stability and health of this microbiome. (ii) We identified skeletons of the DNs and separate pathways consisting of high salience skeletons. Finegoldia species and Staphylococcus epidermidis were the hubs of the skeleton network. The roles that they play in the nLDVM deserve more attention of future studies.

Critical Network Structures and Medical Ecology Mechanisms Underlying Human Microbiome-Associated Diseases

A fundamental problem in studies on human microbiome-associated diseases (MADs) is to understand the relationships between microbiome structures and health status of hosts. For example, species diversity metrics have been routinely evaluated in virtually all studies on MADs, yet a recent meta-analysis revealed that, in only approximately one-third of the cases, diversity and diseases were related. In this study, we ask whether Hubbell's neutral theory (supplemented with the normalized stochasticity ratio [NSR]) or critical microbiome network structures may offer better alternatives. Whereas neutral theory and NSR focus on stochastic processes, we use core/periphery and high-salience skeleton networks to evaluate deterministic, asymmetrical niche effects, assuming that all species or their interactions were not “born” equal and focusing on non-neutral, critical network structures. We found that properties of critical network structures are more indicative of disease effects. Finally, seven findings (mechanisms, interpretations, and postulations) regarding medical ecology mechanisms underlying MADs were summarized.

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Seven findings (mechanisms/interpretations/postulations) of medical ecology proposed

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Critical network structures more indicative of disease effects than ecology metrics

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One-third seems ceiling of diversity-disease relations, half to two-thirds of network structures

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Super resilience (unexplained one-third to half gap) is likely attributed to host genome

Assessing and Interpreting the Metagenome Heterogeneity With Power Law

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.

Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases

The AKP (Anna Karenina principle), which refers to observations inspired by the opening line of Leo Tolstoy's Anna Karenina, “all happy families are all alike; each unhappy family is unhappy in its own way,” predicts that all “healthy” microbiomes are alike and each disease-associated microbiome is “sick” in its own way in human microbiome-associated diseases (MADs). The AKP hypothesis predicts the rise of heterogeneity/stochasticity in human microbiomes associated with dysbiosis due to MADs. We used the beta-diversity in Hill numbers and stochasticity analysis to detect AKP and anti-AKP effects. We tested the AKP with 27 human MAD studies and discovered that the AKP, anti-AKP, and non-AKP effects were exhibited in approximately 50%, 25%, and 25% of the MAD cases, respectively. Mechanistically, AKP effects are primarily influenced by highly dominant microbial species and less influenced by rare species. In contrast, all species appear to play equal roles in influencing anti-AKP effects.

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About a half of microbiome-associated diseases follow AKP (Anna Karenina principle)

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AKP effects are primarily influenced by highly dominant microbial species

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About one-fourth of microbiome-associated diseases follow the anti-AKP

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All species appear to play equal roles in influencing the anti-AKP effects

How and Why Men and Women Differ in Their Microbiomes: Medical Ecology and Network Analyses of the Microgenderome

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.).

Comparative power law analysis for the spatial heterogeneity scaling of the hot-spring microbiomes

Spatial heterogeneity is a fundamental property of any natural ecosystems, including hot spring and human microbiomes. Two important scales that spatial heterogeneity exhibits are population and community scales, and Taylor's power law (PL) and its extensions (PLEs) offer ideal quantitative models to assess population- and community-level heterogeneities. Here we analyse 165 hot spring microbiome samples at the global scale that cover a wide range of temperatures (7.5-99°C) and pH levels (3.3-9). We explore a question of fundamental importance for measuring the spatial heterogeneity of the hot-spring microbiome and further discuss their ecological implications: How do critical environmental factors such as temperature and pH influence the scaling of community spatial heterogeneity? We are particularly interested in the existence of a universal scaling model that is independent of environmental gradients. By applying PL and PLEs, we were able to obtain such scaling parameters of the hot spring at both community and population levels, which are temperature- and pH-invariant. These findings suggest that while the hot-spring microbiomes located at different regions may have different environmental conditions, they share a fundamental heterogeneity scaling parameter, analogically similar to the gravitational acceleration on Earth, which may vary slightly depending on altitude and latitude, but is invariant overall. In contrast, similar to the physics of the Moon and Earth, which have different gravitational accelerations, the hot spring and human microbiomes can have different scaling parameters as demonstrated in this study.

Foundation Species, Non-trophic Interactions, and the Value of Being Common

Foundation species define ecosystems, control the biological diversity of associated species, modulate critical ecosystem processes, and often have important cultural values and resonance. This review summarizes current understanding of the characteristics and traits of foundation species and how to distinguish them from other “important” species in ecological systems (e.g., keystone, dominant, and core species); illustrates how analysis of the structure and function of ecological networks can be improved and enriched by explicit incorporation of foundation species and their non-trophic interactions; discusses the importance of pro-active identification and management of foundation species as a cost-effective and efficient method of sustaining valuable ecosystem processes and services and securing populations of associated rare, threatened, or endangered species; and suggests broader engagement of citizen-scientists and non-specialists in the identification and study of foundation species and their biological and cultural values.