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

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.

Distinct compositions and functions of circulating microbial DNA in the peripheral blood compared to fecal microbial DNA in healthy individuals

The crucial function of circulating microbial DNA (cmDNA) in peripheral blood is gaining recognition because of its importance in normal physiology and immunity in healthy individuals. Evidence suggests that cmDNA in peripheral blood is derived from highly abundant, translocating gut microbes. However, the associations with and differences between cmDNA in peripheral blood and the gut microbiome remain unclear. We collected blood, urine, and fecal samples from volunteers to compare their microbial information via 16S rDNA sequencing. The results revealed that, compared with gut microbial DNA, cmDNA in peripheral blood was associated with reduced diversity and a distinct microbiota composition. The cmDNA in the blood reflects the biochemical processes of microorganisms, including synthesis, energy conversion, degradation, and adaptability, surpassing that of fecal samples. Interestingly, cmDNA in blood showed a limited presence of DNA from anaerobes and gram-positive bacteria, which contrast with the trend observed in fecal samples. Furthermore, analysis of cmDNA revealed traits associated with mobile elements and potential pathologies, among others, which were minimal in stool samples. Notably, cmDNA analysis indicated similarities between the microbial functions and phenotypes in blood and urine samples, although greater diversity was observed in urine samples. Source Tracker analysis suggests that gut microbes might not be the main source of blood cmDNA, or a selective mechanism allows only certain microbial DNA into the bloodstream. In conclusion, our study highlights the composition and potential functions associated with cmDNA in peripheral blood, emphasizing its selective presence; however, further research is required to elucidate the mechanisms involved.IMPORTANCEOur research provides novel insights into the unique characteristics and potential functional implications of circulating microbial DNA (cmDNA) in peripheral blood. Unlike other studies that analyzed sequencing data from fecal or blood microbiota in different study cohorts, our comparative analysis of cmDNA from blood, urine, and fecal samples from the same group of volunteers revealed a distinct blood-specific cmDNA composition. We discovered a decreased diversity of microbial DNA in blood samples compared to fecal samples as well as an increased presence of biochemical processes microbial DNA in blood. Notably, we add to the existing knowledge by documenting a reduced abundance of anaerobes and gram-positive bacteria in blood compared to fecal samples according to the analysis of cmDNA and gut microbial DNA, respectively. This observation suggested that a potential selective barrier or screening mechanism might filter microbial DNA molecules, indicating potential selectivity in the translocation process which contrasts with the traditional view that cmDNA primarily originates from random translocation from the gut and other regions. By highlighting these differences, our findings prompt a reconsideration of the origin and role of cmDNA in blood circulation and suggest that selective processes involving more complex biological mechanisms may be involved.

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.

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.

Structure and Dynamics of the Breast Tissue Microbiomes Under Tumor Influences: An Approach With Neutral, Near-Neutral, and Niche-Neutral Hybrid Models

The structure and dynamics of breast tissue bacteria can have far-reaching influences on women’s health, particularly on breast tumor development. However, there is little understanding on the ecological processes that shape the structure and dynamics of breast tissue bacteria. Here, we fill the gap by applying three metacommunity models for investigating the community assembly and diversity maintenance, including Sloan near neutral model, Harris et al. multisite neutral and Tang & Zhou niche-neutral hybrid models to reanalyze the 16S-rRNA sequencing datasets of 23 healthy, 12 benign tumor, and 33 malignant tumor tissue samples. First, we found that, at the community/metacommunity levels, the mechanisms of bacteria assembly and diversity maintenance of breast tissue bacteria were moderately influenced by stochastic drifts of bacteria demography (division, death, and dispersal of bacterial cells). At species level, on average, approximately 10 and 5% species were above (positively selected) and below (negatively selected) neutral, respectively. Furthermore, malignant tumor may raise the positively selected species up to 17%. Second, malignant tumor appears to inhibit microbial dispersal as evidenced by lowered migration rates, compared with the migration in normal and benign tumor tissues. These theoretic findings can be inspirational for further investigating the relationships between tissue bacteria and breast tumor progression/development.

Predicting the Outbreak Risks and Inflection Points of COVID-19 Pandemic with Classic Ecological Theories

Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID-19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID-19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor's power-law) and Ma's population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell's neutral theory, and Ma's diversity-time relationship adapted for the infection-time relationship. Fisher-Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b-values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID-19. (ii) The infection aggregation critical threshold (M 0) adapted from PACD varies with time (outbreak-stage), space (region) and public-health interventions. Exceeding M 0, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally.