Metagenomics meets time series analysis: unraveling microbial community dynamics

Gut microbiota in hepatocellular carcinoma immunotherapy: immune microenvironment remodeling and gut microbiota modification

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with limited treatment options at advanced stages. The gut microbiota, a diverse community of microorganisms residing in the gastrointestinal tract, plays a pivotal role in regulating immune responses through the gut-liver axis. Emerging evidence underscores its impact on HCC progression and the efficacy of immunotherapy. This review explores the intricate interactions between gut microbiota and the immune system in HCC, with a focus on key immune cells and pathways involved in tumor immunity. Additionally, it highlights strategies for modulating the gut microbiota - such as fecal microbiota transplantation, dietary interventions, and probiotics - as potential approaches to enhancing immunotherapy outcomes. A deeper understanding of these mechanisms could pave the way for novel therapeutic strategies aimed at improving patient prognosis.

Exploratory Analysis of Gut Microbiota Profile in Duchenne Muscular Dystrophy (DMD) Patients with Intellectual Disability

This study investigates the differences in gut microbiota composition between DMD patients with (DMD +) and without (DMD -) intellectual disability (ID) and its potential role in cognitive outcomes. In this study, we assessed the gut microbiota in 50 genetically confirmed DMD patients (median age 13.1 years) using 16S rRNA gene sequencing. Cognitive assessment was performed using the Wechsler Intelligence Scales, with ID defined as an IQ < 70. Stool samples were analyzed, and statistical methods were used to assess alpha- and beta-diversity. Thirty-four percent of patients had ID. No significant differences were found in alpha-diversity or in the Firmicutes/Bacteroidetes ratio. However, beta-diversity analysis revealed significant differences between DMD + and DMD - groups, including, in DMD + , an increased abundance of Propionibacterium and Bifidobacterium, and a reduction in Bulleidia. These bacteria are involved in metabolic pathways that can influence neurological health through the gut-brain axis, particularly via the production of short-chain fatty acids. While these preliminary findings suggest a possible association between gut microbiota profile and cognitive impairment in DMD, further research is needed to explore a causal relationship and consider microbiota-targeted therapeutic strategies.

Metagenomic Characterization of Microbiome Taxa Associated with Coral Reef Communities in North Area of Tabuk Region, Saudia Arabia

The coral microbiome is highly related to the overall health and the survival and proliferation of coral reefs. The Red Sea's unique physiochemical characteristics, such a significant north-south temperature and salinity gradient, make it a very intriguing research system. However, the Red Sea is rather isolated, with a very diversified ecosystem rich in coral communities, and the makeup of the coral-associated microbiome remains little understood. Therefore, comprehending the makeup and dispersion of the endogenous microbiome associated with coral is crucial for understanding how the coral microbiome coexists and interacts, as well as its contribution to temperature tolerance and resistance against possible pathogens. Here, we investigate metagenomic sequencing targeting 16S rRNA using DNAs from the sediment samples to identify the coral microbiome and to understand the dynamics of microbial taxa and genes in the surface mucous layer (SML) microbiome of the coral communities in three distinct areas close to and far from coral communities in the Red Sea. These findings highlight the genomic array of the microbiome in three areas around and beneath the coral communities and revealed distinct bacterial communities in each group, where Pseudoalteromonas agarivorans (30%), Vibrio owensii (11%), and Pseudoalteromonas sp. Xi13 (10%) were the most predominant species in samples closer to coral (a coral-associated microbiome), with the domination of Pseudoalteromonas_agarivorans and Vibrio_owensii in Alshreah samples distant from coral, while Pseudoalteromonas_sp._Xi13 was more abundant in closer samples. Moreover, Proteobacteria such as Pseudoalteromonas, Pseudomonas and Cyanobacteria were the most prevalent phyla of the coral microbiome. Further, Saweehal showed the highest diversity far from corals (52.8%) and in Alshreah (7.35%) compared to Marwan (1.75%). The microbial community was less diversified in the samples from Alshreah Far (5.99%) and Marwan Far (1.75%), which had comparatively lower values for all indices. Also, Vibrio species were the most prevalent microorganisms in the coral mucus, and the prevalence of these bacteria is significantly higher than those found in the surrounding saltwater. These findings reveal that there is a notable difference in microbial diversity across the various settings and locales, revealing that geographic variables and coral closeness affect the diversity of microbial communities. There were significant differences in microbial community composition regarding the proximity to coral. In addition, there were strong positive correlations between genera Pseudoalteromonas and Vibrio in close-to-coral environments, suggesting that these bacteria may play a synergistic role in Immunizing coral, raising its tolerance towards environmental stress and overall coral health.

Microbial functional guilds respond cohesively to rapidly fluctuating environments

Microbial communities experience environmental fluctuations across timescales from rapid changes in moisture, temperature, or light levels to long-term seasonal or climactic variations. Understanding how microbial populations respond to these changes is critical for predicting the impact of perturbations, interventions, and climate change on communities. Since communities typically harbor tens to hundreds of distinct taxa, the response of microbial abundances to perturbations is potentially complex. However, while taxonomic diversity is high, in many communities taxa can be grouped into functional guilds of strains with similar metabolic traits. These guilds effectively reduce the complexity of the system by providing a physiologically motivated coarse-graining. Here, using a combination of simulations, theory, and experiments, we show that the response of guilds to nutrient fluctuations depends on the timescale of those fluctuations. Rapid changes in nutrient levels drive cohesive, positively correlated abundance dynamics within guilds. For slower timescales of environmental variation, members within a guild begin to compete due to similar resource preferences, driving negative correlations in abundances between members of the same guild. Our results provide a route to understanding the relationship between functional guilds and community response to changing environments, as well as an experimental approach to discovering functional guilds via designed nutrient perturbations to communities.

