Hasil untuk "Microbial ecology"

Zehao Lu, Thijs L van der Plas, Parinaz Rashidi et al.

Large, open datasets can accelerate ecological research, particularly by enabling researchers to develop new insights by reusing datasets from multiple sources. However, to find the most suitable datasets to combine and integrate, researchers must navigate diverse ecological and environmental data provider platforms with varying metadata availability and standards. To overcome this obstacle, we have developed a large language model (LLM)-based metadata harvester that flexibly extracts metadata from any dataset's landing page, and converts these to a user-defined, unified format using existing metadata standards. We validate that our tool is able to extract both structured and unstructured metadata with equal accuracy, aided by our LLM post-processing protocol. Furthermore, we utilise LLMs to identify links between datasets, both by calculating embedding similarity and by unifying the formats of extracted metadata to enable rule-based processing. Our tool, which flexibly links the metadata of different datasets, can therefore be used for ontology creation or graph-based queries, for example, to find relevant ecological and environmental datasets in a virtual research environment.

Xiaowen Chen, Kyle Crocker, Seppe Kuehn et al.

The competition for resources is a defining feature of microbial communities. In many contexts, from soils to host-associated communities, highly diverse microbes are organized into metabolic groups or guilds with similar resource preferences. The resource preferences of individual taxa that give rise to these guilds are critical for understanding fluxes of resources through the community and the structure of diversity in the system. However, inferring the metabolic capabilities of individual taxa, and their competition with other taxa, within a community is challenging and unresolved. Here we address this gap in knowledge by leveraging dynamic measurements of abundances in communities. We show that simple correlations are often misleading in predicting resource competition. We show that spectral methods such as the cross-power spectral density (CPSD) and coherence that account for time-delayed effects are superior metrics for inferring the structure of resource competition in communities. We first demonstrate this fact on synthetic data generated from consumer-resource models with time-dependent resource availability, where taxa are organized into groups or guilds with similar resource preferences. By applying spectral methods to oceanic plankton time-series data, we demonstrate that these methods detect interaction structures among species with similar genomic sequences. Our results indicate that analyzing temporal data across multiple timescales can reveal the underlying structure of resource competition within communities.

Quan Nguyen, Adji Bousso Dieng

While monitoring biodiversity through camera traps has become an important endeavor for ecological research, identifying species in the captured image data remains a major bottleneck due to limited labeling resources. Active learning -- a machine learning paradigm that selects the most informative data to label and train a predictive model -- offers a promising solution, but typically focuses on uncertainty in the individual predictions without considering uncertainty across the entire dataset. We introduce a new active learning policy, Vendi information gain (VIG), that selects images based on their impact on dataset-wide prediction uncertainty, capturing both informativeness and diversity. We applied VIG to the Snapshot Serengeti dataset and compared it against common active learning methods. VIG needs only 3% of the available data to reach 75% accuracy, a level that baselines require more than 10% of the data to achieve. With 10% of the data, VIG attains 88% predictive accuracy, 12% higher than the best of the baselines. This improvement in performance is consistent across metrics and batch sizes, and we show that VIG also collects more diverse data in the feature space. VIG has broad applicability beyond ecology, and our results highlight its value for biodiversity monitoring in data-limited environments.

Kevin Bradley Dsouza, Graham Alexander Watt, Yuri Leonenko et al.

Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically optimal allocation of land to margin and habitat interventions at the individual farm level. These farm-specific intervention levels are then passed to an Ecological Connectivity (EC) model, which spatially arranges them across the landscape to maximize connectivity while preserving farm-level profitability. Finally, we introduce a Bayesian Optimization (BO) approach that translates these spatial outcomes into simple, cost effective, and scalable policy instruments, such as subsidies and eco-premiums, using non-spatial, farm-level policy parameters. Applying the framework to a Canadian agricultural landscape, we demonstrate how it enhances connectivity under real-world economic constraints. Our approach provides a globally relevant tool for aligning farm incentives with biodiversity goals, advancing the development of agri-environmental policies that are economically viable and ecologically effective.

