Hasil untuk "Microbial ecology"

F. Chapelle

Muhammad Saleem, Jie Hu, A. Jousset

Microorganisms drive several processes needed for robust plant growth and health. Harnessing microbial functions is thus key to productive and sustainable food production. Molecular methods have led to a greater understanding of the soil microbiome composition. However, translating species or gene composition into microbiome functionality remains a challenge. Community ecology concepts such as the biodiversity–ecosystem functioning framework may help predict the assembly and function of plant-associated soil microbiomes. Higher diversity can increase the number and resilience of plant-beneficial functions that can be coexpressed and unlock the expression of plant-beneficial traits that are hard to obtain from any species in isolation. We combine well-established community ecology concepts with molecular microbiology into a workable framework that may enable us to predict and enhance soil microbiome functionality to promote robust plant growth in a global change context.

S. Biller, P. Berube, D. Lindell et al.

F. Ju, Tong Zhang

Archana Pant, T. Maiti, Dinesh Mahajan et al.

The efficacy of drugs widely varies in individuals, and the gut microbiota plays an important role in this variability. The commensal microbiota living in the human gut encodes several enzymes that chemically modify systemic and orally administered drugs, and such modifications can lead to activation, inactivation, toxification, altered stability, poor bioavailability, and rapid excretion. Our knowledge of the role of the human gut microbiome in therapeutic outcomes continues to evolve. Recent studies suggest the existence of complex interactions between microbial functions and therapeutic drugs across the human body. Therapeutic drugs or xenobiotics can influence the composition of the gut microbiome and the microbial encoded functions. Both these deviations can alter the chemical transformations of the drugs and hence treatment outcomes. In this review, we provide an overview of (i) the genetic ecology of microbially encoded functions linked with xenobiotic degradation; (ii) the effect of drugs on the composition and function of the gut microbiome; and (iii) the importance of the gut microbiota in drug metabolism.

Kartik Baruah, Dipesh Debnath, Dipesh Debnath et al.

IntroductionBacteria belonging to the Harveyi clade of vibrios, including Vibrio campbellii infect many wild and cultured aquatic organisms and cause major losses in global aquaculture. Although not a primary human pathogen, V. campbellii can act opportunistically, particularly following exposure to marine environments or seafood, and in immunocompromised individuals, highlighting the links between aquatic ecosystems, food production systems, and human health. Carvacrol, a phenolic monoterpenoid found in oregano and thyme essential oils, is approved for use in human and animal food. Beyond its safety profile, this compound has also been reported to possess diverse pharmacological effects, including anticarcinogenic, anti-inflammatory and antimicrobial.MethodsUsing gnotobiotic brine shrimp Artemia as an in vivo model, we examined whether carvacrol can provide dual protection against V. campbellii by inhibiting the production of bacterial virulence and modulating host immune responses.Results and discussionCarvacrol significantly improved the survival of Artemia during V. campbellii challenge while showing low toxicity at effective concentrations. The protection was associated with reduced bacterial virulence, including decreased biofilm formation and lower hemolytic and caseinase activities. Additionally, carvacrol modulated the expression of defence-related genes (hsp70, prophenoloxidase, transglutaminase, and ferritin) in time-dependent and stochastic patterns, rather than sustained upregulation. Overall, these findings suggest that carvacrol enhances disease resistance through both pathogen- and host-directed mechanisms. Given its food-grade safety status, carvacrol holds strong translational potential as a functional antimicrobial strategy to support disease control and health management in aquaculture, warranting further evaluation under realistic farming conditions.

Julius E. Brinck, Martin F. Laursen, Mikael Pedersen et al.

Abstract Intestinal pH influences microbiota composition and activity, yet its impact on microbial metabolite production remains elusive. Gut bacterial tryptophan catabolism yields metabolites with opposing health effects. Indole, a precursor of indoxyl sulfate (IS), is linked to chronic kidney disease (CKD), while indolelactic acid (ILA) and indolepropionic acid (IPA) have positive health effects. Analysis of fecal pH and tryptophan metabolites in two human cohorts revealed positive correlations between fecal pH, indole, and urinary IS, and negative correlations with ILA and IPA. Fecal indole and pH showed no correlation with fecal tryptophanase (tnaA) gene abundance. In vitro fermentations showed that low pH (5.5) inhibited indole production by E. coli, enhancing tryptophan availability for C. sporogenes to produce beneficial metabolites. Human fecal cultures confirmed pH-dependent tnaA gene repression and indole suppression. These findings highlight the role of pH as a key regulator of gut bacterial tryptophan metabolism with therapeutic relevance for CKD.