Dynamics of wheat rhizosphere microbiome and its impact on grain production across growth stages

Crop plant microbiomes are increasingly seen as important in plant nutrition and health, and a key to maintaining food productivity. Currently, little is known of the temporal changes that occur in the wheat rhizosphere microbiome as the plant develops, and how this varies among different sites. We used a pot-based mesocosm experiment with the same modern wheat cultivar grown in eight soils from across the North China Plain, a major wheat producing area. DNA from rhizosphere soil was taken from wheat plants, from seedling up to grain harvesting stage, and amplicon sequenced for prokaryotes and microeukaryotes, followed by community analysis. Our results showed that rhizosphere diversity of prokaryotes and microeukaryotes increased over time in most sites. While there was turnover between earlier- and later-arriving species, the predominant successional model was accumulation, with early arrivals remaining in place as others colonized the rhizosphere. Rhizosphere community network modularity and stability increased during the development and maturation of the wheat plant. The abundances of certain stage-specific keystone species were correlated with eventual grain yield - suggesting a potentially important role in wheat production. Some keystone species belonged to groups previously implicated in various functions. This study provides a basis for further experimental investigation of the wheat rhizosphere microbiome, its role in determining crop yields, and the potential for microbiome engineering to promote yields. The sequential arrival and accumulation of microbiota suggests that deliberate inoculation might accelerate this process.

Growth-rate distributions of gut microbiota time series

Logarithmic growth-rates are fundamental observables for describing ecological systems and the characterization of their distributions with analytical techniques can greatly improve their comprehension. Here a neutral model based on a stochastic differential equation with demographic noise, which presents a closed form for these distributions, is used to describe the population dynamics of microbiota. Results show that this model can successfully reproduce the log-growth rate distribution of the considered abundance time-series. More significantly, it predicts its temporal dependence, by reproducing its kurtosis evolution when the time lag τ is increased. Furthermore, its typical shape for large τ is assessed, verifying that the distribution variance does not diverge with τ. The simulated processes generated by the calibrated stochastic equation and the analysis of each time-series, taken one by one, provided additional support for our approach. Alternatively, we tried to describe our dataset by using a logistic neutral model with an environmental stochastic term. Analytical and numerical results show that this model is not suited for describing the leptokurtic log-growth rates distribution found in our data. These results support an effective neutral model with demographic stochasticity for describing the considered microbiota.

Biotic and abiotic factors acting on community assembly in parallel anaerobic digestion systems from a brewery wastewater treatment plant

Anaerobic digestion is a complex microbial process that mediates the transformation of organic waste into biogas. The performance and stability of anaerobic digesters relies on the structure and function of the microbial community. In this study, we asked whether the deterministic effect of wastewater composition outweighs the effect of reactor configuration on the structure and dynamics of anaerobic digester archaeal and bacterial communities. Biotic and abiotic factors acting on microbial community assembly in two parallel anaerobic digestion systems, an upflow anaerobic sludge blanket digestor (UASB) and a closed digester tank with a solid recycling system (CDSR), from a brewery WWTP were analysed utilizing 16S rDNA and mcrA amplicon sequencing and genome-centric metagenomics. This study confirmed the deterministic effect of the wastewater composition on bacterial community structure, while the archaeal community composition resulted better explained by organic loading rate (ORL) and volatile free acids (VFA). According to the functions assigned to the differentially abundant metagenome-assembled genomes (MAGs) between reactors, CDSR was enriched in genes related to methanol and methylamines methanogenesis, protein degradation, and sulphate and alcohol utilization. Conversely, the UASB reactor was enriched in genes associated with carbohydrate and lipid degradation, as well as amino acid, fatty acid, and propionate fermentation. By comparing interactions derived from the co-occurrence network with predicted metabolic interactions of the prokaryotic communities in both anaerobic digesters, we conclude that the overall community structure is mainly determined by habitat filtering.

Structure and composition of early biofilms formed on dental implants are complex, diverse, subject-specific and dynamic

Biofilm-associated peri-implant infections pose a major problem in modern medicine. The understanding of biofilm development is hampered by biofilm complexity and the lack of robust clinical models. This study comprehensively characterized the dynamics of early biofilm formation in the transmucosal passage of implant abutments in 12 patients. Biofilm structures and compositions were complex, diverse, subject-specific and dynamic. A total of 371 different bacterial species were detected. 100 phylogenetically diverse unnamed species and 35 taxonomically diverse disease-associated species comprised an average 4.3% and 3.1% of the community, respectively, but reached up to 12.7% and 21.7% in some samples. Oral taxa formed numerous positive associations and clusters and were characterized by a high potential for metabolic interactions. The subspecies diversity was highly patient-specific and species-dependent, with 1427 ASVs identified in total. The unprecedented depth of early biofilm characterization in this study will support the development of individualized preventive and early diagnostic strategies.

Exploring interspecific interaction variability in microbiota: A review

Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.

Soil Bacteria in Archaeology: What Could Rank Abundance Functions Tell Us About Ancient Human Impacts on Microbial Communities?

Metagenomic analysis of soil bacterial communities based on 16S rRNA reflects a typical community composition containing a low number of high-abundance types and a very high number of low-abundance types. Here, the formation of characteristic rank order functions of bacterial abundance is investigated by modelling the dynamics of soil bacterial communities, assuming a succession of different bacterial populations that grow rapidly and decay more slowly. We found that the characteristic shape of typical rank order functions is well reflected by simulations. In addition, our model allowed us to investigate strong disturbances in the soil, which could be expected in cases of strongly changing local environmental conditions in soil, e.g., after translocation and covering of soil material. Such events could lead to the formation of shoulders in the rank order functions. Abundance rank orders observed in cases of some archaeological soil samples do indeed show such a shoulder and could be well interpreted by simulated rank order functions. As a result, it can be concluded that the investigations herein support our hypothesis that abundance rank orders contain information about the temporal order of developing bacterial types in changing communities and thus store information about local environmental conditions in the past, including ancient humans' impact on soil. This information can be used for interpretation of archeological findings and for reconstruction of different former human activities, as well as knowledge on the translocation of soil material in the past.

Effect of Helicobacter pylori on sleeve gastrectomy and gastric microbiome differences in patients with obesity and diabetes

BACKGROUND

Obesity and diabetes mellitus (DM) have become public health concerns worldwide. Both conditions have severe consequences and are associated with significant medical costs and productivity loss. Additionally, Helicobacter pylori infection may be a risk factor for the development of these conditions. However, whether eradicating H. pylori infection directly causes weight loss or improves insulin sensitivity is unknown.

