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It is postulated that the tumor tissue microbiome is one of the enabling characteristics that can either promote or suppress the ability of tumors to acquire certain hallmarks of cancer. This underscores its critical importance in carcinogenesis, cancer progression, and therapy responses. However, characterizing the tumor microbiomes is extremely challenging because of their low biomass and severe difficulties in controlling laboratory-borne contaminants, which is further aggravated by lack of comprehensively effective computational approaches to identify unique or enriched microbial species associated with cancers. Here we take advantage of a recent computational framework by Ma (2024), termed metagenome comparison (MC) framework (MCF), which can detect treatment-specific, unique or enriched OMUs (operational metagenomic unit), or US/ES (unique/enriched species) when adapted for this study. We apply the MCF to reanalyze four lung cancer tissue microbiome datasets, which include samples from Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), and their adjacent normal tissue (NT) controls. Our analysis is structured around three distinct schemes: Scheme I-separately detecting the US/ES for each of the four lung cancer microbiome datasets; Scheme II-consolidation of the four datasets followed by detection of US/ES in the combined datasets; Scheme III-construction of the union and intersection sets of US/ES derived from the results of the preceding two schemes. The generated lists of US/ES, including enriched microbial phyla, likely hold significant biomedical value for developing diagnostic and prognostic biomarkers for lung cancer risk assessment, improving the efficacy of immunotherapy, and designing novel microbiome-based therapies in lung cancer research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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., (© 2024. The Author(s).)

This study explores the relationship between microbial diversity and disease status in human lung cancer tissue microbiomes, using a sample size of 1212. Analysis divided the data into primary tumour (PT) and normal tissue (NT) categories. Differences in microbial diversity between PT and NT were significant in 57% of comparisons, although dataset dependence was a factor in the diversity levels. Shared species analysis (SSA) indicated no significant differences between PT and NT in over 90% of comparisons. Network diversity assessments revealed significant differences between NT and PT regarding species relative abundances and network link abundances for q = 0-3. Additionally, significant variations were found between NT and lung squamous cell carcinoma (LUSC) at q = 0. in network link probabilities, illustrating the diversity in species interactions. Our findings suggest a stable overall microbiome diversity and composition in lung cancer patients' lung tissues despite patients with diagnosed lung tumours, indicating modified microbial interactions within the tumour. These results highlight an association between altered microbiome interaction patterns and lung tumours, offering new insights into the ecological dynamics of lung cancer microbiomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

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., Competing Interests: The author declares no conflict of interest.

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of FEMS.)

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., (© FEMS 2020.)

In a pioneering study, Zaura et al. (2009) found that majority of oral microbes fall within the five phyla including, Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and Fusobacteria. Subsequent studies further identified a set of microbes that were commonly shared among unrelated individuals (i.e., core). However, these existing studies may have not been designed to investigate the interactions among various core species. Here by harnessing the power of ecological network analysis, we identified some important ecological guilds in the form of network clusters. In particular, we found that the strongest cluster is an alliance between Firmicutes and Bacteroidetes against Actinobacteria (FBA-guild). Within the guild, we further identified two sub-guilds, the Actinobacteria-dominant sub-guild (ASG) and Firmicutes-dominant allied with Bacteroidetes sub-guild (FBSG). Furthermore, we identified so-termed guard nodes in both sub-guilds, and their role may be to inhibit the peer sub-guild given they held competitive interactions only with the outside nodes only but held cooperative interactions only with the internal nodes, which we termed civilian nodes given that they only held cooperative interactions. We postulated that FBA-guild might be to do with protection of oral health against some opportunistic pathogens from Corynebacterium and Actinomyces, the two major genera of Actinobacteria (target of FB alliance).

Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship (DDR) for the human MADs, and whether there are consistent differences in the taxonomic composition of microbiomes sampled from healthy versus diseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versus diseased individuals in 41 comparisons extracted from 27 previously published studies of human MADs. In the DDR analysis, the average effect size across studies did not differ from zero for a comparison of healthy versus diseased individuals. In 30 of 41 comparisons (73%) there was no significant difference in microbiome diversity of healthy versus diseased individuals, or of different disease classes. For the species composition analysis (shared species analysis), the effect sizes were significantly different from zero. In 33 of 41 comparisons (80%), there were fewer OTUs (operational taxonomic units) shared between healthy and diseased individuals than expected by chance, but with 49% (20 of 41 comparisons) statistically significant. These results imply that the taxonomic composition of disease-associated microbiomes is often distinct from that of healthy individuals. Because species composition changes with disease state, some microbiome OTUs may serve as potential diagnostic indicators of disease. However, the overall species diversity of human microbiomes is not a reliable indicator of disease.

