Long-term grazing reduces soil fungal network complexity but enhances plant-soil microbe network connectivity in a semi-arid grassland.

Grazing plays a significant role in shaping both aboveground vegetation and belowground microbial communities in arid and semi-arid grasslands, which in turn affects ecosystem functions and sustainability. Therefore, it was essential to implement effective grazing management practices to preserve ecological balance and support sustainable development in these delicate environments. To optimize the traditional continuous grazing policy, we conducted a 10-year seasonal grazing experiment with five treatments in a typical grassland in northern China: no grazing (NG), continuous summer grazing (CG), and three seasonal grazing treatments (G57 in May and July, G68 in June and August, and G79 in July and September). Our study found that although grazing reduced plant community biomass, G68 treatment maintained high plant height and community diversity (P < 0.05). Grazing did not affect soil bacterial and archaeal alpha diversity, but CG treatment reduced soil fungal diversity (P < 0.05). CG reduced the archaeal network's vertices (which represent microbial taxa, OTUs) and connections (ecological interactions between taxa), but seasonal grazing increased its complexity. Furthermore, grazing did not change bacterial networks but enhanced cross-domain interactions (relationships between different biological groups) of plant-soil fungi and plant-soil archaea. Overall, we used the Mantel test to find that soil microbial diversity was positively correlated with soil physicochemical properties rather than plant community characteristics after grazing. These findings are beneficial for the optimization of sustainable grassland management policies and the protection of plant and soil biodiversity.

Plant domestication shapes rhizosphere microbiome assembly and metabolic functions.

BACKGROUND: The rhizosphere microbiome, which is shaped by host genotypes, root exudates, and plant domestication, is crucial for sustaining agricultural plant growth. Despite its importance, how plant domestication builds up specific rhizosphere microbiomes and metabolic functions, as well as the importance of these affected rhizobiomes and relevant root exudates in maintaining plant growth, is not well understood. Here, we firstly investigated the rhizosphere bacterial and fungal communities of domestication and wild accessions of tetraploid wheat using amplicon sequencing (16S and ITS) after 9 years of domestication process at the main production sites in China. We then explored the ecological roles of root exudation in shaping rhizosphere microbiome functions by integrating metagenomics and metabolic genomics approaches. Furthermore, we established evident linkages between root morphology traits and keystone taxa based on microbial culture and plant inoculation experiments. RESULTS: Our results suggested that plant rhizosphere microbiomes were co-shaped by both host genotypes and domestication status. The wheat genomes contributed more variation in the microbial diversity and composition of rhizosphere bacterial communities than fungal communities, whereas plant domestication status exerted much stronger influences on the fungal communities. In terms of microbial interkingdom association networks, domestication destabilized microbial network and depleted the abundance of keystone fungal taxa. Moreover, we found that domestication shifted the rhizosphere microbiome from slow growing and fungi dominated to fast growing and bacteria dominated, thereby resulting in a shift from fungi-dominated membership with enrichment of carbon fixation genes to bacteria-dominated membership with enrichment of carbon degradation genes. Metagenomics analyses further indicated that wild cultivars of wheat possess higher microbial function diversity than domesticated cultivars. Notably, we found that wild cultivar is able to harness rhizosphere microorganism carrying N transformation (i.e., nitrification, denitrification) and P mineralization pathway, whereas rhizobiomes carrying inorganic N fixation, organic N ammonification, and inorganic P solubilization genes are recruited by the releasing of root exudates from domesticated wheat. More importantly, our metabolite-wide association study indicated that the contrasting functional roles of root exudates and the harnessed keystone microbial taxa with different nutrient acquisition strategies jointly determined the aboveground plant phenotypes. Furthermore, we observed that although domesticated and wild wheats recruited distinct microbial taxa and relevant functions, domestication-induced recruitment of keystone taxa led to a consistent growth regulation of root regardless of wheat domestication status. CONCLUSIONS: Our results indicate that plant domestication profoundly influences rhizosphere microbiome assembly and metabolic functions and provide evidence that host plants are able to harness a differentiated ecological role of root-associated keystone microbiomes through the release of root exudates to sustain belowground multi-nutrient cycles and plant growth. These findings provide valuable insights into the mechanisms underlying plant-microbiome interactions and how to harness the rhizosphere microbiome for crop improvement in sustainable agriculture. Video Abstract.

Microbes drive metabolism, community diversity, and interactions in response to microplastic-induced nutrient imbalance.