METHODS

In this study, we confirmed the effect of sleeve gastrectomy according to the state of the gastric microbiota in 40 patients with obesity, DM, and H. pylori infection. Patients with obesity were divided into four groups: non-DM without H. pylori infection (ND), non-DM with H. pylori infection (ND-HP), DM, and DM with H. pylori infection (DM-HP) using 16S V3-V4 sequencing.

RESULTS

In the DM group, ALT, hemoglobin, HbA1c, blood glucose, and HSI significantly decreased, whereas high-density lipoprotein significantly increased. However, in the H. pylori-positive group, no significant difference was observed. The diversity of gastric microbiota decreased in the order of the ND > DM > ND-HP > DM-HP groups. We also conducted a correlation analysis between the preoperative microbes and clinical data. In the ND-HP group, most of the top 20 gastric microbiota were negatively correlated with glucose metabolism. However, H. pylori infection was positively correlated with pre-insulin levels.

CONCLUSION

Therefore, these findings indicate that patients with obesity and diabetes clearly benefit from surgery, but H. pylori infection may also affect clinical improvement.

Update on the pathogenesis of atopic dermatitis

Atopic dermatitis is a chronic, recurrent, and multifactorial skin-mucosal manifestation resulting from the interaction between elements mainly associated with the skin barrier deficit, the homeostasis of the immune response, neurological aspects, and patterns of reactivity to environmental antigens, which are established in genetically predisposed individuals. In addition to the skin, atopic diathesis involves other organs such as the airways (upper and lower), eyes, digestive tract, and neuropsychiatric aspects, which inflict additional morbidity on the dermatological patient. The different phenotypes of the disease fundamentally depend on the participation of each of these factors, in different life circumstances, such as age groups, occupational exposure patterns, physical activity, pollution, genetic load, and climatic factors. A better understanding of the complexity of its pathogenesis allows not only the understanding of therapeutic targets but also how to identify preponderant elements that mediate disease activity in each circumstance, for selecting the best treatment strategies and mitigation of triggering factors. This narrative review presents an update on the pathogenesis of atopic dermatitis, especially aimed at understanding the clinical manifestations, the main disease phenotypes and the context of available therapeutic strategies.

Dynamics of drinking water biofilm formation associated with Legionella spp. colonization

Understanding how Legionella spp. proliferate in multispecies biofilms is essential to develop strategies to control their presence in building plumbing. Here, we analyzed biofilm formation and Legionella spp. colonization on new plumbing material during 8 weeks. Biofilm formation was characterized by an initial increase in intact cell concentrations up to 9.5 × 105 cells/cm2, followed by a steady decrease. We identified Comamonas, Caulobacter, Schlegella, Blastomonas and Methyloversatilis as pioneer genera in the biofilm formation process. Importantly, L. pneumophila was the dominant Legionella spp. and rapidly colonized the biofilms, with culturable cell concentrations peaking at 3.1 × 104 MPN/cm2 after 4 weeks already. Moreover, several Legionella species co-occurred and had distinct dynamics of biofilm colonization. Vermamoeba vermiformis (V. vermiformis) was the dominant protist identified with 18S rRNA gene amplicon sequencing. Together our results highlight that biofilm formation upon introduction of new building plumbing material is a dynamic process where pathogenic Legionella species can be part of the earliest colonizers.

Non-invasive ventilation restores the gut microbiota in rats with acute heart failure

Heart failure (HF) is an increasingly prevalent disease in humans; it induces multiple symptoms and damages health. The animal gut microbiota has critical roles in host health, which might be related to HF symptoms. Currently, several options are used to treat HF, including non-invasive ventilation (NIV). However, studies on gut microbiota responses to acute HF and associated treatments effects on gut communities in patients are scarce. Here, short-term (1 week after treatments) and long-term (3 months after treatment) variations in gut microbiota variations in rats with acute HF treated were examined NIV through high-throughput sequencing of the bacterial 16S rRNA gene. Through comparison of gut microbiota alpha diversity, it was observed lower gut microbiota richness and diversity in animals with acute HF than in normal animals. Additionally, beta-diversity analysis revealed significant alterations in the gut microbiota composition induced by acute HF, as reflected by increased Firmicutes/Bacteroidetes (F/B) ratios and Proteobacteria enrichment. When network analysis results were combined with the null model, decreased stability and elevated deterministic gut microbiota assemblies were observed in animals with acute HF. Importantly, in both short- and long-term periods, NIV was found to restore gut microbiota dysbiosis to normal states in acute HF rats. Finally, it was shown that considerable gut microbiota variations existed in rats with acute HF, that underlying microbiota mechanisms regulated these changes, and confirmed that NIV is suitable for HF treatment. In future studies, these findings should be validated with different model systems or clinical samples.

MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques

BACKGROUND

In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes.

RESULTS

The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest.

CONCLUSIONS

The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

Seasonal and interannual variability of the free-living and particle-associated bacteria of a coastal microbiome

Marine microbial communities differ genetically, metabolically, and ecologically according to their lifestyle, and they may respond differently to environmental changes. In this study, we investigated the seasonal dynamics of bacterial assemblies in the free-living (FL) and particle-associated (PA) fractions across a span of 6 years in the Blanes Bay Microbial Observatory in the Northwestern Mediterranean. Both lifestyles showed marked seasonality. The trends in alpha diversity were similar, with lower values in spring-summer than in autumn-winter. Samples from both fractions were grouped seasonally and the percentage of community variability explained by the measured environmental variables was comparable (32% in FL and 31% in PA). Canonical analyses showed that biotic interactions were determinants of bacterioplankton dynamics and that their relevance varies depending on lifestyles. Time-decay curves confirmed a high degree of predictability in both fractions. Yet, 'seasonal' Amplicon Sequence Variants (ASVs) (as defined by Lomb Scargle time series analysis) in the PA communities represented 46% of the total relative abundance while these accounted for 30% in the FL fraction. These results demonstrate that bacteria inhabiting both fractions exhibit marked seasonality, highlighting the importance of accounting for both lifestyles to fully comprehend the dynamics of marine prokaryotic communities.

