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67 results for “microbial interactions”
Microbial narrow-escape is facilitated by wall interactions: Simulation Supplementary material
<p>Simulation codes and simulation results for the paper "Microbial narrow-escape is facilitated by wall interactions".</p>
Using the Tea Bag Index to unravel how interactions between an antibiotic (Trimethoprim) and endocrine disruptor (17a-estradiol) affect aquatic microbial activity: Supporting Dataset 1
<p>The constant release of complex mixture of pharmaceuticals, including antimicrobials and endocrine disruptors, into the aquatic environment. These have the potential to affect aquatic microbial metabolism and alter biogeochemical cycling of carbon and nutrients. We used the Tea Bag Index (TBI) for decomposition within a series of contaminant exposure experiments to test how interactions between an antibiotic (trimethoprim) and endocrine disruptor (17a-estradiol) affects microbial activity in an aquatic system. The TBI is a citizen science tool used to test microbial activity by measuring the differential degradation of green and rooibos tea as proxies for labile and recalcitrant organic matter decomposition. Here we present the raw data on pharmaceutical exposures and the mass loss of the Rooibos and Green tea bags within the experiment. From Tea Bag mass loss we then calculated the Stabilisation Factor (S) and Initial Decomposition Rate of the labile organic matter fraction.</p>
SURFBIO Training: "Analytical methods for the study of microbial cell-Surface and Surface-colloid interactions" (2021)
<p>2021. SURFBIO project training within WP1.</p><p>Content:</p><ul><li><strong>Webinar on Vertical scanning interferometry: a microscopic technique to analyze surface reactivity, </strong>by Dr. Cornelius Fischer (HZDR, Germany) </li><li><strong>Webinar on An introduction to radiolabelling as a versatile tool in colloid tracing, </strong>by Stefan Schymura (HZDR, Germany).</li><li><strong>Webinar on Development and construction of biocarriers and aggregates for potential industrial applications</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li><li><strong>Materials and fluidic design to study artificially functionalized microorganisms</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li></ul>
Assessing the importance of interspecific interactions in the evolution of microbial communities
<p>These data and script are related to the article entitled "Assessing the importance of interspecific interactions in the evolution of microbial communities". This study reports the results of an experiment that aimed at understanding the role of interactions between bacterial species in the evolutionary responses of bacterial communities. The phenotype (optical density) of eight communities composed of two bacterial strains was assessed before and after an experimental evolution of five months (with a transfer each 3.5 days) and compared to the phenotype of communities rebuilt from the same strains that evolved in isolation. The phenotype of the bacterial strains of the study grown in isolation under the three evolutionary treatments (ancestor, evolved in isolation, evolved in community) was also assessed. All the data and codes needed to reproduce the figures and tables presented in the manuscript are provided.</p>
Interspecific social interactions shape public goods production in natural microbial communities
<p>The R code (Hesse_etal.R) descibes step by step how metal polution affects the ecology and evolution of a community-wide public good – the production of metal-detoxifying siderophores. This code accompanies the manuscript "Interspecific interactions shape public goods production in natural microbial communities" (https://www.biorxiv.org/content/10.1101/710715v1).</p> <p>The R code is divided into different sections per figure (1-5 and supplementary Figure S1). Each section first starts with reading in the appropriate data files (cvs files) and continues with statistics and plotting of figures.</p>
Figure 1 in Soil quality, leaf litter quality, and microbial biomass interactively drive soil respiration in a microcosm experiment
Figure 1. Principal components analysis of (A) soil quality and (B) leaf litter quality across the experimental treatments (Table S1-2). Soil quality was quantified as a combination of soil pH, C, N, and C:N; leaf litter quality was quantified as a combination of leaf Ca, C, lignin, Mg, N, P, C:N, C:P, and N:P. Soil and leaf litter were collected from Hainich National Park, Germany.
Fig. 3 in Review paper Stimulation of Plant Growth through Interactions of Bacteria and Protozoa: Testing the Auxiliary Microbial Loop Hypothesis
Fig. 3. Difference in growth responses of 16 cultivars of rice (Oryza sativa L.) grown in autoclaved soil and with a diverse soil bacterial filtrate reinoculated into the farmland soil in presence (black bars) and absence (white bars) of Acanthamoeba sp. Shoot dry weight (a), total root length (b), number of laterals at seminal root (c), and total nitrogen uptake (d). Vertical error bars represent standard deviation (n = 4–9). The symbols * and ** indicate a significant difference at P <0.05 and 0.01 by one way ANOVA, respectively. Data from Somasundaram et al. (2008).
Fig. 1 in Review paper Stimulation of Plant Growth through Interactions of Bacteria and Protozoa: Testing the Auxiliary Microbial Loop Hypothesis
Fig. 1. Respiration of glucose-C (µg CO -C * g–1 soil) after addi2 tion of 1,000, 2,000, 4,000, and 8,000 ppm glucose to soil from the Heteren field site (Scheu 1992). 1,000 ppm glucose are completely respired by soil microorganisms within a single day, but glucose was not lasting longer than 4 days after saturation of the soil with glucose at 2,000–8,000 ppm (mean of 3 replicates ± 1 SD, see Ekelund et al. (2009) for a characterization of the soil).
Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure
<p>Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222</p> <p> https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1</p> <p> </p>
Evolution in interacting species alters predator life history traits, behavior and morphology in experimental microbial communities
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Microbial dietary preference and interactions affect the export of lipids to the deep ocean
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Synthetic microbial consortia with programmable ecological interactions
<p>1. Central to the composition, structure and function of any microbial community is the complex species interaction web. But understanding the overwhelming complexity of ecological interaction webs has been challenging, owing at least partly to the lack of efficient tools for disentangling species interactions in natural or artificial microbial communities.</p> <p>2. In this study, we developed a microbial experimental system which allows for rapidly generating microbial consortia with programmable ecological interactions. We engineered the model organism Escherichia coli to construct metabolism- and quorum sensing-based modules. The two engineered modules were used to create synthetic microbial consortia of synergy, competition and exploitation.</p> <p>3. We showed that each of synthetic microbial consortia displayed the unique mode of population dynamics under certain initial inoculation conditions. We also demonstrated that the transitions between exploitation and the types of competition or synergy based on the same paired strains were plausible by tuning the two engineered modules. We lastly derived mathematical models to quantitatively capture the experimentally observed population dynamics of these synthetic microbial consortia.</p> <p>4. This approach offers a fresh angle to engineering microbial systems for experimentally testing ecological questions with a much greater control and manipulation.</p>
Soil microbial community, dissolved organic matter and nutrient cycling interactions change along an elevation gradient in subtropical China
<p>This table contains data on soil dissolved organic matter at different elevations in Wuyi Mountain, China, which is attached to the manuscript submitted to Journal of Environmental Management (Manuscript ID:JEMA-D-23-01912). </p>
Designing a Synthetic Microbial Community through Genome Metabolic Modeling to enhance Plant-Microbe Interaction
<p>Supplementary data 1 - <strong>Reconstructed genome-scale metabolic networks from MAGs and Hosts</strong></p> <p>Supplementary data 2 - P<strong>lant growth-promoting traits among members of the minimal community</strong></p> <p> </p> <p>Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynCom, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and a careful selection of compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed an <em>in-silico</em> approach using genome metabolic modeling to design a synthetic microbial community aimed at improving the yield of important crop plants. We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, EPS production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our selection process for the SymCom, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. Our study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.</p>
Plant-microbe interactions derive the rhizosphere microbial assembly and nitrogen cycling in a subtropical forest
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Synthetic microbial consortia with programmable ecological interactions
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The role of plant-pollinator interactions in structuring nectar microbial communities
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Necromass mass loss and microbial abundance for necromass interactions study
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Data from: Local interactions and self-organized spatial patterns stabilize microbial cross-feeding against cheaters
Mutualisms are ubiquitous, but models predict they should be susceptible to cheating. Resolving this paradox has become relevant to synthetic ecology: cooperative cross-feeding, a nutrient exchange mutualism, has been proposed to stabilize microbial consortia. Previous attempts to understand how cross-feeders remain robust to non-producing cheaters have relied on complex behavior (e.g., cheater punishment) or group selection. Using a stochastic spatial model, we demonstrate two novel mechanisms that can allow cross-feeders to outcompete cheaters, rather than just escape from them. Both mechanisms work through the spatial segregation of the resources, which prevents individual cheaters from acquiring the resources they need to reproduce. First, if microbe dispersal is low but resources are shared widely, then the cross-feeders self-organize into stable spatial patterns. Here the cross-feeders can build up where the resource they need is abundant, and send their resource to where their partner is, separating resources at regular intervals in space. Second, if dispersal is high but resource sharing is local, then random variation in population density creates small-scale variation in resource density, separating the resources from each other by chance. These results suggest that cross-feeding may be more robust than previously expected and offer strategies to engineer stable consortia.
Data from: Land use in mountain grasslands alters drought response and recovery of carbon allocation and plant-microbial interactions
1. Mountain grasslands have recently been exposed to substantial changes in land-use and climate and in the near future will likely face an increased frequency of extreme droughts. To date is not known how the drought responses of carbon (C) allocation, a key process in the C cycle, are affected by land-use changes in mountain grassland. 2. We performed an experimental summer drought on an abandoned grassland and a traditionally managed hay meadow and traced the fate of recent assimilates through the plant-soil continuum. We applied two 13CO2 pulses, at peak drought and in the recovery phase shortly after rewetting. 3. Drought decreased total C uptake in both grassland types and led to a loss of aboveground carbohydrate storage pools. The belowground C allocation to root sucrose was enhanced by drought, especially in the meadow, which also held larger root carbohydrate storage pools. 4. The microbial community of the abandoned grassland comprised more saprotrophic fungal and Gram (+) bacterial markers compared to the meadow. Drought increased the newly introduced AM and saprotrophic fungi:bacteria ratio in both grassland types. At peak drought the 13C transfer into AM fungi, saprotrophic fungi and Gram (-) bacteria was more strongly reduced in the meadow than in the abandoned grassland, which contrasted the patterns of the root carbohydrate pools. 5. In both grassland types the C allocation largely recovered after rewetting. Slowest recovery was found for AM fungi and their 13C uptake. In contrast, all bacterial markers quickly recovered C uptake. In the meadow, where plant nitrate uptake was enhanced after drought, C uptake was even higher than in control plots. 6. Synthesis. Our results suggest that resistance and resilience (i.e. recovery) of plant C dynamics and plant-microbial interactions are negatively related, i.e. high resistance is followed by slow recovery and vice versa. The abandoned grassland was more resistant to drought than the meadow and possibly had a stronger link to AM fungi that could have provided better access to water through the hyphal network. In contrast, meadow communities strongly reduced C allocation to storage and C transfer to the microbial community in the drought phase, but in the recovery phase invested C resources in the bacterial communities to gain more nutrients for regrowth. We conclude that management of mountain grasslands increases their resilience to drought.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.