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., (© 2019 John Wiley & Sons Ltd.)

SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications-ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography "maps" for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity.

We extend the ecological laws of species-time relationship (STR) and species-time-area relationship (STAR) to general diversity time-period relationship (DTR) and diversity-time-area relationship (DTAR), and test the extensions with the human vaginal microbiome datasets by building 1460 DTR/DTAR models. Our extensions were inspired by the observation that Hill numbers, well regarded as the most appropriate measure of alpha-diversity and also particularly suitable for multiplicative beta-diversity partitioning, are actually in the units of effective species, and therefore, should be able to substitute for species in the STR and STAR. We found that the traditional power law (PL) model is only applicable for DTR at diversity order zero (i.e., species richness); at higher diversity orders (q = 1-4), the power law with exponent cutoff (PLEC) and power law with inverse exponent cutoff (PLIEC) are more appropriate. In particular, PLEC has an advantage over PLIEC in predicting maximal accumulation diversity (MAD) over time. In fact, with the DTR extensions, we can construct DTR and MAD profiles. To the best of our knowledge, this is the first comprehensive investigation of the DTR/DTAR in human microbiome. Methodologically, our DTR/DTAR profiles can characterize general diversity scaling beyond species richness, covering both alpha- and beta-diversity regimes across different diversity orders.

Relatively little progress in the methodology for differentiating between the healthy and diseased microbiomes, beyond comparing microbial community diversities with traditional species richness or Shannon index, has been made. Network analysis has increasingly been called for the task, but most currently available microbiome datasets only allows for the construction of simple species correlation networks (SCNs). The main results from SCN analysis are a series of network properties such as network degree and modularity, but the metrics for these network properties often produce inconsistent evidence. We propose a simple new network property, the P/N ratio, defined as the ratio of positive links to the number of negative links in the microbial SCN. We postulate that the P/N ratio should reflect the balance between facilitative and inhibitive interactions among microbial species, possibly one of the most important changes occurring in diseased microbiome. We tested our hypothesis with five datasets representing five major human microbiome sites and discovered that the P/N ratio exhibits contrasting differences between healthy and diseased microbiomes and may be harnessed as an in silico biomarker for detecting disease-associated changes in the human microbiome, and may play an important role in personalized diagnosis of the human microbiome-associated diseases.

Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam's razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.

Several ecological hypotheses (e.g., specific plaque, non-specific plaque and keystone pathogen) regarding the etiology of periodontitis have been proposed since the 1990s, most of which have been centered on the concept of dysbiosis associated with periodontitis. Nevertheless, none of the existing hypotheses have presented mechanistic interpretations on how and why dysbiosis actually occurs. Hubbell's neutral theory of biodiversity offers a powerful null model to test hypothesis regarding the mechanism of community assembly and diversity maintenance from the metagenomic sequencing data, which can help to understand the forces that shape the community dynamics such as dysbiosis. Here we reanalyze the dataset from Abusleme et al.'s comparative study of the oral microbial communities from periodontitis patients and healthy individuals. Our study demonstrates that 14 out of 61 communities (23%) passed the neutrality test, a percentage significantly higher than the previous reported neutrality rate of 1% in human microbiome (Li & Ma 2016, Scientific Reports). This suggests that, while the niche selection may play a predominant role in the assembly and diversity maintenance in oral microbiome, the effect of neutral dynamics may not be ignored. However, no statistically significant differences in the neutrality passing rates were detected between the periodontitis and healthy treatments with Fisher's exact probability test and multiple testing corrections, suggesting that the mechanism of community assembly is robust against disturbances such as periodontitis. In addition, our study confirmed previous finding that periodontitis patients exhibited higher biodiversity. These findings suggest that while periodontitis may significantly change the community composition measured by diversity (i.e., the exhibition or 'phenotype' of community assembly), it does not seem to cause the 'mutation' of the 'genotype" (mechanism) of community assembly. We argue that the 'phenotypic' changes explain the observed link (not necessarily causal) between periodontitis and community dysbiosis, which is certainly worthy of further investigation.

