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 comprehensive diversity analysis on the gut microbiomes of ASD patients: from alpha, beta to gamma diversities.

Autism spectrum disorder (ASD) is estimated to influence as many as 1% children worldwide, but its etiology is still unclear. It has been suggested that gut microbiomes play an important role in regulating abnormal behaviors associated with ASD. A de facto standard analysis on the microbiome-associated diseases has been diversity analysis, and nevertheless, existing studies on ASD-microbiome relationship have not produced a consensus. Here, we perform a comprehensive analysis of the diversity changes associated with ASD involving alpha-, beta-, and gamma-diversity metrics, based on 8 published data sets consisting of 898 ASD samples and 467 healthy controls (HC) from 16S-rRNA sequencing. Our findings include: (i) In terms of alpha-diversity, in approximately 1/3 of the studies cases, ASD patients exhibited significantly higher alpha-diversity than the HC, which seems to be consistent with the "1/3 conjecture" of diversity-disease relationship (DDR). (ii) In terms of beta-diversity, the AKP (Anna Karenina principle) that predict all healthy microbiomes should be similar, and every diseased microbiome should be dissimilar in its own way seems to be true in approximately 1/2 to 3/4 studies cases. (iii) In terms of gamma-diversity, the DAR (diversity-area relationship) modeling suggests that ASD patients seem to have large diversity-area scaling parameter than the HC, which is consistent with the AKP results. However, the MAD (maximum accrual diversity) and RIP (ratio of individual to population diversity) parameters did not suggest significant differences between ASD patients and HC. Throughout the study, we adopted Hill numbers to measure diversity, which stratified the diversity measures in terms of the rarity-commonness-dominance spectrum. It appears that the differences between ASD patients and HC are more propounding on rare-species side than on dominant-species side. Finally, we discuss the apparent inconsistent diversity-ASD relationships among different case studies and postulate that the relationships are not monotonic.

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.

CDC (Cindy and David's Conversations) game: Advising President to survive pandemic.

Ongoing debates on anti-COVID19 policies have been focused on coexistence-with versus zero-out (virus) strategies, which can be simplified as "always open (AO)" versus "always closed (AC)." We postulate that a middle ground, dubbed LOHC (low-risk-open and high-risk-closed), is likely favorable, precluding obviously irrational HOLC (high-risk-open and low-risk-closed). From a meta-strategy perspective, these four policies cover the full spectrum of anti-pandemic policies. By emulating the reality of anti-pandemic policies today, the study aims to identify possible cognitive gaps and traps by harnessing the power of evolutionary game-theoretic analysis and simulations, which suggest that (1) AO and AC seem to be "high-probability" events (0.412-0.533); (2) counter-intuitively, the middle ground-LOHC-seems to be small-probability event (0.053), possibly mirroring its wide adoptions but broad failures. Besides devising specific policies, an equally important challenge seems to deal with often hardly avoidable policy transitions along the process from emergence, epidemic, through pandemic, to endemic state.

Virome comparison (VC): A novel approach to comparing viromes based on virus species specificity and virome specificity diversity.

The human virome, or the viral communities distributed on or in our body, is estimated to contain about 380 trillion of viruses (individuals), which has far reaching influences on our health and diseases. Obviously, the sheer numbers of viruses alone make the comparisons of two or multiple viromes extremely challenging. In fact, the theory of computation in computer science for so-termed NP-hard problems stipulates that the problem is unsolvable when the size of virome is sufficiently large even with fastest supercomputers. Practically, one has to develop heuristic and approximate algorithms to obtain practically satisfactory solutions for NP-hard problems. Here, we extend the species-specificity and specificity-diversity framework to develop a method for virome comparison (VC). The VC method consists of a pair of metrics: virus species specificity (VS) and virome specificity diversity (VSD) and corresponding pair of random search algorithms. Specifically, the VS and VS permutation (VSP) test can detect unique virus species (US) or enriched virus species (ES) in each virome (treatment), and the VSD and VSD permutation (VSDP) test can further determine holistic differences between two viromes or their subsets (assemblages of viruses). The test with four virome data sets demonstrated that the VC method is effective, efficient, and robust.

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.

Further Quantifying the Niche-Neutral Continuum of Human Digestive Tract Microbiomes with Near Neutral Model and Stochasticity Analysis.