Yining Xie, Yangji Cidan, Zhuoma Cisang et al.

ABSTRACT Yaks (Bos grunniens), native to the Qinghai-Tibet Plateau, have evolved extraordinary physiological resilience to chronic hypoxia, cold, and nutritional scarcity. However, the integrated metabolic and microbial mechanisms underlying these adaptations remain poorly defined. Here, a comprehensive multi-omics analysis was performed on thirty grazing heifer yaks (2.5 years old) from three altitudes—3,600 m (low altitude [LA]), 4,000 m (middle altitude [MA]), and 4,500 m (high altitude [HA])—to investigate how altitude affects host physiology, metabolism, and gut microbial ecology. Increasing altitude significantly reduced serum total protein, globulin, blood urea nitrogen, and alkaline phosphatase, indicating suppressed anabolic metabolism and nitrogen-sparing strategies. Antioxidant capacity (total superoxide dismutase, total antioxidant capacity) and pro-inflammatory cytokines (interleukin-2 [IL-2], IL-6, tumor necrosis factor-α, interferon-γ) increased (P < 0.05), while glutathione peroxidase, IL-4, IL-10, growth hormone, insulin-like growth factor-1, and growth hormone-releasing hormone declined (P < 0.05), reflecting energy reallocation from growth toward antioxidation and immune maintenance under hypoxia. Plasma metabolomics revealed distinct altitude-dependent reprogramming, with enrichment of retinol metabolism at 4,000 m and α-linolenic acid metabolism, tricarboxylic acid (TCA) cycle, and branched-chain amino acid biosynthesis at 4,500 m. These pathways link lipid remodeling, oxidative balance, and oxygen utilization. The gut microbiota displayed altitude-specific shifts, characterized by enrichment of Christensenellaceae_R-7_group and Monoglobus and reduced UCG-005 and Rikenellaceae_RC9_gut_group, accompanied by lower fecal volatile fatty acids (P < 0.05). Correlation analyses confirmed tight associations between fermentative taxa and volatile fatty acids production. Collectively, our results establish a serum–metabolome–microbiota axis as a central mechanism supporting yak adaptation to high altitude.IMPORTANCEThis study demonstrates that the gut microbiota plays a crucial role in how yaks adapt to high-altitude hypoxia. Rising altitude not only alters the composition of gut microbes but also shifts their metabolic activity toward improving fermentation efficiency and antioxidant capacity. These microbial changes are closely linked with host metabolism, forming a coordinated serum–metabolome–microbiota network that helps maintain energy balance and immune stability when oxygen is limited. The enrichment of retinol and α-linolenic acid metabolism as altitude-responsive pathways further highlights the metabolic interplay between host and microbes in supporting physiological resilience. Overall, our findings show that microbial flexibility and metabolic cooperation are key factors enabling ruminants to survive in extreme environments, providing a scientific basis for microbiome-informed strategies to enhance yak health and productivity on the Qinghai-Tibet Plateau.

Anqi Wang, Yue Hua, Xinyue Zhang et al.

Hyper-learning and Unlearning is a speculative animation that reflect how learning is reconfigured within digital media ecologies. Using architectural education as a microcosm, the work reframes the city as a hyper-learning apparatus where urban space, algorithmic systems, and platform infrastructures condition cognition and agency. By staging both hyper-learning and the unlearning induced by machine-supported cognition, the work critiques institutional gatekeeping while revealing how platforms reshape expertise, memory, and spatial experience. This project invites viewers to reconsider how urban space becomes pedagogical infrastructure in a posthumanism era.

J. Gilbert, Brent E. Stephens

Ruiwen Hu, Hai-Ming Zhao, Xihui Xu et al.