Hyunwoo Yoo, Gail L. Rosen

Traditional machine learning models struggle to generalize in microbiome studies where only metadata is available, especially in small-sample settings or across studies with heterogeneous label formats. In this work, we explore the use of large language models (LLMs) to classify microbial samples into ontology categories such as EMPO 3 and related biological labels, as well as to predict pathogen contamination risk, specifically the presence of E. Coli, using environmental metadata alone. We evaluate LLMs such as ChatGPT-4o, Claude 3.7 Sonnet, Grok-3, and LLaMA 4 in zero-shot and few-shot settings, comparing their performance against traditional models like Random Forests across multiple real-world datasets. Our results show that LLMs not only outperform baselines in ontology classification, but also demonstrate strong predictive ability for contamination risk, generalizing across sites and metadata distributions. These findings suggest that LLMs can effectively reason over sparse, heterogeneous biological metadata and offer a promising metadata-only approach for environmental microbiology and biosurveillance applications.

Júnia Schultz, Sharifah Altalhi, Sharifah Altalhi et al.

IntroductionHydrothermal vents are among Earth’s most extreme ecosystems, characterized by high temperatures, elevated metal concentrations, and steep chemical gradients that sustain specialized microbial life. Although bacterial and archaeal communities in these environments have been extensively studied, fungal diversity remains poorly understood. The recently discovered Hatiba Mons hydrothermal vent field in the Red Sea Rift provides a unique setting to investigate fungal communities in a hypersaline, metal-rich environment.MethodsWe analyzed fungal diversity in crusts, sediments, and microbial mats collected from five active vent sites at Hatiba Mons. A total of 38 subsamples were obtained using a remotely operated vehicle (ROV) during the KRSE Aegaeo RV cruise in May 2022. DNA was extracted, and the fungal ITS rRNA gene region was sequenced on an Illumina MiSeq platform. Sequence processing and taxonomic assignment were performed with QIIME2 and the UNITE database, while downstream statistical analyses were conducted in R with phyloseq.ResultsFungal community composition varied significantly across sample types, as shown by Principal Coordinates Analysis (PCoA) and confirmed by PERMANOVA. Ascomycota, Basidiomycota, and Chytridiomycota dominated the assemblages. Functional predictions using FUNGuild revealed diverse ecological roles, including saprotrophic, symbiotic, and pathogenic lifestyles.DiscussionThis study provides the first characterization of fungal communities in the Hatiba Mons hydrothermal system. The distinct taxonomic and functional profiles observed suggest that fungi contribute to biogeochemical cycling and ecosystem dynamics in extreme marine habitats. These findings expand current knowledge of fungal ecology in hydrothermal vents and underscore the importance of including fungi in future deep-sea microbiological research.

Zhi-Yun Huang, Ying Li, Hu-Anhe Xiong et al.

The role of rhizosphere bacteria in facilitating plant invasion is increasingly acknowledged, yet the influence of specific microbial functional traits remains insufficiently understood. This study addresses this gap by isolating two bacterial strains, <i>Bacillus</i> sp. ScRB44 and <i>Pseudomonas</i> sp. ScRB22, from the rhizosphere of the invasive weed <i>Solidago canadensis</i>. We assessed their nitrogen utilization capacity and indoleacetic acid (IAA) production capabilities to evaluate their ecological functions. Our three-stage experimental design encompassed strain promotion, nutrient stress, and competition phases. <i>Bacillus</i> sp. ScRB44 demonstrated robust IAA production and significantly improved the nitrogen utilization efficiency, significantly enhancing <i>S. canadensis</i> growth, especially under nutrient-poor conditions, and promoting a shift in biomass allocation toward the roots, thereby conferring a competitive advantage over native species. Conversely, <i>Pseudomonas</i> sp. ScRB22 exhibited limited functional activity and a negligible impact on plant performance. These findings underscore that the ecological impact of rhizosphere bacteria on invasive weeds is closely linked to their specific growth-promoting functions. By enhancing stress adaptation and optimizing resource allocation, certain microorganisms may facilitate the establishment of invasive weeds in adverse environments. This study highlights the significance of microbial functional traits in invasion ecology and suggests novel approaches for microbiome-based invasive weed management, with potential applications in agricultural soil health improvement and ecological restoration.