The impact of conventional and biodegradable microplastics on soil nutrients (carbon and nitrogen) has been widely examined, and the alteration of nutrient conditions further influences microbial biosynthesis processes. Nonetheless, the influence of microplastic-induced nutrient imbalances on soil microorganisms (from metabolism to community interactions) is still not well understood. We hypothesized that conventional and biodegradable microplastic could alter soil nutrients and microbial processes. To fill this knowledge gap, we conducted soil microcosms with polyethylene (PE, new and aged) and polylactic acid (PLA, new and aged) microplastics to evaluate their effects on the soil enzymatic stoichiometry, co-occurrence interactions, and success patterns of soil bacterial communities. New and aged PLA induced soil N immobilization, which decreased soil mineral N by 91-141 %. The biodegradation of PLA led to a higher bioavailable C and wider bioavailable C:N ratio, which further filtered out specific microbial species. Both new and aged PLA had a higher abundance of copiotrophic members (Proteobacteria, 35-51 % in PLA, 26-34 % in CK/PE treatments) and rrn copy number. The addition of PLA resulted in a lower alpha diversity and reduced network complexity. Conversely, because of the chemically stable hydrocarbon structure of PE polymers, the new and aged PE microplastics had a minor effect on soil mineral N, bacterial community composition, and network complexity, but led to microbial C limitation. Collectively, all microplastics increased soil C-, N-, and P -acquiring enzyme activities and reduced the number of keystone species and the robustness of the co-occurrence network. The PLA treatment enhanced nitrogen fixation and ureolysis, whereas the PE treatment increased the degradation of recalcitrant carbon. Overall, the alteration of soil nutrient conditions by microplastics affected the microbial metabolism and community interactions, although the effects of PE and PLA microplastics were distinct.

Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis.

Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.

Modeling spatial interaction networks of the gut microbiota.

How the gut microbiota is organized across space is postulated to influence microbial succession and its mutualistic relationships with the host. The lack of dynamic or perturbed abundance data poses considerable challenges for characterizing the spatial pattern of microbial interactions. We integrate allometric scaling theory, evolutionary game theory, and prey-predator theory into a unified framework under which quasi-dynamic microbial networks can be inferred from static abundance data. We illustrate that such networks can capture the full properties of microbial interactions, including causality, the sign of the causality, strength, and feedback loop, and are dynamically adaptive along spatial gradients, and context-specific, characterizing variability between individuals and within the same individual across time and space. We design and conduct a gut microbiota study to validate the model, characterizing key spatial determinants of the microbial differences between ulcerative colitis and healthy controls. Our model provides a sophisticated means of unraveling a complete atlas of how microbial interactions vary across space and quantifying causal relationships between such spatial variability and change in health state.

Hypergraph clustering based on modularity feature projection for high-order relationship community detection of microorganisms.

Microbial community is an important part of organisms or ecosystems to maintain health and stability. Analyzing the interaction of microorganisms in the ecosystem and mining the co-occurrence module of the microbial community can deepen the understanding of microbial community function. This could also improve the ability to manipulate the microbial community, thus provide new means for ecological restoration, disease treatment and drug development. Instead of the investigations of pairwise relationships, more and more studies have realized that the higher-order interactions may play important roles in explaining the diversity and complexity of the community. In this study, a hypergraph clustering (HCMFP) based on modularity feature projection is proposed to detect the microbial community in higher-order interaction network among microbes. Specifically, HCMFP uses information entropy to mine the higher-order logical relationships among microbes, and constructs a hypergraph learning model based on modularity feature projection to detect the microbial community. The experimental results show that compared with other methods, HCMFP has better clustering performance and reliable convergence speed. The proposed method is an effective tool for high-order organizations in microbial interaction network. The code and data in this study is freely available at https://github.com/Mayingjun20179/ HCMFP.

Indole metabolism by phenol-stimulated activated sludges: Performance, microbial communities and network analysis.

Indole and phenol often coexist in the coking wastewater, while the effects of phenol on microbial communities of indole metabolism were less explored. In this study, the microbial interactions within activated sludge microbial communities stimulated by indole (group A) or by indole and phenol (group B) were systematically investigated in sequencing batch reactors (SBRs). The results showed that the removal of indole was increased by adding phenol. By using high-throughput sequencing technology, it was found that α-diversity was reduced in both groups. According to the relative abundance analysis, the indole-degrading genus Comamonas was the core genus in both groups (33.94% and 61.40%). But another indole-degrading genus Pseudomonas was only enriched in group A with 12.22% relative abundance. Meanwhile, common aromatic degrading genus Dyella and Thermomonas were enriched only in group B. It was found that the relative abundance of cytochrome P450 and styrene degradation enzymes were increased in group B by PICRUSt analysis. Based on the phylogenetic molecular ecological networks (pMENs), module hub OTU_1149 (Burkholderia) was only detected in group B, and the positive interactions between the key functional genus Burkholderia and other bacteria were increased. This study provides new insights into our understanding of indole metabolism communities stimulated by phenol, which would provide useful information for practical coking wastewater treatment.

Rhizosphere analysis of field-grown Panax ginseng with different degrees of red skin provides the basis for preventing red skin syndrome.

