Effects of Waterlogging on Soybean Rhizosphere Bacterial Community Using V4, LoopSeq, and PacBio 16S rRNA Sequence

Utility analyses of AVITI sequencing chemistry

BACKGROUND

DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.

RESULTS

Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.

CONCLUSION

These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Fine-scale characterization of the soybean rhizosphere microbiome via synthetic long reads and avidity sequencing

BACKGROUND

The rhizosphere microbiome displays structural and functional dynamism driven by plant, microbial, and environmental factors. While such plasticity is a well-evidenced determinant of host health, individual and community-level microbial activity within the rhizosphere remain poorly understood, due in part to the insufficient taxonomic resolution achieved through traditional marker gene amplicon sequencing. This limitation necessitates more advanced approaches (e.g., long-read sequencing) to derive ecological inferences with practical application. To this end, the present study coupled synthetic long-read technology with avidity sequencing to investigate eukaryotic and prokaryotic microbiome dynamics within the soybean (Glycine max) rhizosphere under field conditions.

RESULTS

Synthetic long-read sequencing permitted de novo reconstruction of the entire 18S-ITS1-ITS2 region of the eukaryotic rRNA operon as well as all nine hypervariable regions of the 16S rRNA gene. All full-length, mapped eukaryotic amplicon sequence variants displayed genus-level classification, and 44.77% achieved species-level classification. The resultant eukaryotic microbiome encompassed five kingdoms (19 genera) of protists in addition to fungi - a depth unattainable with conventional short-read methods. In the prokaryotic fraction, every full-length, mapped amplicon sequence variant was resolved at the species level, and 23.13% at the strain level. Thirteen species of Bradyrhizobium were thereby distinguished in the prokaryotic microbiome, with strain-level identification of the two Bradyrhizobium species most reported to nodulate soybean. Moreover, the applied methodology delineated structural and compositional dynamism in response to experimental parameters (i.e., growth stage, cultivar, and biostimulant application), unveiled a saprotroph-rich core microbiome, provided empirical evidence for host selection of mutualistic taxa, and identified key microbial co-occurrence network members likely associated with edaphic and agronomic properties.

CONCLUSIONS

This study is the first to combine synthetic long-read technology and avidity sequencing to profile both eukaryotic and prokaryotic fractions of a plant-associated microbiome. Findings herein provide an unparalleled taxonomic resolution of the soybean rhizosphere microbiota and represent significant biological and technological advancements in crop microbiome research.

Utility Analyses of AVITI Sequencing Chemistry

Background

DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.

Results

Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.

Conclusion

These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Uncovering the microbial community structure and physiological profiles of terrestrial mud volcanoes: A comprehensive metagenomic insight towards their trichloroethylene biodegradation potentiality

Mud volcanoes are dynamic geological features releasing methane (CH4), carbon dioxide (CO2), and hydrocarbons, harboring diverse methane and hydrocarbon-degrading microbes. However, the potential application of these microbial communities in chlorinated hydrocarbons bioremediation purposes such as trichloroethylene (TCE) has not yet been explored. Hence, this study investigated the mud volcano's microbial diversity functional potentiality in TCE degradation as well as their eco-physiological profiling using metabolic activity. Geochemical analysis of the mud volcano samples revealed variations in pH, temperature, and oxidation-reduction potential, indicating diverse environmental conditions. The Biolog Ecoplate™ carbon substrates utilization pattern showed that the Tween 80 was highly consumed by mud volcanic microbial community. Similarly, MicroResp® analysis results demonstrated that presence of additive C-substrates condition might enhanced the cellular respiration process within mud-volcanic microbial community. Full-length 16 S rRNA sequencing identified Proteobacteria as the dominant phylum, with genera like Pseudomonas and Hydrogenophaga associated with chloroalkane degradation, and methanotrophic bacteria such as Methylomicrobium and Methylophaga linked to methane oxidation. Functional analysis uncovered diverse metabolic functions, including sulfur and methane metabolism and hydrocarbon degradation, with specific genes involved in methane oxidation and sulfur metabolism. These findings provide insights into the microbial diversity and metabolic capabilities of mud volcano ecosystems, which could facilitate their effective application in the bioremediation of chlorinated compounds.