Giorgia Rampanti, Federica Cardinali, Ilario Ferrocino et al.

The aim of the present study was to investigate the microbiota of processed ready-to-eat (fried or boiled) edible insects sold by street vendors at local green markets in Thailand (Bangkok and Koh Samui). To this end, samples of 4 insect species (rhino beetle adults, silkworm pupae, giant waterbugs adults, and black scorpions) were collected and analyzed through viable counting and metataxonomic analysis. Enterobacteriaceae showed counts below 1 log cfu g⁻¹ in all samples, except for black scorpions, which showed elevated counts reaching up to 4 log cfu g⁻¹. Total mesophilic aerobes counts were up to 8 log cfu g⁻¹ in all the analyzed samples. Counts below 1 log cfu g⁻¹ were observed for <i>Escherichia coli</i>, <i>Staphylococcus aureus</i>, sulfite-reducing clostridia viable cells and spores, and <i>Bacillus cereus</i>. All the samples showed the absence of <i>Listeria monocytogenes</i> and <i>Salmonella</i> spp. According to metataxonomic analysis, 14 taxa were consistently present across all insect samples, including <i>Dellaglioa algida</i>, <i>Latilactobacillus curvatus</i>, <i>Latilactobacillus sakei</i>, Acetobacteraceae, <i>Apilactobacillus kunkeei</i>, <i>Bombilactobacillus</i> spp., Enterobacteriaceae, <i>Gilliamella</i> spp., <i>Lactobacillus</i> spp., <i>Lactobacillus apis</i>, <i>Streptococcus thermophilus</i>, <i>Lacticaseibacillus rhamnosus</i>, <i>Lactiplantibacillus plantarum</i>, and <i>Weissella</i> spp. Minority taxa included <i>Alcaligenes</i> spp., <i>Brochothrix thermosphacta</i>, <i>Psychrobacter</i> spp., <i>Staphylococcus saprophyticus</i>, <i>Lactobacillus melliventris</i>, <i>Pediococcus</i> spp., <i>Levilactobacillus brevis</i>, and <i>Snodgrassella alvi</i>.

Alexandros A. Fragkopoulos, Florian Böhme, Nicole Drewes et al.

Photosynthetic microbes have evolved and successfully adapted to the ever-changing environmental conditions in complex microhabitats throughout almost all ecosystems on Earth. In the absence of light, they can sustain their biological functionalities through aerobic respiration, and even in anoxic conditions through anaerobic metabolic activity. For a suspension of photosynthetic microbes in an anaerobic environment, individual cellular motility is directly controlled by its photosynthetic activity, i.e. the intensity of the incident light absorbed by chlorophyll. The effects of the metabolic activity on the collective motility on the population level, however, remain elusive so far. Here, we demonstrate that at high light intensities, a suspension of photosynthetically active microbes exhibits a stable reverse sedimentation profile of the cell density due to the microbes' natural bias to move against gravity. With decreasing photosynthetic activity, and therefore suppressed individual motility, the living suspension becomes unstable giving rise to coherent bioconvective flows. The collective motility is fully reversible and manifests as regular, three-dimensional plume structures, in which flow rates and cell distributions are directly controlled via the light intensity. The coherent flows emerge in the highly unfavourable condition of lacking both light and oxygen and, thus, might help the microbial collective to expand the exploration of their natural habitat in search for better survival conditions.

Daxi Wang, Xinglou Yang, Zirui Ren et al.