Non-invasive VOCs detection to monitor the gut microbiota metabolism in-vitro

This work implemented a non-invasive volatile organic compounds (VOCs) monitoring approach to study how food components are metabolised by the gut microbiota in-vitro. The fermentability of a model food matrix rich in dietary fibre (oat bran), and a pure prebiotic (inulin), added to a minimal gut medium was compared by looking at global changes in the volatilome. The substrates were incubated with a stabilised human faecal inoculum over a 24-h period, and VOCs were monitored without interfering with biological processes. The fermentation was performed in nitrogen-filled vials, with controlled temperature, and tracked by automated headspace-solid-phase microextraction coupled with gas chromatography-mass spectrometry. To understand the molecular patterns over time, we applied a multivariate longitudinal statistical framework: repeated measurements-ANOVA simultaneous component analysis. The methodology was able to discriminate the studied groups by looking at VOCs temporal profiles. The volatilome showed a time-dependency that was more distinct after 12 h. Short to medium-chain fatty acids showed increased peak intensities, mainly for oat bran and for inulin, but with different kinetics. At the same time, alcohols, aldehydes, and esters showed distinct trends with discriminatory power. The proposed approach can be applied to study the intertwined pathways of gut microbiota food components interaction in-vitro.

Network impact of a single-time-point microbial sample

The human microbiome plays a crucial role in determining our well-being and can significantly influence human health. The individualized nature of the microbiome may reveal host-specific information about the health state of the subject. In particular, the microbiome is an ecosystem shaped by a tangled network of species-species and host-species interactions. Thus, analysis of the ecological balance of microbial communities can provide insights into these underlying interrelations. However, traditional methods for network analysis require many samples, while in practice only a single-time-point microbial sample is available in clinical screening. Recently, a method for the analysis of a single-time-point sample, which evaluates its 'network impact' with respect to a reference cohort, has been applied to analyze microbial samples from women with Gestational Diabetes Mellitus. Here, we introduce different variations of the network impact approach and systematically study their performance using simulated 'samples' fabricated via the Generalized Lotka-Volttera model of ecological dynamics. We show that the network impact of a single sample captures the effect of the interactions between the species, and thus can be applied to anomaly detection of shuffled samples, which are 'normal' in terms of species abundance but 'abnormal' in terms of species-species interrelations. In addition, we demonstrate the use of the network impact in binary and multiclass classifications, where the reference cohorts have similar abundance profiles but different species-species interactions. Individualized analysis of the human microbiome has the potential to improve diagnosis and personalized treatments.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Imprints of ecological processes in the taxonomic core community: an analysis of naturally replicated microbial communities enclosed in oil

It is widely assumed that a taxonomic core community emerges among microbial communities from similar habitats because similar environments select for the same taxa bearing the same traits. Yet, a core community itself is no indicator of selection because it may also arise from dispersal and neutral drift, i.e. by chance. Here, we hypothesize that a core community produced by either selection or chance processes should be distinguishable. While dispersal and drift should produce core communities with similar relative taxon abundances, especially when the proportional core community, i.e. the sum of the relative abundances of the core taxa, is large, selection may produce variable relative abundances. We analyzed the core community of 16S rRNA gene sequences of 193 microbial communities occurring in tiny water droplets enclosed in heavy oil from the Pitch Lake, Trinidad and Tobago. These communities revealed highly variable relative abundances along with a large proportional core community (68.0 ± 19.9%). A dispersal-drift null model predicted a negative relationship of proportional core community and compositional variability along a range of dispersal probabilities and was largely inconsistent with the observed data, suggesting a major role of selection for shaping the water droplet communities in the Pitch Lake.

Critical role of the gut microbiota in immune responses and cancer immunotherapy

The gut microbiota plays a critical role in the progression of human diseases, especially cancer. In recent decades, there has been accumulating evidence of the connections between the gut microbiota and cancer immunotherapy. Therefore, understanding the functional role of the gut microbiota in regulating immune responses to cancer immunotherapy is crucial for developing precision medicine. In this review, we extract insights from state-of-the-art research to decipher the complicated crosstalk among the gut microbiota, the systemic immune system, and immunotherapy in the context of cancer. Additionally, as the gut microbiota can account for immune-related adverse events, we discuss potential interventions to minimize these adverse effects and discuss the clinical application of five microbiota-targeted strategies that precisely increase the efficacy of cancer immunotherapy. Finally, as the gut microbiota holds promising potential as a target for precision cancer immunotherapeutics, we summarize current challenges and provide a general outlook on future directions in this field.

Sediment DNA Records the Critical Transition of Bacterial Communities in the Arid Lake

It is necessary to predict the critical transition of lake ecosystems due to their abrupt, non-linear effects on social-economic systems. Given the promising application of paleolimnological archives to tracking the historical changes of lake ecosystems, it is speculated that they can also record the lake's critical transition. We studied Lake Dali-Nor in the arid region of Inner Mongolia because of the profound shrinking the lake experienced between the 1300 s and the 1600 s. We reconstructed the succession of bacterial communities from a 140-cm-long sediment core at 4-cm intervals and detected the critical transition. Our results showed that the historical trajectory of bacterial communities from the 1200 s to the 2010s was divided into two alternative states: state1 from 1200 to 1300 s and state2 from 1400 to 2010s. Furthermore, in the late 1300 s, the appearance of a tipping point and critical slowing down implied the existence of a critical transition. By using a multi-decadal time series from the sedimentary core, with general Lotka-Volterra model simulations, local stability analysis found that bacterial communities were the most unstable as they approached the critical transition, suggesting that the collapse of stability triggers the community shift from an equilibrium state to another state. Furthermore, the most unstable community harbored the strongest antagonistic and mutualistic interactions, which may imply the detrimental role of interaction strength on community stability. Collectively, our study showed that sediment DNA can be used to detect the critical transition of lake ecosystems.