It is postulated that the human digestive tract (DT) from mouth to intestine is differentiated into diverse niches. For example, Segata et al. discovered that the microbiomes of diverse habitats along the DT could be distinguished as 4 types (niches) including (i) stool; (ii) sub-gingival plaques (SubP) and supra-gingival plaques (SupP); (iii) tongue dorsum (TD), throat (TH), palatine tonsils (PT), and saliva (Sal); and (iv) hard palate (HP) and buccal mucosa (BM), and keratinized gingiva (KG). These niches are different not only in composition, but also in metabolic potentials. In a previous study, we applied Harris et al's multi-site neutral and Tang and Zhou's niche-neutral hybrid models to characterize the DT niches discovered by Segata et al. Here, we complement the previous study by applying Sloan's near-neural model and Ning et al's stochasticity analysis framework to quantify the niche-neutral continuum of the DT microbiome distribution to shed light on the possible ecological/evolutionary mechanism that shapes the continuum. Overall but excluding the stool site, the proportion of neutral OTUs (46%) is slightly higher than that of the positive selection (38%), but significantly higher than negative selection (15%). The gut (stool) exhibited 3 to 12 times lower neutrality than other DT sites. The analysis also cross-verified our previous hypothesis that the KG (keratinized gingiva) is of distinct assembly dynamics in the DT microbiome, should be treated as a fifth niche. Our findings offer new insight on the long-standing debate concerning whether a minimum of 2-mm of KG width is necessary for marginal periodontal health.

Chromosome-level genome assembly of the Muscovy duck provides insight into fatty liver susceptibility.

The Muscovy duck (Cairina moschata) is an economically important poultry species, which is susceptible to fatty liver. Thus, the Muscovy duck may serve as an excellent candidate animal model of non-alcoholic fatty liver disease. However, the mechanisms underlying fatty liver development in this species are poorly understood. In this study, we report a chromosome-level genome assembly of the Muscovy duck, with a contig N50 of 11.8 Mb and scaffold N50 of 83.16 Mb. The susceptibility of Muscovy duck to fatty liver was mainly attributed to weak lipid catabolism capabilities (fatty acid ß-oxidation and lipolysis). Furthermore, conserved noncoding elements (CNEs) showing accelerated evolution contributed to fatty liver formation by down-regulating the expression of genes involved in hepatic lipid catabolism. We propose that the susceptibility of Muscovy duck to fatty liver is an evolutionary by-product. In conclusion, this study revealed the potential mechanisms underlying the susceptibility of Muscovy duck to fatty liver.

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.

Shared Species Analysis, Augmented by Stochasticity Analysis, Is More Effective Than Diversity Analysis in Detecting Variations in the Gut Microbiomes.

Diversity analysis is a de facto standard procedure for most existing microbiome studies. Nevertheless, diversity metrics can be insensitive to changes in community composition (identities). For example, if species A (e.g., a beneficial microbe) is replaced by equal number of species B (e.g., an opportunistic pathogen), the diversity metric may not change, but the community composition has changed. The shared species analysis (SSA) is a computational technique that can discern changes of community composition by detecting the increase/decrease of shared species between two sets of microbiome samples, and it should be more sensitive than standard diversity analysis in discerning changes in microbiome structures. Here, we investigated the effects of ethnicity and lifestyles in China on the structure of Chinese gut microbiomes by reanalyzing the datasets of a large Chinese cohort with 300+ individuals covering 7 biggest Chinese ethnic groups (>95% Chinese population). We found: (i) Regarding lifestyles, SSA revealed significant differences between 100% of pair-wise comparisons in community compositions across all but phylum taxon levels (phylum level = 29%), but diversity analysis only revealed 14-29% pair-wise differences in community diversity across all four taxon levels. (ii) Regarding ethnicities, SSA revealed 100% pair-wise differences in community compositions across all but phylum (phylum level = 48-62%) levels, but diversity analysis only revealed 5-57% differences in community diversity across all four taxon levels. (iii) Ethnicity seems to have more prevalent effects on community structures than lifestyle does (iv) Community structures of the gut microbiomes are more stable at the phylum level than at the other three levels. (v) SSA is more powerful than diversity analysis in detecting the changes of community structures; furthermore, SSA can produce lists of unique and shared OTUs. (vi) Finally, we performed stochasticity analysis to mechanistically interpret the observed differences revealed by the SSA and diversity analyses.

Influences of Helicobacter pylori infection on diversity, heterogeneity, and composition of human gastric microbiomes across stages of gastric cancer development.