BACKGROUND: Ginseng red skin root syndrome (GRS) is one of the most common ginseng (Panax ginseng Meyer) diseases. It leads to a severe decline in P. ginseng quality and seriously affects the P. ginseng industry in China. However, as a root disease, the characteristics of the GRS rhizosphere microbiome are still unclear. METHODS: The amplicon bacterial 16 S rRNA genes and fungal ITS (Internal Transcribed Spacer) regions Illumina sequencing technology, combined with microbial diversity and composition analysis based on R software, was used to explore the relationship between soil ecological environment and GRS. RESULTS: There were significant differences in the diversity and richness of soil microorganisms between the rhizosphere with different degrees of disease, especially between healthy P. ginseng (HG) and heavily diseased groups. The variation characteristics of microbial abundance in different taxa levels were analyzed. The interaction network of rhizosphere microorganisms of P. ginseng under GRS background was established. We also found that different P. ginseng rhizosphere microbial communities have multiple changes in stability and complexity through the established interaction network. Microbes closely related to potential pathogenic fungi were also identified according to the interaction network, which provided clues for looking for biological control agents. Finally, the Distance-based redundancy analysis (dbRDA) results indicated that total phosphorus (TP), available potassium (AK), available phosphorus (AP), catalase (CAT), invertase (INV) are the key factors that influence the microbial communities. Moreover, the content of these key factors in the rhizosphere was negatively correlated with disease degrees. CONCLUSIONS: In this study, we comprehensively analyzed the rhizosphere characteristics of P. ginseng with different levels of disease, and explored the interaction relationship among microorganisms. These results provide a basis for soil improvement and biological control of field-grown in the future.

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes.

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

Identification of microbial interaction network: zero-inflated latent Ising model based approach.

BACKGROUND: Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. METHODS AND RESULTS: To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. CONCLUSIONS: Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

Characterization of the Aedes albopictus (Diptera: Culicidae) holobiome: bacterial composition across land use type and mosquito sex in Malaysia.

Understanding the diversity and dynamics of the microbiota within the mosquito holobiome is of great importance to apprehend how the microbiota modulates various complex processes and interactions. This study examined the bacterial composition of Aedes albopictus across land use type and mosquito sex in the state of Selangor, Malaysia using 16S rRNA sequencing. The bacterial community structure in mosquitoes was found to be influenced by land use type and mosquito sex, with the environment and mosquito diet respectively identified to be the most likely sources of microbes. We found that approximately 70% of the microbiota samples were dominated by Wolbachia and removing Wolbachia from analyses revealed the relatively even composition of the remaining bacterial microbiota. Furthermore, microbial interaction network analysis highlighted the prevalence of co-exclusionary patterns in all networks regardless of land use and mosquito sex, with Wolbachia exhibiting co-exclusionary interactions with other residential bacteria such as Xanthomonas, Xenophilus and Zymobacter.

IMPARO: inferring microbial interactions through parameter optimisation.

BACKGROUND: Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can be inferred by examining microbial abundance profiles. Abundance profiles are often interpreted with the Lotka Volterra model in research. However existing research fails to consider a biologically meaningful underlying mathematical model for MINs or to address the possibility of multiple solutions. RESULTS: In this paper we present IMPARO, a method for inferring microbial interactions through parameter optimisation. We use biologically meaningful models for both the abundance profile, as well as the MIN. We show how multiple MINs could be inferred with similar reconstructed abundance profile accuracy, and argue that a unique solution is not always satisfactory. Using our method, we successfully inferred clear interactions in the gut microbiome which have been previously observed in in-vitro experiments. CONCLUSIONS: IMPARO was used to successfully infer microbial interactions in human microbiome samples as well as in a varied set of simulated data. The work also highlights the importance of considering multiple solutions for MINs.

Florfenicol restructured the microbial interaction network for wastewater treatment by microbial electrolysis cells.

To investigate the influence of antibiotics on microbial interactions in a biofilm community, we set up eight replicate reactors of microbial electrolysis cell (MEC) and applied a broad-spectrum antibiotic florfenical (FLO) as an environmental disturbance. According to the results, exposure to FLO resulted in degradation of reactor performance. The MEC could also rebound back to the comparably stable state at a certain time which exhibited a great resilience ability in response to antibiotic disturbance. The FLO perturbation showed a significant influence on the electroactive biofilms (EABs) with a distinct reformation of the community structure. Network analysis revealed that microbial interactions in the biofilms after full recovery became much closer, with a rapid increase in the positive interactions between the predominant genus Geobacter and other microorganisms as compared to the stage before FLO disturbance. Moreover, the keystone species in the networks after full recovery possessed more connections between Geobacter and potential synergistic species. Our results demonstrated that FLO, with broad-spectrum antibacterial ability, could restructure the EABs with more positive interactions for hydrogen production. This study demonstrated the response mechanisms of the MECs to the antibiotic disturbance, providing a scientific reference for the rapid development of this biotechnology to treat wastewater containing antibiotics.

