Metagenomic analysis decodes the fungal diversity of bio-dynamic preparations

Aim: Biodynamic farming system involves use of 8 different biodynamic preparations (BD 500-BD 507). Multi functionality of any ecosystem is due to its microbial diversity and community composition of microbes. So the present study was aimed to determine the total fungal population viz. unculturable ones, metagenomic analysis was done. Methodology: In the present study, 18S rDNA sequencing of V3-V4 amplicon regions was performed to identify and characterize fungal diversity, which existed in these preparations. Results: Alpha diversity was found to be maximum in BD506 with 868 OTU (operational taxanomic units) and minimum in BD507 with 254 OTU. At phylum level, the most abundant phylum was Ascomycota as recorded in 7 BD preparations with exception in the BD 500 (Unassigned). At genus level highest percentage of OTU abundance was observed for unassigned genus in all BD preparations, except Mortierella in BD 500 and BD 502; Microascus in BD 501 and BD504; Gymnoascus in BD503, Scedosporium in BD 505, Mucor in BD 506 and Hyphopichia in BD 507. On the basis of species diversity, BD502, 503 and 506 showed high percentage of OTU abundance for Mucor racemosus, while Mortierella oligospora was abundant in BD500, Dipodascus geotrichum in BD 501, Kernia pachypleura in BD504, Petriella setifera in BD505 and Hyphopichia burtonii in BD 507. Interpretation: This indicated a unqiue class of fungus predominating each type of BD preparation. Furthermore, a large proportion of unassigned fungi at phylum and genus level were detected in metagenome analysis which might have specific roles in contributing for their overall effectiveness of each kind of BD preparations.

Metagenomic insights into plant growth promoting genes inherent in bacterial endophytes of Emilia sonchifolia (Linn.) DC

Studies on the genome of endophytes reveal the metabolic potential of endophytic microbiome including both culturable and unculturable fractions. The metagenome analysis through the Illumina HiSeq platform gives access to the genetic data encrypted for the molecular machinery, which takes part in plant growth promotion activity of the endophyte in various aspects including production of plant growth hormones and enhancing nutrient availability for the host plant. The present work was undertaken to identify the genes involved in plant growth promotion activities from the endophytes of Emilia sonchifolia(Linn.) DC. through metagenome analysis. Metagenomic studies include the analysis of functional annotations which aid in the detection of biocatalysts taking part in the metabolic pathway of host plants. The annotations of expressed genes in different databases like NCBI Nr, KEGG, eggnog and CAZy resulted in enlisting the vast array of information on the genetic diversity of the endophytic microbiome. The metagenome analysis of endophytic bacteria from the medicinal plant E.sonchifolia unveiled characteristic functional genes involved in plant growth promotion such as nitrogen metabolism (nif) and siderophore production (enterobactin category), ipdC and tnaA (IAA producing), ACC deaminase coding genes (regulation of elevated ethylene levels in host tissues), Mo-Nitrogenase, nitrous-oxide reductase (nosZ), nitrate reductase (narG, napA), nitrite reductase (nirD) (nutrient assimilation and absorption) enterobactin siderophore synthetase components F and D and acid phosphatase genes. This clearly explains the effective plant-microbe relationship and the role of bacterial endophytic microbes in regulating the growth of host plants.

Millimeter-scale vertical partitioning of nitrogen cycling in hypersaline mats reveals prominence of genes encoding multi-heme and prismane proteins

Microbial mats are modern analogues of the first ecosystems on the Earth. As extant representatives of microbial communities where free oxygen may have first been available on a changing planet, they offer an ecosystem within which to study the evolution of biogeochemical cycles requiring and inhibited by oxygen. Here, we report the distribution of genes involved in nitrogen metabolism across a vertical oxygen gradient at 1 mm resolution in a microbial mat using quantitative PCR (qPCR), retro-transcribed qPCR (RT-qPCR) and metagenome sequencing. Vertical patterns in the presence and expression of nitrogen cycling genes, corresponding to oxygen requiring and non-oxygen requiring nitrogen metabolism, could be seen across gradients of dissolved oxygen and ammonium. Metagenome analysis revealed that genes annotated as hydroxylamine dehydrogenase (proper enzyme designation EC 1.7.2.6, hao) and hydroxylamine reductase (hcp) were the most abundant nitrogen metabolism genes in the mat. The recovered hao genes encode hydroxylamine dehydrogenase EC 1.7.2.6 (HAO) proteins lacking the tyrosine residue present in aerobic ammonia oxidizing bacteria (AOB). Phylogenetic analysis confirmed that those proteins were more closely related to ɛHao protein present in Campylobacterota lineages (previously known as Epsilonproteobacteria) rather than oxidative HAO of AOB. BLAST analysis of some transcribed proteins indicated that they likely functioned as a nitrate reductase. The presence of hao sequences related with ɛHao protein, as well as numerous hcp genes encoding a prismane protein, suggest the presence of a nitrogen cycling pathway previously described in Nautilia profundicola as ancestral to the most commonly studied present day nitrogen cycling pathways.

