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#microbiome

7 posts7 participants0 posts today
Public
Paul Houle

🦠 Emulsion technique boosts plant growth with bacteria

phys.org/news/2025-03-emulsion

phys.orgEmulsion technique boosts plant growth with bacteriaResearchers have demonstrated a technique for successfully encapsulating bacteria that can then be stored and applied to plants to improve plant growth and protect against pests and pathogens. The technique opens the door to creating a wide range of crop applications that allow farmers to make use of these beneficial bacteria in conjunction with agrochemicals. The paper, "Pickering Emulsion for Enhanced Viability of Plant Growth Promoting Bacteria and Combined Delivery of Agrochemicals and Biologics," is published in the journal Advanced Functional Materials.
#agriculture#plants#microbes
Public
Newsramp

Creative Biolabs advances next-generation probiotics research, developing precision microbial solutions for targeted disease interventions through advanced genetic engineering and comprehensive strain evaluation. Transforming microbiome science one strain at a time. #Microbiome #Probiotics

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Public
astephen

New preprint on microbiome data transformation methods -argues that compositional transformation is not superior to more standard differential abundance methods such as DESeq! A bit of a turn up for the books! #microbiome #datascience 🧪

www.biorxiv.org/content/10.1...
biorxiv.org/content/10.1101/20

bioRxiv · Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truthPrevious benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and “real data” examples, but to the best of our knowledge, none have yet employed experimental data with known “ground truth” differential abundance. A key debate in the field centers on whether compositional methods are necessary for DA analysis, which is challenging to answer due to the lack of ground truth data. To address this gap, we created the Bioconductor data package MicrobiomeBenchmarkData , featuring three microbiome datasets with established biological ground truths: 1) diverse oral microbiomes from supragingival and subgingival plaques, expected to favor aerobic and anaerobic bacteria, respectively, 2) low-diversity microbiomes from healthy vaginas and bacterial vaginosis, conditions that have been well-characterized through cell culture and microscopy, and 3) a spike-in dataset with constant, known absolute abundances of three bacteria. We benchmarked 17 DA approaches and demonstrated that compositional DA methods are not beneficial but rather lack sensitivity, show increased variability in constant-abundance spike-ins, and, most surprisingly, more frequently produce paradoxical results with DA in the wrong direction for the low-diversity microbiome. Conversely, commonly used methods in microbiome literature, such as LEfSe , the Wilcoxon test, and RNA-seq-derived methods, performed best. We conclude that researchers continue using widely adopted non-parametric or RNA-seq DA methods and that further development of compositional methods includes benchmarking against datasets with known biological ground truth. ### Competing Interest Statement The authors have declared no competing interest.
Public
astephen

New preprint on microbiome data transformation methods -argues that compositional transformation is not superior to more standard differential abundance methods such as DESeq! A bit of a turn up for the books! #microbiome #datascience 🧪

www.biorxiv.org/content/10.1...
biorxiv.org/content/10.1101/20

bioRxiv · Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truthPrevious benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and “real data” examples, but to the best of our knowledge, none have yet employed experimental data with known “ground truth” differential abundance. A key debate in the field centers on whether compositional methods are necessary for DA analysis, which is challenging to answer due to the lack of ground truth data. To address this gap, we created the Bioconductor data package MicrobiomeBenchmarkData , featuring three microbiome datasets with established biological ground truths: 1) diverse oral microbiomes from supragingival and subgingival plaques, expected to favor aerobic and anaerobic bacteria, respectively, 2) low-diversity microbiomes from healthy vaginas and bacterial vaginosis, conditions that have been well-characterized through cell culture and microscopy, and 3) a spike-in dataset with constant, known absolute abundances of three bacteria. We benchmarked 17 DA approaches and demonstrated that compositional DA methods are not beneficial but rather lack sensitivity, show increased variability in constant-abundance spike-ins, and, most surprisingly, more frequently produce paradoxical results with DA in the wrong direction for the low-diversity microbiome. Conversely, commonly used methods in microbiome literature, such as LEfSe , the Wilcoxon test, and RNA-seq-derived methods, performed best. We conclude that researchers continue using widely adopted non-parametric or RNA-seq DA methods and that further development of compositional methods includes benchmarking against datasets with known biological ground truth. ### Competing Interest Statement The authors have declared no competing interest.
Public
astephen