Interrogation of Internal Workings in Microbial Community Assembly: Play a Game through a Behavioral Network?

Increasing evidence shows that the influence of microbiota on biogeochemical cycling, plant development, and human health is executed through a complex network of microbe-microbe interactions. However, characterizing how microbes interact and work together within closely packed and highly heterogeneous microbial ecosystems is extremely challenging. Here, we describe a rule-of-thumb framework for visualizing polymicrobial interactions and extracting general principles that underlie microbial communities. We integrate elements of metabolic ecology, behavioral ecology, and game theory to quantify the interactive strategies by which microbes at any taxonomic level compete for resources and cooperate symbiotically with each other to form and stabilize ecological communities. We show how the framework can chart an omnidirectional landscape of microbial cooperation and competition that may drive various natural processes. This framework can be implemented into genome-wide association studies to unravel the genetic mechanisms underlying microbial interaction networks and their evolutionary consequences along spatiotemporal gradients.IMPORTANCE Identifying general biological rules that underlie the complexity and heterogeneity of microbial communities has proven to be highly challenging. We present a rule-of-thumb framework for studying and characterizing how microbes interact with each other across different taxa to determine community behavior and dynamics. This framework is computationally simple but conceptually meaningful, and it can provide a starting point to generate novel biological hypotheses about microbial interactions and explore internal workings of microbial community assembly in depth.

Microbial Interaction Network Inference in Microfluidic Droplets.

Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.

Functional response of sediment bacterial community to iron-reducing bioaugmentation with Shewanella decolorationis S12.

Bioaugmentation with exogenously functional microbes is a widely used technology in bioengineering and environmental remediation. Generally, the colonization of inoculated bacteria is considered to be the determining factor in technical success. However, increasing reports have shown that bioaugmentation was still effective when the colonization of inoculated bacteria was unsuccessful. Here, an augmentation study with iron-reducing bacteria (IRB, Shewanella decolorationis S12) was conducted in Fe(II)-poor sediments to elucidate the role of exogenously inoculated bacteria for bioaugmentation performance. The results showed that a sufficient amount of IRB inputs enhanced the iron reduction in bioaugmented sediments, even though the exogenous IRB did not colonize after the beginning of the experiment (less than 1% at day 3). The iron reduction function responded to stimulation of the indigenous IRB community such as Clostridium, Cupriavidus, Fervidicella, and Acinetobacter, which comprised less than 1% in the initial sediments. Moreover, compared with microbial community in control sediment, more positive correlations between OTUs were observed for that in S12-added sediments upon network analysis. The pH and oxidation-reduction potential of sediment were found to be the predominant factors shaping the iron-reducing microbial communities. It meant that exogenous IRB successfully trigged functional community via altering microenvironment by the inoculated bacteria. Overall, this study provides a new insight into the understanding of the role of single strain addition in iron-reducing bioaugmentation.

Distinct Network Interactions in Particle-Associated and Free-Living Bacterial Communities during a Microcystis aeruginosa Bloom in a Plateau Lake.

Particle-associated bacteria (PAB) and free-living bacteria (FLB) from aquatic environments during phytoplankton blooms differ in their physical distance from algae. Both the interactions within PAB and FLB community fractions and their relationship with the surrounding environmental properties are largely unknown. Here, by using high-throughput sequencing and network-based analyses, we compared the community and network characteristics of PAB and FLB from a plateau lake during a Microcystis aeruginosa bloom. Results showed that PAB and FLB differed significantly in diversity, structure and microbial connecting network. PAB communities were characterized by highly similar bacterial community structure in different sites, tighter network connections, important topological roles for the bloom-causing M. aeruginosa and Alphaproteobacteria, especially for the potentially nitrogen-fixing (Pleomorphomonas) and algicidal bacteria (Brevundimonas sp.). FLB communities were sensitive to the detected environmental factors and were characterized by significantly higher bacterial diversity, less connectivity, larger network size and marginal role of M. aeruginosa. In both networks, covariation among bacterial taxa was extensive (>88% positive connections), and bacteria potentially affiliated with biogeochemical cycling of nitrogen (i.e., denitrification, nitrogen-fixation and nitrite-oxidization) were important in occupying module hubs, such as Meganema, Pleomorphomonas, and Nitrospira. These findings highlight the importance of considering microbial network interactions for the understanding of blooms.

Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm.

BACKGROUND: Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges. RESULTS: This work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN. CONCLUSIONS: Performance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.

Metagenomic surveys of gut microbiota.

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational taxonomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ultimately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interaction among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe-microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe-microbe interaction networks.