Strain-level analysis reveals the vertical microbial transmission during the life cycle of bumblebee

Background Microbial acquisition and development of the gut microbiota impact the establishment of a healthy host-microbes symbiosis. Compared with other animals, the eusocial bumblebees and honeybees possess a simple, recurring, and similar set of gut microbiota. However, all bee gut phylotypes have high strain-level diversity. Gut communities of different bee species are composed of host-specific groups of strains. The variable genomic regions among strains of the same species often confer critical functional differences, such as carbon source utilization, essential for the natural selection of specific strains. The annual bumblebee colony founded by solitary queens enables tracking the transmission routes of gut bacteria during development stages. Results Here, we first showed the changes in the microbiome of individual bumblebees across their holometabolous life cycle. Some core gut bacteria persist throughout different stages of development. Gut microbiota of newly emerged workers always resembles those of their queens, suggesting a vertical transmission of strains from queens to the newborn workers. We then follow the dynamic changes in the gut community by comparing strain-level metagenomic profiles of queen-worker pairs longitudinally collected across different stages of the nest development. Species composition of both queen and worker shifts with the colony’s growth, and the queen-to-worker vertical inheritance of specific strains was identified. Finally, comparative metagenome analysis showed clear host-specificity for microbes across different bee hosts. Species from honeybees often possess a higher level of strain variation, and they also exhibited more complex gene repertoires linked to polysaccharide digestion. Our results demonstrate bacterial transmission events in bumblebee, highlighting the role of social interactions in driving the microbiota composition. Conclusions By the community-wide metagenomic analysis based on the custom genomic database of bee gut bacteria, we reveal strain transmission events at high resolution and the dynamic changes in community structure along with the colony development. The social annual life cycle of bumblebees is key for the acquisition and development of the gut microbiota. Further studies using the bumblebee model will advance our understanding of the microbiome transmission and the underlying mechanisms, such as strain competition and niche selection.

Rapid metagenomic workflow using annotated 16S RNA dataset

Thanks to the dramatic progress in DNA sequencing technology, it is now possible to decipher sequences in a mixed state. Therefore, the subsequent data analysis has become important, and the demand for metagenomic analysis is very high. Existing metagenomic data analysis workflows for 16S amplicon sequences have been mainly focused on sequences from short reads sequencers, while researchers cannot apply those workflows for sequences from long read sequencers. A practical metagenome workflow for long read sequencers is therefore really needed. In a domestic version of the BioHackathon called BH21.8 held in Aomori, Japan (23-27 August 2021), we first discussed the reproducible workflow for metagenome analysis. We then designed a rapid metagenomic workflow using annotated 16S RNA dataset (Ref16S) and the practical use case for using the workflow developed. Finally, we discussed how to maintain Ref16S and requested Life Science Database Archive in JST NBDC to archive the dataset. After a stimulus discussion in BH21.8, we could clarify the current issues in the metagenomic data analysis. We also could successfully construct a rapid workflow for those data specially from long reads by using newly constructed Ref16S.

Microbial Diversity in the Phyllosphere and Rhizosphere of an Apple Orchard Managed under Prolonged “Natural Farming” Practices

Microbial diversity in an apple orchard cultivated with natural farming practices for over 30 years was compared with conventionally farmed orchards to analyze differences in disease suppression. In this long-term naturally farmed orchard, major apple diseases were more severe than in conventional orchards but milder than in a short-term natural farming orchard. Among major fungal species in the phyllosphere, we found that Aureobasidium pullulans and Cryptococcus victoriae were significantly less abundant in long-term natural farming, while Cladosporium tenuissimum predominated. However, diversity of fungal species in the phyllosphere was not necessarily the main determinant in the disease suppression observed in natural farming; instead, the maintenance of a balanced, constant selection of fungal species under a suitable predominant species such as C. tenuissimum seemed to be the important factors. Analysis of bacteria in the phyllosphere revealed Pseudomonas graminis, a potential inducer of plant defenses, predominated in long-term natural farming in August. Rhizosphere metagenome analysis showed that Cordyceps and Arthrobotrys, fungal genera are known to include insect- or nematode-infecting species, were found only in long-term natural farming. Among soil bacteria, the genus Nitrospira was most abundant, and its level in long-term natural farming was more than double that in the conventionally farmed orchard.

Bacteria-Derived Extracellular Vesicles in Urine as a Novel Biomarker for Gastric Cancer: Integration of Liquid Biopsy and Metagenome Analysis

Early detection is crucial for improving the prognosis of gastric cancer, but there are no non-invasive markers for the early diagnosis of gastric cancer in real clinical settings. Recently, bacteria-derived extracellular vesicles (EVs) emerged as new biomarker resources. We aimed to evaluate the microbial composition in gastric cancer using bacteria-derived EVs and to build a diagnostic prediction model for gastric cancer with the metagenome data. Stool, urine, and serum samples were prospectively collected from 453 subjects (gastric cancer, 181; control, 272). EV portions were extracted from the samples for metagenome analysis. Differences in microbial diversity and composition were analyzed with 16S rRNA gene profiling, using the next-generation sequencing method. Biomarkers were selected using logistic regression models based on relative abundances at the genus level. The microbial composition of healthy groups and gastric cancer patient groups was significantly different in all sample types. The compositional differences of various bacteria, based on relative abundances, were identified at the genus level. Among the diagnostic prediction models for gastric cancer, the urine-based model showed the highest performance when compared to that of stool or serum. We suggest that bacteria-derived EVs in urine can be used as novel metagenomic markers for the non-invasive diagnosis of gastric cancer by integrating the liquid biopsy method and metagenome analysis.