The extensive use of phthalic acid esters (PAEs) has led to their widespread distribution across various environments. As PAEs pose significant threats to human health, it is urgent to develop efficient strategies to eliminate them from environments. Bacteria-driven PAE biodegradation has been considered as an inexpensive yet effective strategy to restore the contaminated environments. Despite great advances in bacterial culturing and sequencing, the inherent complexity of indigenous microbial community hinders us to mechanistically understand in situ PAE biodegradation and efficiently harness the degrading power of bacteria. The synthetic microbial ecology provides us a simple and controllable model system to address this problem. In this review, we focus on the current progress of PAE biodegradation mediated by bacterial isolates and indigenous bacterial communities, and discuss the prospective of synthetic PAE-degrading bacterial communities in PAE biodegradation research. It is anticipated that the theories and approaches of synthetic microbial ecology will revolutionize the study of bacteria-driven PAE biodegradation and provide novel insights for developing effective bioremediation solutions.

Carly R. Muletz-Wolz, Julian Urrutia-Carter, Owen Osborne et al.

Abstract Using multi-omics tools, we discovered new antimicrobial peptides (AMPs) and examined AMP-microbial interactions in three Appalachian salamander species (Plethodon cinereus, Eurycea bislineata and Notophthalmus viridescens). We conducted skin transcriptomics (n = 13) and proteomics (n = 91) to identify 200+ candidate AMPs. With candidate AMPs, we identified correlations with skin microbiomes and synthesized 20 peptides to challenge against pathogens of amphibians (Batrachochytrium dendrobatidis: Bd) and humans (ESKAPEE). Using transcriptomics, candidate AMPs were detected in all individuals with Cathelidicins being most common. Using proteomics, AMPs were found in 34% of individuals (31/91)—predominately E. bislineata—with Kinin-like peptides being most common. Candidate AMP composition generally predicted skin bacterial composition, suggesting that AMPs influence host-microbial symbioses. Crude and synthesized peptides showed limited activity against Bd. Two synthesized Cathelicidins (Pcin-CATH3 and Pcin-CATH5) inhibited human pathogens, Acinetobacter baumannii, Pseudomonas aeruginosa and Escherichia coli. Our findings inform the potential usage of AMPs in conservation and translational applications.

Anahí Rodríguez-Martínez, Silvia Bartolucci, Francesco Caravelli et al.

Understanding how credit flows through inter-firm networks is critical for assessing financial stability and systemic risk. In this study, we introduce DebtStreamness, a novel metric inspired by trophic levels in ecological food webs, to quantify the position of firms within credit chains. By viewing credit as the ``primary energy source'' of the economy, we measure how far credit travels through inter-firm relationships before reaching its final borrowers. Applying this framework to Uruguay's inter-firm credit network, using survey data from the Central Bank, we find that credit chains are generally short, with a tiered structure in which some firms act as intermediaries, lending to others further along the chain. We also find that local network motifs such as loops can substantially increase a firm's DebtStreamness, even when its direct borrowing from banks remains the same. Comparing our results with standard economic classifications based on input-output linkages, we find that DebtStreamness captures distinct financial structures not visible through production data. We further validate our approach using two maximum-entropy network reconstruction methods, demonstrating the robustness of DebtStreamness in capturing systemic credit structures. These results suggest that DebtStreamness offers a complementary ecological perspective on systemic credit risk and highlights the role of hidden financial intermediation in firm networks.

Jennifer Shi, Christopher K. Frantz, Christian Kimmich et al.

Designing institutions for social-ecological systems requires models that capture heterogeneity, uncertainty, and strategic interaction. Multiple modeling approaches have emerged to meet this challenge, including empirical game-theoretic analysis (EGTA), which merges ABM's scale and diversity with game-theoretic models' formal equilibrium analysis. The newly popular class of LLM-driven simulations provides yet another approach, and it is not clear how these approaches can be integrated with one another, nor whether the resulting simulations produce a plausible range of behaviours for real-world social-ecological governance. To address this gap, we compare four LLM-augmented frameworks: procedural ABMs, generative ABMs, LLM-EGTA, and expert guided LLM-EGTA, and evaluate them on a real-world case study of irrigation and fishing in the Amu Darya basin under centralized and decentralized governance. Our results show: first, procedural ABMs, generative ABMs, and LLM-augmented EGTA models produce strikingly different patterns of collective behaviour, highlighting the value of methodological diversity. Second, inducing behaviour through system prompts in LLMs is less effective than shaping behaviour through parameterized payoffs in an expert-guided EGTA-based model.