Abstract Background Zoonotic viruses cause substantial public health and socioeconomic problems worldwide. Understanding how viruses evolve and spread within and among wildlife species is a critical step when aiming for proactive identification of viral threats to prevent future pandemics. Despite the many proposed factors influencing viral diversity, the genomic diversity and structure of viral communities in East Africa are largely unknown. Results Using 38.3 Tb of metatranscriptomic data obtained via ultradeep sequencing, we screened vertebrate-associated viromes from 844 bats and 250 rodents from Kenya and Uganda collected from the wild. The 251 vertebrate-associated viral genomes of bats (212) and rodents (39) revealed the vast diversity, host-related variability, and high geographic specificity of viruses in East Africa. Among the surveyed viral families, Coronaviridae and Circoviridae showed low host specificity, high conservation of replication-associated proteins, high divergence among viral entry proteins, and frequent recombination. Despite major dispersal limitations, recurrent mutations, cocirculation, and occasional gene flow contribute to the high local diversity of viral genomes. Conclusions The present study not only shows the landscape of bat and rodent viromes in this zoonotic hotspot but also reveals genomic signatures driven by the evolution and dispersal of the viral community, laying solid groundwork for future proactive surveillance of emerging zoonotic pathogens in wildlife. Video Abstract

Fanyue Li, Roypim Thananusak, Nachon Raethong et al.

<i>Mucor circinelloides</i> WJ11 is a lipid-producing strain with industrial potential. A holistic approach using gene manipulation and bioprocessing development has improved lipid production and the strain’s economic viability. However, the systematic regulation of lipid accumulation and carotenoid biosynthesis in <i>M. circinelloides</i> remains unknown. To dissect the metabolic mechanism underlying lipid and carotenoid biosynthesis, transcriptome analysis and reporter metabolites identification were implemented between the wild-type (WJ11) and <i>ΔcarRP</i> WJ11 strains of <i>M. circinelloides</i>. As a result, transcriptome analysis revealed 10,287 expressed genes, with 657 differentially expressed genes (DEGs) primarily involved in amino acid, carbohydrate, and energy metabolism. Integration with a genome-scale metabolic model (GSMM) identified reporter metabolites in the <i>ΔcarRP</i> WJ11 strain, highlighting metabolic pathways crucial for amino acid, energy, and nitrogen metabolism. Notably, the downregulation of genes associated with carotenoid biosynthesis and acetyl-CoA generation suggests a coordinated relationship between the carotenoid and fatty acid biosynthesis pathways. Despite disruptions in the carotenoid pathway, lipid production remains stagnant due to reduced acetyl-CoA availability, emphasizing the intricate metabolic interplay. These findings provide insights into the coordinated relationship between carotenoid and fatty acid biosynthesis in <i>M. circinelloides</i> that are valuable in applied research to design optimized strains for producing desired bioproducts through emerging technology.

R. Crawford, M. Klug, C. A. Reddy

Olivier Monga, Zakaria Belghali, Mouad Klai et al.

Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated research efforts to unveil the extremely complex micro-scale processes that control the activity of soil microorganisms. Voxel-based description (up to hundreds millions voxels) of the pore space can be extracted, from grey level 3D CT scanner images, by means of simple image processing tools. Classical methods for numerical simulation of biological dynamics using mesh of voxels, such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the use of more compact and reliable geometrical representations of pore space can drastically decrease the computational cost of the simulations. Several recent works propose basic analytic volume primitives (e.g. spheres, generalized cylinders, ellipsoids) to define a piece-wise approximation of pore space for numerical simulation of draining, diffusion and microbial decomposition. Such approaches work well but the drawback is that it generates approximation errors. In the present work, we study another alternative where pore space is described by means of geometrically relevant connected subsets of voxels (regions) computed from the curvilinear skeleton. Indeed, many works use the curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes within various domains (medicine, material sciences, petroleum engineering, etc.) but only a few ones in soil sciences. Within the context of soil sciences, most studies dealing with 3D medial axis focus on the determination of pore throats. Here, we segment pore space using curvilinear skeleton in order to achieve numerical simulation of microbial decomposition (including diffusion processes). We validate simulation outputs by comparison with other methods using different pore space geometrical representations (balls, voxels).

Julia M. Kelliher, Aaron J. Robinson, Reid Longley et al.

