Sample preservation and storage significantly impact taxonomic and functional profiles in metaproteomics studies of the human gut microbiome

With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of , as well as . Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at -80 °C should be chosen wherever possible

Water Deficit History Selects Plant Beneficial Soil Bacteria Differently Under Conventional and Organic Farming

Water deficit tolerance is critical for plant fitness and survival, especially when successive drought events happen. Specific soil microorganisms are however able to improve plant tolerance to stresses, such as those displaying a 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity. Microorganisms adapted to dry conditions can be selected by plants over time because of properties such as sporulation, substrate preference, or cell-wall thickness. However, the complexity and interconnection between abiotic factors, like drought or soil management, and biotic factors, like plant species identity, make it difficult to elucidate the general selection processes of such microorganisms. Using a pot experiment in which wheat and barley were grown on conventional and organic farming soils, we determined the effect of water deficit history on soil microorganisms by comparing single and successive events of water limitation. The analysis showed that water deficit strongly impacts the composition of both the total microbial community (16S rRNA genes) and one of ACC deaminase-positive (acdS(+)) microorganisms in the rhizosphere. In contrast, successive dry conditions moderately influence the abundance and diversity of both communities compared to a single dry event. We revealed interactive effects of the farming soil type and the water deficit conditioning treatment. Indeed, possibly due to better nutrient status, plants grown on soils from conventional farming showed higher growth and were able to select more adapted microbial taxa. Some of them are already known for their plant-beneficial properties like the Actinobacteria Streptomyces, but interestingly, some Proteobacteria were also enriched after a water deficit history under conventional farming. Our approach allowed us to identify key microbial taxa promoting drought adaptation of cereals, thus improving our understanding of drought effects on plant-microbe interactions

Sample Preservation and Storage Significantly Impact Taxonomic and Functional Profiles in Metaproteomics Studies of the Human Gut Microbiome

With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from Actinobacteria were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of Bacteroidetes, as well as Proteobacteria. Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at −80 °C should be chosen wherever possible

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (⁠>95% pure, >90% complete) and high-quality (⁠>90% pure, >70% complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (⁠>95% pure, >90% complete) and high-quality (⁠>90% pure, >70% complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method