Soil prokaryotic and fungal biome structures associated with crop disease status across the Japan Archipelago

Archaea, bacteria, and fungi in the soil are increasingly recognized as determinants of agricultural productivity and sustainability. A crucial step for exploring soil microbiomes with important ecosystem functions is to perform statistical analyses on the potential relationship between microbiome structure and functions based on comparisons of hundreds or thousands of environmental samples collected across broad geographic ranges. In this study, we integrated agricultural field metadata with microbial community analyses by targeting 2,903 bulk soil samples collected along a latitudinal gradient from cool-temperate to subtropical regions in Japan (26.1-42.8 °N). The data involving 632 archaeal, 26,868 bacterial, and 4,889 fungal operational taxonomic units detected across the fields of 19 crop plant species allowed us to conduct statistical analyses (permutational analyses of variance, generalized linear mixed models, randomization analyses, and network analyses) on the relationship among edaphic factors, microbiome compositions, and crop disease prevalence. We then examined whether the diverse microbes form species sets varying in potential ecological impacts on crop plants. A network analysis suggested that the observed prokaryotes and fungi were classified into several species sets (network modules), which differed substantially in association with crop disease prevalence. Within the network of microbe-to-microbe coexistence, ecologically diverse microbes, such as an ammonium-oxidizing archaeon, an antibiotics-producing bacterium, and a potentially mycoparasitic fungus, were inferred to play key roles in shifts between crop-disease-promotive and crop-disease-suppressive states of soil microbiomes. The bird's-eye view of soil microbiome structure will provide a basis for designing and managing agroecosystems with high disease-suppressive functions.IMPORTANCEUnderstanding how microbiome structure and functions are organized in soil ecosystems is one of the major challenges in both basic ecology and applied microbiology. Given the ongoing worldwide degradation of agroecosystems, building frameworks for exploring structural diversity and functional profiles of soil microbiomes is an essential task. Our study provides an overview of cropland microbiome states in light of potential crop-disease-suppressive functions. The large data set allowed us to explore highly functional species sets that may be stably managed in agroecosystems. Furthermore, an analysis of network architecture highlighted species that are potentially used to cause shifts from disease-prevalent states of agroecosystems to disease-suppressive states. By extending the approach of comparative analyses toward broader geographic ranges and diverse agricultural practices, agroecosystem with maximized biological functions will be further explored.

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

Progress on network modeling and analysis of gut microecology: a review

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

Temporal profiling of sediment microbial communities in the Three Gorges Reservoir Area discovered time-dissimilarity patterns and multiple stable states

Microbial communities play vital roles in cycling nutrients and maintaining water quality in aquatic ecosystems. To better understand the dynamics of microbial communities and to pave way to effective ecological remediation, it's essential to reveal the temporal patterns of the communities and to identify their states. However, research exploring the dynamic changes of microbial communities needs a large amount of time-series data, which could be an extravagant requirement for a single study. In this research, we overcame this challenge by conducting a meta-analysis of years of accumulations of 16S rRNA high-throughput sequencing data from the Three Gorges Reservoir Area (TGRA), an ecological and environmental hotspot. For better understanding the microbial communities time-dissimilarity dynamics, three microbial communities time-dissimilarity patterns were hypothesized, and the linear pattern in the TGRA was validated. In addition, to explore the stability of microbial communities in the TGRA, two alternative stable states were revealed, and their differences in community richness, alpha diversity indices, community composition, ecological network topological properties, and metabolic functions were demonstrated. In short, two states of microbial communities showed distinct richness and alpha diversity indices, and the communities in one state were more dominated by Halomonas and Nitrosopumilaceae genera, facilitating nitrogen cycling metabolic processes; whilst the main genera of the other state were Bathyarchaeia and Methanosaeta, which favored methane-related metabolism. Moreover, different studies and environmental differences between mainstream and tributaries were attributed as the potential inducing factors of the state division. Our study provides a comprehensive insight into the dynamics and stability of microbial communities in the TGRA, and a reference for future studies on microbial community dynamics.

The road forward to incorporate seawater microbes in predictive reef monitoring

Marine bacterioplankton underpin the health and function of coral reefs and respond in a rapid and sensitive manner to environmental changes that affect reef ecosystem stability. Numerous meta-omics surveys over recent years have documented persistent associations of opportunistic seawater microbial taxa, and their associated functions, with metrics of environmental stress and poor reef health (e.g. elevated temperature, nutrient loads and macroalgae cover). Through positive feedback mechanisms, disturbance-triggered heterotrophic activity of seawater microbes is hypothesised to drive keystone benthic organisms towards the limit of their resilience and translate into shifts in biogeochemical cycles which influence marine food webs, ultimately affecting entire reef ecosystems. However, despite nearly two decades of work in this space, a major limitation to using seawater microbes in reef monitoring is a lack of a unified and focused approach that would move beyond the indicator discovery phase and towards the development of rapid microbial indicator assays for (near) real-time reef management and decision-making. By reviewing the current state of knowledge, we provide a comprehensive framework (defined as five phases of research and innovation) to catalyse a shift from fundamental to applied research, allowing us to move from descriptive to predictive reef monitoring, and from reactive to proactive reef management.

Internal carbon recycling by heterotrophic prokaryotes compensates for mismatches between phytoplankton production and heterotrophic consumption

Molecular observational tools are useful for characterizing the composition and genetic endowment of microbial communities but cannot measure fluxes, which are critical for the understanding of ecosystems. To overcome these limitations, we used a mechanistic inference approach to estimate dissolved organic carbon (DOC) production and consumption by phytoplankton operational taxonomic units and heterotrophic prokaryotic amplicon sequence variants and inferred carbon fluxes between members of this microbial community from Western English Channel time-series data. Our analyses focused on phytoplankton spring and summer blooms, as well as bacteria summer blooms. In spring blooms, phytoplankton DOC production exceeds heterotrophic prokaryotic consumption, but in bacterial summer blooms heterotrophic prokaryotes consume three times more DOC than produced by the phytoplankton. This mismatch is compensated by heterotrophic prokaryotic DOC release by death, presumably from viral lysis. In both types of summer blooms, large amounts of the DOC liberated by heterotrophic prokaryotes are reused through internal recycling, with fluxes between different heterotrophic prokaryotes being at the same level as those between phytoplankton and heterotrophic prokaryotes. In context, internal recycling accounts for approximately 75% and 30% of the estimated net primary production (0.16 vs 0.22 and 0.08 vs 0.29 μmol l-1 d-1) in bacteria and phytoplankton summer blooms, respectively, and thus represents a major component of the Western English Channel carbon cycle. We have concluded that internal recycling compensates for mismatches between phytoplankton DOC production and heterotrophic prokaryotic consumption, and we encourage future analyses on aquatic carbon cycles to investigate fluxes between heterotrophic prokaryotes, specifically internal recycling.