Abstract As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how “microbiomes within microbiomes” affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales. Video Abstract

Jordan S. Mar, Naruhisa Ota, Nick D. Pokorzynski et al.

Abstract Background IL-22 is induced by aryl hydrocarbon receptor (AhR) signaling and plays a critical role in gastrointestinal barrier function through effects on antimicrobial protein production, mucus secretion, and epithelial cell differentiation and proliferation, giving it the potential to modulate the microbiome through these direct and indirect effects. Furthermore, the microbiome can in turn influence IL-22 production through the synthesis of L-tryptophan (L-Trp)-derived AhR ligands, creating the prospect of a host-microbiome feedback loop. We evaluated the impact IL-22 may have on the gut microbiome and its ability to activate host AhR signaling by observing changes in gut microbiome composition, function, and AhR ligand production following exogenous IL-22 treatment in both mice and humans. Results Microbiome alterations were observed across the gastrointestinal tract of IL-22-treated mice, accompanied by an increased microbial functional capacity for L-Trp metabolism. Bacterially derived indole derivatives were increased in stool from IL-22-treated mice and correlated with increased fecal AhR activity. In humans, reduced fecal concentrations of indole derivatives in ulcerative colitis (UC) patients compared to healthy volunteers were accompanied by a trend towards reduced fecal AhR activity. Following exogenous IL-22 treatment in UC patients, both fecal AhR activity and concentrations of indole derivatives increased over time compared to placebo-treated UC patients. Conclusions Overall, our findings indicate IL-22 shapes gut microbiome composition and function, which leads to increased AhR signaling and suggests exogenous IL-22 modulation of the microbiome may have functional significance in a disease setting. Video Abstract

Jessa P. Ata, Anna W. Schoettle, Rachael A. Sitz et al.

Fungal endophytic communities in needles of field-grown Pinus flexilis previously inferred to carry major gene resistance (R) to white pine blister rust (WPBR) or to lack it (S) were surveyed to identify unique microbes that may be recruited by WPBR-resistant genotypes. Resistant and susceptible trees were sampled in each of 11 P. flexilis populations for a total of 50 trees sampled. Through next-generation sequencing, this study showed a diverse needle mycobiota in P. flexilis, of which many remain unknown, regardless of the presence or absence of the WPBR resistance gene, Cr4. Ascomycota dominated the mycobiota (88.9%) followed by Basidiomycota (4.4%) and Chytridiomycota (0.03%), and the remaining 6.7% were unclassified. Shared (n = 105) and unique (n = 48 in R and n = 49 in S) fungal taxa, including differentially abundant operational taxonomic units, were identified that could provide insights into core mycobiota and host genotype-specific fungal groups. Marginal variation of the fungal diversity and structure was observed between host genotypes, which indicates that neither Cr4 nor the physiological differences associated with the presence or absence of the gene affects mycobiota recruitment. Instead, other parameters, including host size (diameter at breast height) and site elevation, significantly influenced the variability of the composition and structure of the fungal endophytic community. Further investigations are needed to understand the relationship of unique or differentially abundant taxa with one genotype or the other, and to determine the role of the needle mycobiota in WPBR disease development in natural stands of P. flexilis.

Evan D. Gorman, Manuel E. Lladser

Ultrametric matrices have a rich structure that is not apparent from their definition. Notably, the subclass of strictly ultrametric matrices are covariance matrices of certain weighted rooted binary trees. In applications, these matrices can be large and dense, making them difficult to store and handle. In this manuscript, we exploit the underlying tree structure of these matrices to sparsify them via a similarity transformation based on Haar-like wavelets. We show that, with overwhelmingly high probability, only an asymptotically negligible fraction of the off-diagonal entries in random but large strictly ultrametric matrices remain non-zero after the transformation; and develop a fast algorithm to compress such matrices directly from their tree representation. We also identify the subclass of matrices diagonalized by the wavelets and supply a sufficient condition to approximate the spectrum of strictly ultrametric matrices outside this subclass. Our methods give computational access to a covariance model of the microbiologists' Tree of Life, which was previously inaccessible due to its size, and motivate defining a new but wavelet-based phylogenetic $β$-diversity metric. Applying this metric to a metagenomic dataset demonstrates that it can provide novel insight into noisy high-dimensional samples and localize speciation events that may be most important in determining relationships between environmental factors and microbial composition.