Shing Yan Li, Mehran Kardar, Zhijie Feng et al.

In complex ecological communities, species may self-organize into clusters or clumps where highly similar species can coexist. The emergence of such species clusters can be captured by the interplay between neutral and niche theories. Based on the generalized Lotka-Volterra model of competition, we propose a minimal model for ecological communities in which the steady states contain self-organized clusters. In this model, species compete only with their neighbors in niche space through a common interaction strength. Unlike many previous theories, this model does not rely on random heterogeneity in interactions. Even in this minimal model where only the common interaction strength is varied, we find an exponentially large set of states that exhibit a rich variety of cluster patterns with different sizes and combinations. There are sharp phase transitions into the formation of clusters. There are also multiple phase transitions between different sets of possible cluster patterns, many of which accumulate near a small number of critical points. We analyze this phase structure using both numerical and analytical methods. In addition, the special case with only nearest neighbor interactions is exactly solvable using the method of transfer matrices from statistical mechanics. We analyze the critical behavior of these systems.

Boyu Ma, Jiaxiao Shi, Yiming Ji et al.

This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling in ecosystems. ECO draws an analogy between the dynamic process of solving optimization problems and ecological cycling. Unique update strategies are designed for the producer, consumer and decomposer, aiming to enhance the balance between exploration and exploitation processes. Through these strategies, ECO is able to achieve the global optimum, simulating the evolution of an ecological system toward its optimal state of stability and balance. Moreover, the performance of ECO is evaluated against five highly cited algorithms-CS, HS, PSO, GWO, and WOA-on 23 classical unconstrained optimization problems and 24 constrained optimization problems from IEEE CEC-2006 test suite, verifying its effectiveness in addressing various global optimization tasks. Furthermore, 50 recently developed metaheuristic algorithms are selected to form the algorithm pool, and comprehensive experiments are conducted on IEEE CEC-2014 and CEC-2017 test suites. Among these, five top-performing algorithms, namely ARO, CFOA, CSA, WSO, and INFO, are chosen for an in-depth comparison with the ECO on the IEEE CEC-2020 test suite, verifying the ECO's exceptional optimization performance. Finally, in order to validate the practical applicability of ECO in complex real-world problems, five state-of-the-art algorithms, including NSM-SFS, FDB-SFS, FDB-AGDE, L-SHADE, and LRFDB-COA, along with four best-performing algorithms from the "CEC2020 competition on real-world single objective constrained optimization", namely SASS, sCMAgES, EnMODE, and COLSHADE, are selected for comparative experiments on five engineering problems from CEC-2020-RW test suite (real-world engineering problems), demonstrating that ECO achieves performance comparable to those of advanced algorithms.

Patrick D. Schloss

ABSTRACT Considering it is common to find as much as 100-fold variation in the number of 16S rRNA gene sequences across samples in a study, researchers need to control for the effect of uneven sequencing effort. How to do this has become a contentious question. Some have argued that rarefying or rarefaction is “inadmissible” because it omits valid data. A number of alternative approaches have been developed to normalize and rescale the data that purport to be invariant to the number of observations. I generated community distributions based on 12 published data sets where I was able to assess the ability of multiple methods to control for uneven sequencing effort. Rarefaction was the only method that could control for variation in uneven sequencing effort when measuring commonly used alpha and beta diversity metrics. Next, I compared the false detection rate and power to detect true differences between simulated communities with a known effect size using various alpha and beta diversity metrics. Although all methods of controlling for uneven sequencing effort had an acceptable false detection rate when samples were randomly assigned to two treatment groups, rarefaction was consistently able to control for differences in sequencing effort when sequencing depth was confounded with treatment group. Finally, the statistical power to detect differences in alpha and beta diversity metrics was consistently the highest when using rarefaction. These simulations underscore the importance of using rarefaction to normalize the number of sequences across samples in amplicon sequencing analyses.IMPORTANCESequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.

Jong Il Park, Deok-Sun Lee, Sang Hoon Lee et al.