New preprint on microbiome data transformation methods -argues that compositional transformation is not superior to more standard differential abundance methods such as DESeq! A bit of a turn up for the books! #microbiome #datascience 🧪

www.biorxiv.org/content/10.1...
biorxiv.org/content/10.1101/20

bioRxiv · Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truthPrevious benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and “real data” examples, but to the best of our knowledge, none have yet employed experimental data with known “ground truth” differential abundance. A key debate in the field centers on whether compositional methods are necessary for DA analysis, which is challenging to answer due to the lack of ground truth data. To address this gap, we created the Bioconductor data package MicrobiomeBenchmarkData , featuring three microbiome datasets with established biological ground truths: 1) diverse oral microbiomes from supragingival and subgingival plaques, expected to favor aerobic and anaerobic bacteria, respectively, 2) low-diversity microbiomes from healthy vaginas and bacterial vaginosis, conditions that have been well-characterized through cell culture and microscopy, and 3) a spike-in dataset with constant, known absolute abundances of three bacteria. We benchmarked 17 DA approaches and demonstrated that compositional DA methods are not beneficial but rather lack sensitivity, show increased variability in constant-abundance spike-ins, and, most surprisingly, more frequently produce paradoxical results with DA in the wrong direction for the low-diversity microbiome. Conversely, commonly used methods in microbiome literature, such as LEfSe , the Wilcoxon test, and RNA-seq-derived methods, performed best. We conclude that researchers continue using widely adopted non-parametric or RNA-seq DA methods and that further development of compositional methods includes benchmarking against datasets with known biological ground truth. ### Competing Interest Statement The authors have declared no competing interest.
Public
Javier del Campo

Reconstructing the dynamics of past coral endosymbiotic algae communities using coral ancient DNA (coraDNA) #corals #symbiosis #microbiome link.springer.com/article/10.1

SpringerLinkReconstructing the dynamics of past coral endosymbiotic algae communities using coral ancient DNA (coraDNA) - Coral ReefsMost scleractinian corals are under threat from global warming, yet some exhibit remarkable resistance to heat stress. Coral thermal tolerance depends on both the intrinsic properties of the cnidarian host and the physiology of their symbiotic algae (Symbiodiniaceae). Some corals adapt to heat stress by altering their Symbiodiniaceae communities, but these shifts are primarily observed over very short evolutionary timescales, leaving a gap in our understanding of coral holobiont evolution over longer periods. In this study, we combined ancient DNA analysis from a coral core with a metabarcoding approach to reconstruct past Symbiodiniaceae communities associated with a living colony of Porites lobata from New Caledonia over the last century. Using the SymPortal analytical pipeline, we identified 53 ‘defining intragenomic variants’ (DIVs) related to Symbiodiniaceae. The number of Symbiodiniaceae DIVs per sample ranged from 5 to 22 and showed no correlation with the age of the coraDNA sample or the total number of sequences obtained. Our results reveal a generally stable Symbiodiniaceae community associated with P. lobata, dominated by the C15 clade, though substantial shifts in community composition coincided with an extreme warm winter event. Additionally, we detected the presence of Azadinium spinosum (Dinophyceae) in two coraDNA replicates from the same position in the coral core, and sequences of the host P. lobata in a subset of samples. This study lays the groundwork for future research into the evolutionary dynamics of coral holobionts at the colony level across extensive temporal scales.
Public
earthling

Coral Reef Microbiome by Raquel S. Peixoto & Christian R. Voolstra (eds), 2025

Most up-to-date summary of microbiomes of corals and their reefs globally.

Includes scientific aspects of how microbial therapies and knowledge can support restoration/rehabilitation.

Informs science-based decisions that can support practical coral reef restoration and rehabilitation approaches.

link.springer.com/book/10.1007

@bookstodon
#books
#nonfiction
#CoralReefs
#microbiome

Microorganisms, the catalysts of all biogeochemical cycles on Earth, are the origin and essence of life—an invisible yet powerful force sustaining all living organisms. The health of both individual organisms and ecosystems critically depends on functional microbiomes that drive essential processes such as nutrient cycling, pathogen control, detoxification, and resilience. However, like their macroorganism counterparts, these beneficial microbes are vulnerable to environmental changes, and their decline often accelerates ecosystem degradation. Anthropogenic impacts have profoundly altered and often degraded most ecosystems and their microbiomes, with coral reefs being no exception. Stony corals, the foundation of these vibrant ecosystems, are among the most threatened marine organisms. Shifts in coral microbiomes toward dysbiotic (harmful) assemblages are increasingly recognized as both a cause and consequence of coral mortality. In response, active intervention strategies are being developed to restore and rehabilitate degraded microbiomes, aiming to restore and re-establish the beneficial microbial communities that sustain the health of both their host organisms and ecosystems. The success of these approaches depends on our understanding of the distribution, ecological roles, and interactions between corals and their associated microbiomes, as well as how environmental factors influence them and their potential to either amplify or mitigate anthropogenic impacts.
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