The mechanism of microbial community succession and microbial co-occurrence network in soil with compost application

The application of organic and chemical fertilizer into soil can regulate microbial communities. However, the response mechanism of microbial communities in soil to compost and chemical fertilizer application remain unclear. In this study, compost made of tobacco leaves individually and combined with chemical fertilizer was applied, respectively, to investigate their effect on soil microorganisms during the pot-culture process. High-throughput sequence, neutral community model and null model were employed to clarify how soil microbial community respond to the application of compost and chemical fertilizer. Furthermore, random forest model was applied to predict the relationships between the plant agronomical traits and the soil microorganism during the pot-culture process. The results demonstrated that the simultaneous application of compost and chemical fertilizer increased significantly the richness and diversity of the microorganisms in soil (p < 0.05), groups C and D led to a significant reduction in the number of nodes and edges in the microbial network (77.78 %-96.57 %). The dominant bacteria in the application of 50 g fertilizer accounted for the highest proportion (40 %) and organic matter was the main factors driving the change in bacterial communities. Compared to the tilled soil, the microbial communities of the soil with the simultaneous application of compost and chemical fertilizer were more susceptible to stochastic processes, and soil microorganisms had less influence on the growth of crops during pot-culture. In conclusion, the simultaneous application of compost and fertilizer altered the ecological functions of soil microbial communities, leading to an enhanced stochastic process of community formation.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.

Recent advances in understanding the role of the skin microbiome in the treatment of atopic dermatitis

The skin is the largest organ in the human body, and histologically consists of the epidermis, dermis and subcutaneous tissue. Humans maintain a cooperative symbiotic relationship with their skin microbiota, a complex community of bacteria, fungi and viruses that live on the surface of the skin, and which act as a barrier to protect the body from the inside and outside. The skin is a 'habitat' and vast 'ecosystem' inhabited by countless microbes; as such, relationships have been forged through millions of years of coevolution. It is not surprising then that microbes are key participants in shaping and maintaining essential physiological processes. In addition to maintaining barrier function, the unique symbiotic microbiota that colonizes the skin increases the immune response and provides protection against pathogenic microbes. This review examines our current understanding of skin microbes in shaping and enhancing the skin barrier, as well as skin microbiome-host interactions and their roles in skin diseases, such as atopic dermatitis (AD). We also report on the current status of AD therapeutic drugs that target the skin microbiome, related research on current therapeutic strategies, and the limitations and future considerations of skin microbiome research. In particular, as a future strategy, we discuss the need for a skin-on-a-chip-based microphysiological system research model amenable to biomimetic in vitro studies and human skin equivalent models, including skin appendages.

Functional Redundancy Secures Resilience of Chain Elongation Communities upon pH Shifts in Closed Bioreactor Ecosystems

For anaerobic mixed cultures performing microbial chain elongation, it is unclear how pH alterations affect the abundance of key players, microbial interactions, and community functioning in terms of medium-chain carboxylate yields. We explored pH effects on mixed cultures enriched in continuous anaerobic bioreactors representing closed model ecosystems. Gradual pH increase from 5.5 to 6.5 induced dramatic shifts in community composition, whereas product range and yields returned to previous states after transient fluctuations. To understand community responses to pH perturbations over long-term reactor operation, we applied Aitchison PCA clustering, linear mixed-effects models, and random forest classification on 16S rRNA gene amplicon sequencing and process data. Different pH preferences of two key chain elongation species─one Clostridium IV species related to Ruminococcaceae bacterium CPB6 and one Clostridium sensu stricto species related to Clostridium luticellarii─were determined. Network analysis revealed positive correlations of Clostridium IV with lactic acid bacteria, which switched from Olsenella to Lactobacillus along the pH increase, illustrating the plasticity of the food web in chain elongation communities. Despite long-term cultivation in closed systems over the pH shift experiment, the communities retained functional redundancy in fermentation pathways, reflected by the emergence of rare species and concomitant recovery of chain elongation functions.

Exploring the Entropy Complex Networks with Latent Interaction

In the present work, we study the introduction of a latent interaction index, examining its impact on the formation and development of complex networks. This index takes into account both observed and unobserved heterogeneity per node in order to overcome the limitations of traditional compositional similarity indices, particularly when dealing with large networks comprising numerous nodes. In this way, it effectively captures specific information about participating nodes while mitigating estimation problems based on network structures. Furthermore, we develop a Shannon-type entropy function to characterize the density of networks and establish optimal bounds for this estimation by leveraging the network topology. Additionally, we demonstrate some asymptotic properties of pointwise estimation using this function. Through this approach, we analyze the compositional structural dynamics, providing valuable insights into the complex interactions within the network. Our proposed method offers a promising tool for studying and understanding the intricate relationships within complex networks and their implications under parameter specification. We perform simulations and comparisons with the formation of Erdös-Rényi and Barabási-Alber-type networks and Erdös-Rényi and Shannon-type entropy. Finally, we apply our models to the detection of microbial communities.