Understanding the behaviors of ecological systems is challenging given their multi-faceted complexity. To proceed, theoretical models such as Lotka-Volterra dynamics with random interactions have been investigated by the dynamical mean-field theory to provide insights into underlying principles such as how biodiversity and stability depend on the randomness in interaction strength. Yet the fully-connected structure assumed in these previous studies is not realistic as revealed by a vast amount of empirical data. We derive a generic formula for the abundance distribution under an arbitrary distribution of degree, the number of interacting neighbors, which leads to degree-dependent abundance patterns of species. Notably, in contrast to the well-mixed system, the number of surviving species can be reduced as the community becomes cooperative in heterogeneous interaction structures. Our study, therefore, demonstrates that properly taking into account heterogeneity in the interspecific interaction structure is indispensable to understanding the diversity in large ecosystems, and our general theoretical framework can apply to a much wider range of interacting many-body systems.

Xin Yang, Zhiyi Wang, Junling Niu et al.

Abstract Background Dysbiotic gut microbiome, genetically predisposed or chemically disrupted, has been linked with insulin-dependent diabetes (IDD) including autoimmune type 1 diabetes (T1D) in both humans and animal models. However, specific IDD-inducing gut bacteria remain to be identified and their casual role in disease development demonstrated via experiments that can fulfill Koch’s postulates. Results Here, we show that novel gut pathobionts in the Muribaculaceae family, enriched by a low-dose dextran sulfate sodium (DSS) treatment, translocated to the pancreas and caused local inflammation, beta cell destruction and IDD in C57BL/6 mice. Antibiotic removal and transplantation of gut microbiota showed that this low DSS disrupted gut microbiota was both necessary and sufficient to induce IDD. Reduced butyrate content in the gut and decreased gene expression levels of an antimicrobial peptide in the pancreas allowed for the enrichment of selective members in the Muribaculaceae family in the gut and their translocation to the pancreas. Pure isolate of one such members induced IDD in wildtype germ-free mice on normal diet either alone or in combination with normal gut microbiome after gavaged into stomach and translocated to pancreas. Potential human relevance of this finding was shown by the induction of pancreatic inflammation, beta cell destruction and IDD development in antibiotic-treated wildtype mice via transplantation of gut microbiome from patients with IDD including autoimmune T1D. Conclusion The pathobionts that are chemically enriched in dysbiotic gut microbiota are sufficient to induce insulin-dependent diabetes after translocation to the pancreas. This indicates that IDD can be mainly a microbiome-dependent disease, inspiring the need to search for novel pathobionts for IDD development in humans. Video Abstract

Wenkai Teng, Bin Liao, Mengyun Chen et al.

ABSTRACT Bacterial evolution is characterized by strong purifying selection as well as rapid adaptive evolution in changing environments. In this context, the genomic GC content (genomic GC) varies greatly but presents some level of phylogenetic stability, making it challenging to explain based on current hypotheses. To illuminate the evolutionary mechanisms of the genomic GC, we analyzed the base composition and functional inventory of 11,083 representative genomes. A phylogenetically constrained bimodal distribution of the genomic GC, which mainly originated from parallel divergences in the early evolution, was demonstrated. Such variation of the genomic GC can be well explained by DNA replication and repair (DRR), in which multiple pathways correlate with the genomic GC. Furthermore, the biased conservation of various stress-related genes, especially the DRR-related ones, implies distinct adaptive processes in the ancestral lineages of high- or low-GC clades which are likely induced by major environmental changes. Our findings support that the mutational biases resulting from these legacies of ancient adaptation have changed the course of adaptive evolution and generated great variation in the genomic GC. This highlights the importance of indirect effects of natural selection, which indicates a new model for bacterial evolution. IMPORTANCE GC content has been shown to be an important factor in microbial ecology and evolution, and the genomic GC of bacteria can be characterized by great intergenomic heterogeneity, high intragenomic homogeneity, and strong phylogenetic inertia, as well as being associated with the environment. Current hypotheses concerning direct selection or mutational biases cannot well explain these features simultaneously. Our findings of the genomic GC showing that ancient adaptations have transformed the DRR system and that the resulting mutational biases further contributed to a bimodal distribution of it offer a more reasonable scenario for the mechanism. This would imply that, when thinking about the evolution of life, diverse processes of adaptation exist, and combined effects of natural selection should be considered.