The human microbiome project (HMP) has made it possible to test important ecological theories for arguably the most important ecosystem to human health-the human microbiome. Existing limited number of studies have reported conflicting evidence in the case of the neutral theory; the present study aims to comprehensively test the neutral theory with extensive HMP datasets covering all five major body sites inhabited by the human microbiome. Utilizing 7437 datasets of bacterial community samples, we discovered that only 49 communities (less than 1%) satisfied the neutral theory, and concluded that human microbial communities are not neutral in general. The 49 positive cases, although only a tiny minority, do demonstrate the existence of neutral processes. We realize that the traditional doctrine of microbial biogeography "Everything is everywhere, but the environment selects" first proposed by Baas-Becking resolves the apparent contradiction. The first part of Baas-Becking doctrine states that microbes are not dispersal-limited and therefore are neutral prone, and the second part reiterates that the freely dispersed microbes must endure selection by the environment. Therefore, in most cases, it is the host environment that ultimately shapes the community assembly and tip the human microbiome to niche regime.

We aim to investigate the effects of Hodgkin's lymphoma and the chemotherapy for treating the disease on the human milk microbiome through integrated network and community diversity analyses. Our analyses suggest that Hodgkin's lymphoma seems to have a suppressing effect on the milk microbiome by lowering the milk microbial community diversity, as measured by the Hill numbers profiles. Although the diversity analysis did not reveal an effect of chemotherapy on community diversity, bacterial species interaction network analysis shows that chemotherapy may help to slightly restore the milk microbiome impacted by Hodgkin's lymphoma through its influence on the interactions among species (or OTUs). We further constructed diversity-metabolites network, which suggests that the milk microbial diversity is positively correlated with some beneficial milk metabolites such as DHA (DocosaHexaenoic Acid), and that the diversity is negatively correlated with some potentially harmful metabolites such as Butanal. We hence postulate that higher milk microbial diversity should be a signature of healthy mothers and beneficial to infants. Finally, we constructed metabolites OTU correlation networks, from which we identified some special OTUs. These OTUs deserve further investigations given their apparent involvements in regulating the levels of critical milk metabolites such as DHA, Inositol and Butanal.

Taylor's (1961, Nature, 189:732) power law, a power function (V = am(b) ) describing the scaling relationship between the mean and variance of population abundances of organisms, has been found to govern the population abundance distributions of single species in both space and time in macroecology. It is regarded as one of few generalities in ecology, and its parameter b has been widely applied to characterize spatial aggregation (i.e. heterogeneity) and temporal stability of single-species populations. Here, we test its applicability to bacterial populations in the human microbiome using extensive data sets generated by the US-NIH Human Microbiome Project (HMP). We further propose extending Taylor's power law from the population to the community level, and accordingly introduce four types of power-law extensions (PLEs): type I PLE for community spatial aggregation (heterogeneity), type II PLE for community temporal aggregation (stability), type III PLE for mixed-species population spatial aggregation (heterogeneity) and type IV PLE for mixed-species population temporal aggregation (stability). Our results show that fittings to the four PLEs with HMP data were statistically extremely significant and their parameters are ecologically sound, hence confirming the validity of the power law at both the population and community levels. These findings not only provide a powerful tool to characterize the aggregations of population and community in both time and space, offering important insights into community heterogeneity in space and/or stability in time, but also underscore the three general properties of power laws (scale invariance, no average and universality) and their specific manifestations in our four PLEs., (© 2015 John Wiley & Sons Ltd.)

Human gut microbiota shows high inter-subject variations, but the actual spatial distribution and co-occurrence patterns of gut mucosa microbiota that occur within a healthy human instestinal tract remain poorly understood. In this study, we illustrated a model of this mucosa bacterial communities' biogeography, based on the largest data set so far, obtained via 454-pyrosequencing of bacterial 16S rDNAs associated with 77 matched biopsy tissue samples taken from terminal ileum, ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum of 11 healthy adult subjects. Borrowing from macro-ecology, we used both Taylor's power law analysis and phylogeny-based beta-diversity metrics to uncover a highly heterogeneous distribution pattern of mucosa microbial inhabitants along the length of the intestinal tract. We then developed a spatial dispersion model with an R-squared value greater than 0.950 to map out the gut mucosa-associated flora's non-linear spatial distribution pattern for 51.60% of the 188 most abundant gut bacterial species. Furthermore, spatial co-occurring network analysis of mucosa microbial inhabitants together with occupancy (that is habitat generalists, specialists and opportunist) analyses implies that ecological relationships (both oppositional and symbiotic) between mucosa microbial inhabitants may be important contributors to the observed spatial heterogeneity of mucosa microbiota along the human intestine and may even potentially be associated with mutual cooperation within and functional stability of the gut ecosystem.