Gut microbial stability in older Japanese populations: insights from the Mykinso cohort

Gut microbiota imbalance plays an important role in the pathogenesis of various diseases. Here, we determined microbe-microbe interactions and gut microbiome stability in a Japanese population with varying body mass indices (BMIs) and enterotypes. Using 16S ribosomal RNA gene sequencing, we analyzed gut microbial data from fecal samples obtained from 3,365 older Japanese individuals. The individuals were divided into lean, normal, and obese groups based on their BMIs. They were further categorized according to their gut microbiota enterotypes: Bacteroides (enterotype B), Prevotella (enterotype P), and Ruminococcus (enterotype R). We obtained data on different host factors, such as age, BMI, and disease status, using a survey questionnaire evaluated by the Mykinso gut microbiome testing service. Subsequently, we evaluated the co-occurrence network. Individual differences in BMI were associated with differences in co-occurrence networks. By exploring the network topology based on BMI status, we observed that the network density was lower in the lean group than that in the normal group. Furthermore, a simulation-based stability analysis revealed a lower resistance index in the lean group than those in the other two groups. Our results provide insights into various microbe-microbe interactions and gut microbial stability and could aid in developing appropriate therapeutic strategies targeting gut microbiota modulation to manage frailty.

Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment

Gut microbiota plays a key role in host health under normal conditions. However, bacterial composition can be altered by external factors such as antibiotic (AB) intake. While there are many descriptive publications about the effects of AB on gut microbiota composition after treatment, the dynamics and interactions among the bacterial taxa are still poorly understood. In this work, we performed a longitudinal study of gut microbiome dynamics in B. germanica treated with kanamycin. The AB was supplied in three separate periods, giving the microbiota time to recover between each antibiotic intake. We applied two new statistical models, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa and how some groups affect a single taxon. The first model provides information on the importance of a given genus, and the rest of the community, to define the abundance of that genus. The second model, on the other hand, provides details about the relationship between groups of bacteria, focusing on which community groups affect the taxa. These models help us to identify which bacteria are community-dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic. In addition, these models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions, suggesting that when the environment is more hostile (as it is under antibiotic treatment), the whole community tends to work together.

The collapse and re-establishment of stability regulate the gradual transition of bacterial communities from macrophytes- to phytoplankton-dominated types in a large eutrophic lake

Eutrophic lakes often exhibit two alternative types: macrophytes-dominated (MD) and phytoplankton-dominated (PD). However, the nature of bacterial community types that whether the transition from the MD to the PD types occurs in a gradual or abrupt manner remains hotly debated. Further, the theoretical recognition that stability regulates the transition of bacterial community types remains qualitative. To address these issues, we divided the transition of bacterial communities along a trophic gradient into 12 successional stages, ranging from the MD to the PD types. Results showed that 12 states were clustered into three distinct regimes: MD type, intermediate transitional type and PD type. Bacterial communities were not different between consecutive stages, suggesting that the transition of alternative types occurs in a continuous gradient. At the same time, the stability of bacterial communities was significantly lower in the intermediate type than in the MD or PD types, highlighting that the collapse and re-establishment of community stability regulate the transition. Further, our results showed that the high complexity of taxon interactions and strong stochastic processes disrupt the stability. Ultimately, this study enables deeper insights into understanding the alternative types of microbial communities in the view of community stability.

Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

Assessing mechanisms for microbial taxa and community dynamics using process models

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer-resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer-resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.

Contrasting response of microeukaryotic and bacterial communities to the interplay of seasonality and local stressors in shallow soda lakes

Seasonal environmental variation is a leading driver of microbial planktonic community assembly and interactions. However, departures from usual seasonal trends are often reported. To understand the role of local stressors in modifying seasonal succession, we sampled fortnightly, throughout three seasons, five nearby shallow soda lakes exposed to identical seasonal and meteorological changes. We characterised their microeukaryotic and bacterial communities by amplicon sequencing of the 16S and 18S rRNA gene, respectively. Biological interactions were inferred by analyses of synchronous and time-shifted interaction networks, and the keystone taxa of the communities were topologically identified. The lakes showed similar succession patterns during the study period with spring being characterised by the relevance of trophic interactions and a certain level of community stability followed by a more dynamic and variable summer-autumn period. Adaptation to general seasonal changes happened through shared core microbiome of the lakes. Stochastic events such as desiccation disrupted common network attributes and introduced shifts from the prevalent seasonal trajectory. Our results demonstrated that, despite being extreme and highly variable habitats, shallow soda lakes exhibit certain similarities in the seasonality of their planktonic communities, yet local stressors such as droughts instigate deviations from prevalent trends to a greater extent for microeukaryotic than for bacterial communities.

The Vaginal Microbiota of Pregnant Women Varies with Gestational Age, Maternal Age, and Parity

The composition of the vaginal microbiota is heavily influenced by pregnancy and may factor into pregnancy complications, including spontaneous preterm birth. However, results among studies have been inconsistent due, in part, to variation in sample sizes and ethnicity. Thus, an association between the vaginal microbiota and preterm labor continues to be debated. Yet, before assessing associations between the composition of the vaginal microbiota and preterm labor, a robust and in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required. Here, we report a large longitudinal study (n = 474 women, 1,862 vaginal samples) of a predominantly African-American cohort-a population that experiences a relatively high rate of pregnancy complications-evaluating associations between individual identity, gestational age, and other maternal characteristics with the composition of the vaginal microbiota throughout gestation resulting in term delivery. The principal factors influencing the composition of the vaginal microbiota in pregnancy are individual identity and gestational age at sampling. Other factors are maternal age, parity, obesity, and self-reported Cannabis use. The general pattern across gestation is for the vaginal microbiota to remain or transition to a state of Lactobacillus dominance. This pattern can be modified by maternal parity and obesity. Regardless, network analyses reveal dynamic associations among specific bacterial taxa within the vaginal ecosystem, which shift throughout the course of pregnancy. This study provides a robust foundational understanding of the vaginal microbiota in pregnancy and sets the stage for further investigation of this microbiota in obstetrical disease. IMPORTANCE There is debate regarding links between the vaginal microbiota and pregnancy complications, especially spontaneous preterm birth. Inconsistencies in results among studies are likely due to differences in sample sizes and cohort ethnicity. Ethnicity is a complicating factor because, although all bacterial taxa commonly inhabiting the vagina are present among all ethnicities, the frequencies of these taxa vary among ethnicities. Therefore, an in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required prior to evaluating associations between the vaginal microbiota and obstetrical disease. This initial investigation is a large longitudinal study of the vaginal microbiota throughout gestation resulting in a term delivery in a predominantly African-American cohort, a population that experiences disproportionally negative maternal-fetal health outcomes. It establishes the magnitude of associations between maternal characteristics, such as age, parity, body mass index, and self-reported Cannabis use, on the vaginal microbiota in pregnancy.