Udit Raj, Shashankaditya Upadhyay, Moumita Karmakar et al.

An ecological network is a formal representation of a specific type of interaction in a corresponding ecosystem. Such networks have traditionally been modelled as encoding exclusively pairwise interactions among the fundamental units of ecosystems and have been represented and analysed using graph-theoretic methods. However, many real-world ecosystems may entertain non-binary, polyadic relations between their units, which cannot be captured by the pairwise interaction methods, but require higher-order interaction framework, and consequently the corresponding ecological networks cannot be modelled using graph-theoretic framework. This work gives a structural definition of ecological network suitable for modelling all orders of interactions between the fundamental units of the corresponding ecological system, including and going beyond the pairwise interaction framework. Carbon mediation between units of some select ecosystems are studied by modelling the corresponding ecological networks as simplicial complexes following the definition. The concept of graph centrality measure has been extended to simplicial centrality, and some important centrality measures of these networks at various structural levels of the complexes have been calculated. The centrality measures reveal valuable structural information including information about those vertices that are more likely to participate in higher-order interactions, as well as inform whether there is a difference in the ranks of vertices for these higher-order networks based on graph centrality and simplicial centrality measures.

Amanda K Hund, Peter Tiffin, Jean-Gabriel Young et al.

Measuring gene expression simultaneously in both hosts and symbionts offers a powerful approach to explore the biology underlying species interactions. Such dual or simultaneous RNAseq approaches have primarily been used to gain insight into gene function in model systems, but there is opportunity to expand and apply these tools in new ways to understand ecological and evolutionary questions. By incorporating genetic diversity in both hosts and symbionts and studying how gene expression is correlated between partner species, we can gain new insight into host-symbiont coevolution and the ecology of species interactions. In this perspective, we explore how these relatively new tools could be applied to study such questions. We review the mechanisms that could be generating patterns of cross-species gene coexpression, including indirect genetic effects and selective filters, how these tools could be applied across different biological and temporal scales, and outline other methodological considerations and experiment possibilities.

Alfonso Benítez-Páez, Annick V. Hartstra, Max Nieuwdorp et al.

Fecal microbiota transplantation (FMT) is currently used for treating Clostridium difficile infection and explored for other clinical applications in experimental trials. However, the effectiveness of this therapy could vary, and partly depend on the donor’s bacterial species engraftment, whose evaluation is challenging because there are no cost-effective strategies for accurately tracking the microbe transference. In this regard, the precise identification of bacterial species inhabiting the human gut is essential to define their role in human health unambiguously. We used Nanopore-based device to sequence bacterial rrn operons (16S-ITS-23S) and to reveal species-level abundance changes in the human gut microbiota of a FMT trial. By assessing the donor and recipient microbiota before and after FMT, we further evaluated whether this molecular approach reveals strain-level genetic variation to demonstrate microbe transfer and engraftment. Strict control over sequencing data quality and major microbiota covariates was critical for accurately estimating the changes in gut microbial species abundance in the recipients after FMT. We detected strain-level variation via single-nucleotide variants (SNVs) at rrn regions in a species-specific manner. We showed that it was possible to explore successfully the donor-bacterial strain (e.g., Parabacteroides merdae) engraftment in recipients of the FMT by assessing the nucleotide frequencies at rrn-associated SNVs. Our findings indicate that the engraftment of donors’ microbiota is to some extent correlated with the improvement of metabolic health in recipients and that parameters such as the baseline gut microbiota configuration, sex, and age of donors should be considered to ensure the success of FMT in humans. The study was prospectively registered at the Dutch Trial registry – NTR4488 (https://www.trialregister.nl/trial/4488).