BACKGROUND: About a half of the world's population is infected with Helicobacter pylori (H. pylori), but only 1%-3% of them develop gastric cancer. As a primary risk factor for gastric cancer, the relationship between H. pylori infection and gastric microbiome has been a focus in recent years. MATERIALS AND METHODS: We reanalyze 11 human gastric microbiome datasets with or without H. pylori, covering the healthy control (HC) and four disease stages (chronic gastritis (CG), atrophic gastritis (AG), intestinal metaplasia (IM), and gastric cancer (GC)) of gastric cancer development to quantitatively compare the influences of the H. pylori infection and disease stages on the diversity, heterogeneity, and composition of gastric microbiome. Four medical ecology approaches including (i) diversity analysis with Hill numbers, (ii) heterogeneity analysis with Taylor's power law extensions (TPLE), (iii) diversity scaling analysis with diversity-area relationship (DAR) model, and (iv) shared species analysis were applied to fulfill the data reanalysis. RESULTS: (i) The influences of H. pylori infection on the species diversity, spatial heterogeneity, and potential diversity of gastric microbiome seem to be more prevalent than the influences of disease stages during gastric cancer development. (ii) The influences of H. pyloriinfection on diversity, heterogeneity, and composition of gastric microbiomes in HC, CG, IM, and GC stages appear more prevalent than those in AG stage. CONCLUSION: Our study confirmed the impact of H. pylori infection on human gastric microbiomes: The influences of H. pylori infection on the diversity, heterogeneity, and composition of gastric microbiomes appear to be disease-stage dependent.

Stochastic neutral drifts seem prevalent in driving human virome assembly: Neutral, near-neutral and non-neutral theoretic analyses.

It is estimated that human body is inhabited by approximately 380 trillions of viruses, which exist in the form of viral communities and are collectively termed as human virome. How virome is assembled and what kind of forces maintain the composition and diversity of viral communities is still an open question. The question is of obvious importance because of its implications to human health and diseases. Here we address the question by harnessing the power of Hubbell's unified neutral theory of biodiversity (UNTB) in terms of three neutral models including standard Hubbell's neutral model (HNM), Sloan's near-neutral model (SNM) and Harris et al. (2017) multi-site neutral model (MSN), further augmented by Ning et al. (2019) normalized stochasticity ratio (NSR) and Hammal et al. (2015) power analysis for the neutral test (PNT). With the five models applied to 179 virome samples, we aim to obtain robust findings given both Type-I and Type-II errors are addressed and possible alternative, non-neutral processes are detected. It was found that stochastic neutral drifts seem prevalent: approximately 65-92% at metacommunity/landscape scales and 67-80% at virus species scale. The non-neutral selection is approximately 26-28% at community scale and 23% at species scale. The false negative rate is about 2-3%, which suggested rather limited confounding effects of non-neutral process on neutrality tests. We postulate that prevalence of neutrality in human virome is likely due to extremely simple structure of viruses (stands of DNA/RNA) and their inter-species homogeneities, forming the foundation of species equivalence-the hallmark of neutral theory.

Microbiome-host-phylogeny relationships in animal gastrointestinal tract microbiomes.

Among the factors influencing the animal gastrointestinal tract microbiome (AGM) diversity, diet and phylogeny have been extensively studied. However, what made the studies particularly challenging is that diet characteristics per se are product of evolution, and hence totally disentangling both effects is unrealistic, likely explaining the lack of consensus in existing literatures. To further explore microbial diversity and host-phylogeny-diet relationships, we performed a big-data meta-analysis with 4903 16S rRNA AGM samples from 318 animal species covering all six vertebrate and four major invertebrate classes. We discovered that the relationship between AGM-diversity and phylogenetic timeline (PT) follows a power-law or log-linear model, including diet specific power-law relationships. The log-linear nature predicts a generally rising trend of AGM diversity along the evolutionary tree starting from the root, which explains the observation why Mammalia exhibited the highest AGM-diversity. The power-law property suggests that a handful of taxa carry disproportionally large weights to the evolution of diversity patterns than the majority of taxa, which explains why the species richness of Insecta was significant different than those from the other nine classes. Finally, we hypothesize that the diversity-PT power-law relationship explains why species-abundance distributions generally follow highly skewed probability distributions.

Diversity Scaling Analysis of Chinese Gut Microbiomes Across Ethnicities and Lifestyles.