Emergent community architecture despite distinct diversity in the global whale shark (Rhincodon typus) epidermal microbiome

Microbiomes confer beneficial physiological traits to their host, but microbial diversity is inherently variable, challenging the relationship between microbes and their contribution to host health. Here, we compare the diversity and architectural complexity of the epidermal microbiome from 74 individual whale sharks (Rhincodon typus) across five aggregations globally to determine if network properties may be more indicative of the microbiome-host relationship. On the premise that microbes are expected to exhibit biogeographic patterns globally and that distantly related microbial groups can perform similar functions, we hypothesized that microbiome co-occurrence patterns would occur independently of diversity trends and that keystone microbes would vary across locations. We found that whale shark aggregation was the most important factor in discriminating taxonomic diversity patterns. Further, microbiome network architecture was similar across all aggregations, with degree distributions matching Erdos-Renyi-type networks. The microbiome-derived networks, however, display modularity indicating a definitive microbiome structure on the epidermis of whale sharks. In addition, whale sharks hosted 35 high-quality metagenome assembled genomes (MAGs) of which 25 were present from all sample locations, termed the abundant 'core'. Two main MAG groups formed, defined here as Ecogroup 1 and 2, based on the number of genes present in metabolic pathways, suggesting there are at least two important metabolic niches within the whale shark microbiome. Therefore, while variability in microbiome diversity is high, network structure and core taxa are inherent characteristics of the epidermal microbiome in whale sharks. We suggest the host-microbiome and microbe-microbe interactions that drive the self-assembly of the microbiome help support a functionally redundant abundant core and that network characteristics should be considered when linking microbiomes with host health.

Colonization-persistence trade-offs in natural bacterial communities

Fitness equalizing mechanisms, such as trade-offs, are recognized as one of the main factors promoting species coexistence in community ecology. However, they have rarely been explored in microbial communities. Although microbial communities are highly diverse, the coexistence of their multiple taxa is largely attributed to niche differences and high dispersal rates, following the principle 'everything is everywhere, but the environment selects'. We use a dynamical stochastic model based on the theory of island biogeography to study highly diverse bacterial communities over time across three different systems (soils, alpine lakes and shallow saline lakes). Assuming fitness equalization mechanisms, here we newly analytically derive colonization-persistence trade-offs, and report a signal of such trade-offs in natural bacterial communities. Moreover, we show that different subsets of species in the community drive this trade-off. Rare taxa, which are occasional and more likely to follow independent colonization/extinction dynamics, drive this trade-off in the aquatic communities, while the core sub-community did it in the soils. We conclude that equalizing mechanisms may be more important than previously recognized in bacterial communities. Our work also emphasizes the fundamental value of dynamical models for understanding temporal patterns and processes in highly diverse communities.

Modeling bacteria pairwise interactions in human microbiota by Sparse Identification of Nonlinear Dynamics (SINDy). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083503 DOI: 10.1109/embc40787.2023.10341078]

The gut microbiota is a community of high complexity; its composition changes due to ecological interactions, these are studied to understand the relationship with the human health. External stimuli like the administration of probiotics, prebiotics, or drugs are known to modify these interactions. The high complexity of microbiota composition can be studied by considering pairwise interactions. Pairwise interactions in bacterial communities consider each species' directionality and impact on one another, e.g., commensalism (unidirectional positive interaction) or competition (bidirectional negative interaction). These interactions can either be interspecies or intraspecies. The Lotka-Volterra (LV) model has been implemented to characterize these bacteria interactions, considering the ecological relationship among the different species presented. One of the main challenges is determining the specific interaction parameters in LV structure from experimental data. This study implemented a novel approach based on the sparse identification of nonlinear dynamic method (SINDy). One of the assumptions in SINDy method implies the knowledge of the data derivative structure. To fulfill this requirement, a differential neural network algorithm was implemented. We assessed the performance of this approach considering both a simulated and experimental interspecies scenario. A two-species bacterial LV model was simulated in the initial validation stage, and the resulting kinetic growth data was recorded. This data was utilized for training a differential neural network algorithm, which was used to derive a time-derivative structure for the dataset. After this step, SINDy method was implemented to calculate the interaction parameters. Three conditions were evaluated in intraspecies competition, obtaining an average identification parametric error of less than 2%. For experimental data, parametric analysis results are sensitive to detect the influence of a drug presence over the intraspecies interaction with a reduction of 50% in its typical values.Clinical Relevance- In this study, we devised a strategy to determine how two species of the human gastrointestinal microbiota interact and the impact of drug administration on these interactions.

Evolutionary implications of host genetic control for engineering beneficial microbiomes

Engineering new functions in the microbiome requires understanding how host genetic control and microbe-microbe interactions shape the microbiome. One key genetic mechanism underlying host control is the immune system. The immune system can promote stability in the composition of the microbiome by reshaping the ecological dynamics of its members, but the degree of stability will depend on the interplay between ecological context, immune system development, and higher-order microbe-microbe interactions. The eco-evolutionary interplay affecting composition and stability should inform the strategies used to engineer new functions in the microbiome. We conclude with recent methodological developments that provide an important path forward for both engineering new functionality in the microbiome and broadly understanding how ecological interactions shape evolutionary processes in complex biological systems.

Improving the assessment of ecosystem and wildlife health: microbiome as an early indicator

Human activities are causing dramatic declines in ecosystem health, compromising the functioning of the life-support system, economic activity, and animal and human health. In this context, monitoring the health of ecosystems and wildlife populations is crucial for determining ecological dynamics and assessing management interventions. A growing body of evidence indicates that microbiome provides a meaningful early indicator of ecosystem and wildlife health. Microbiome is ubiquitous and both environmental and host-associated microbiomes rapidly reflect anthropogenic disturbances. However, we still need to overcome current limitations such as nucleic acid degradation, sequencing depth, and the establishment of baseline data to maximize the potential of microbiome studies.

Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.