Diversity scaling (changes) of human gut microbiome is important because it measures the inter-individual heterogeneity of diversity and other important parameters of population-level diversity. Understanding the heterogeneity of microbial diversity can be used as a reference for the personalized medicine of microbiome-associated diseases. Similar to diversity per se, diversity scaling may also be influenced by host factors, especially lifestyles and ethnicities. Nevertheless, this important topic regarding Chinese populations has not been addressed, to our best knowledge. Here, we fill the gap by applying a recent extension to the classic species-area relationship (SAR), i.e., diversity-area relationship (DAR), to reanalyze a large dataset of Chinese gut microbiomes covering the seven biggest Chinese ethnic groups (covering > 95% Chinese) living rural and urban lifestyles. Four DAR profiles were constructed to investigate the diversity scaling, diversity overlap, potential maximal diversity, and the ratio of local to global diversity of Chinese gut microbiomes. We discovered the following: (i) The diversity scaling parameters (z) at various taxon levels are little affected by either ethnicity or lifestyles, as exhibited by less than 0.5% differences in pairwise comparisons. (ii) The maximal accrual diversity (potential diversity) exhibited difference in only about 5% of pairwise comparisons, and all of the differences occurred in ethnicity comparisons (i.e., lifestyles had no effects). (iii) Ethnicity seems to have stronger effects than lifestyles across all taxon levels, and this may reflect the reality that China has been experiencing rapid urbanization in the last few decades, while the ethnic-related genetic background may change relatively little during the same period.

Diversity Scaling of Human Digestive Tract (DT) Microbiomes: The Intra-DT and Inter-individual Patterns.

The human gut microbiome has been extensively studied, but its diversity scaling (changes or heterogeneities) along the digestive tract (DT) as well as their inter-individual heterogeneities have not been adequately addressed to the best of our knowledge. Here we fill the gap by applying the diversity-area relationship (DAR), a recent extension to the classic species-area relationship (SAR) in biogeography, by reanalyzing a dataset of over 2000 16s-rRNA microbiome samples obtained from 10 DT sites of over 200 individuals. We sketched out the biogeography "maps" for each of the 10 DT sites by cross-individual DAR analysis, and the intra-DT distribution pattern by cross-DT-site DAR analysis. Regarding the inter-individual biogeography, it was found that all DT sites have the invariant (constant) scaling parameter-all sites possessing the same diversity change rate across individuals, but most sites have different potential diversities, which include the portions of diversity that may be absent locally but present regionally. In the case of this study, the potential diversity of each DT site covers the total diversity of the respective site from all individuals in the cohort. In terms of the genus richness, an average individual hosts approximately 20% of the population-level genus richness (total bacterial genus of a human population). In contrast, in terms of community biodiversity, the percentages of individual over population may exceed 90%. This suggests that the differences between individuals in their DT microbiomes are predominantly in the composition of bacterial species, rather than how their abundances are distributed (i.e., biodiversity). Regarding the intra-DT patterns, the scaling parameter (z) is larger-suggesting that the intra-DT biodiversity changes are larger than inter-individual changes. The higher intra-DT heterogeneity of bacteria diversity, as suggested by larger intra-DT z than the inter-individual heterogeneity, should be expected since the intra-DT heterogeneity reflects the functional differentiations of the DT tract, while the inter-individual heterogeneity (z) reflects the difference of the same DT site across individuals. On average, each DT site contains 21-36% of the genus diversity of the whole DT, and the percentages are even higher in terms of higher taxon levels.

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.

Integrated diversity and shared species analyses of human viromes.

Diversity analysis has been performed routinely on microbiomes, including human viromes. Shared species analysis has been conducted only rarely, but it can be a powerful supplement to diversity analysis. In the present study, we conducted integrated diversity and shared species analyses of human viromes by reanalyzing three published datasets of human viromes with more than 250 samples from healthy vs. diseased individuals and/or rural vs. urban individuals. We found significant differences in the virome diversity measured in the Hill numbers between the healthy and diseased individuals, with diseased individuals exhibiting higher virome diversity than healthy individuals, and rural individual exhibiting higher virome diversity than urban individuals. We applied both "read randomization" and "sample randomization" algorithms to perform shared species analysis. With the more conservative sample randomization algorithm, the observed number of shared species was significantly smaller than the expected shared species in 50% (8 of 16) of the comparisons. These results suggest that integrated diversity and shared species analysis can offer more comprehensive insights in comparing human virome samples than standard diversity analysis alone with potentially powerful applications in differentiating the effects of diseases or other meta-factors.