Uncovering the effect of waterlogging stress on plant microbiome and disease development: current knowledge and future perspectives

Waterlogging is a constant threat to crop productivity and ecological biodiversity. Plants face multiple challenges during waterlogging stress like metabolic reprogramming, hypoxia, nutritional depletion, reduction in gaseous exchange, pH modifications, microbiome alterations and disease promotion all of which threaten plants survival. Due to global warming and climatic change, the occurrence, frequency and severity of flooding has dramatically increased posing a severe threat to food security. Thus, developing innovative crop management technologies is critical for ensuring food security under changing climatic conditions. At present, the top priority among scientists is to find nature-based solutions to tackle abiotic or biotic stressors in sustainable agriculture in order to reduce climate change hazards to the environment. In this regard, utilizing plant beneficial microbiome is one of the viable nature based remedial tool for mitigating abiotic stressors like waterlogging. Beneficial microbiota provides plants multifaceted benefits which improves their growth and stress resilience. Plants recruit unique microbial communities to shield themselves against the deleterious effects of biotic and abiotic stress. In comparison to other stressors, there has been limited studies on how waterlogging stress affects plant microbiome structure and their functional traits. Therefore, it is important to understand and explore how waterlogging alters plant microbiome structure and its implications on plant survival. Here, we discussed the effect of waterlogging stress in plants and its microbiome. We also highlighted how waterlogging stress promotes pathogen occurrence and disease development in plants. Finally, we highlight the knowledge gaps and areas for future research directions on unwiring how waterlogging affects plant microbiome and its functional traits. This will pave the way for identifying resilient microbiota that can be engineered to promote their positive interactions with plants during waterlogging stress.

Short-term continuous monocropping reduces peanut yield mainly via altering soil enzyme activity and fungal community

Continuous monocropping can lead to soil sickness and increase of soil-borne disease, which finally reduces crop yield. Microorganisms benefit plants by increasing nutrient availability, participating in auxin synthesis, and defending against pathogens. However, little is known about the influence of short-term successive peanuts cropping on soil properties, enzyme activities, its yield, plant-associated microbes, and their potential correlations in peanut production. Here, we examined the community structure, composition, network structure and function of microbes in the rhizosphere and bulk soils under different monocropping years. Moreover, we assessed the impact of changes in the soil micro-environment and associated soil microbes on peanut yield. Our results showed that increase of monocropping year significantly decreased most soil properties, enzyme activities and peanut yield (p < 0.05). Principal co-ordinates analysis (PCoA) and analysis of similarities (ANOSIM) indicated that monocropping year significantly influenced the fungal community structure in the rhizosphere and bulk soils (p < 0.01), while had no effect on the bacterial community. With the increase of continuous monocropping year, peanut selectively decreased (e.g., Candidatus_Entotheonella, Bacillus and Bryobacter) or increased (e.g., Nitrospira, Nocardioides, Ensifer, Gaiella, and Novosphingobium) the abundance of some beneficial bacterial genera in the rhizosphere. Continuous monocropping significantly increased the abundance of plant pathogens (e.g., Plectosphaerella, Colletotrichum, Lectera, Gibberella, Metarhizium, and Microdochium) in the rhizosphere and negatively affected the balance of fungal community. Besides, these species were correlated negatively with L-leucine aminopeptidase (LAP) activity. Network co-occurrence analysis showed that continuous monocropping simplified the interaction network of bacteria and fungi. Random forest and partial least squares path modeling (PLS-PM) analysis further showed that fungal community, pathogen abundance, soil pH, and LAP activity negatively affected peanut yield. In conclusion, short-term continuous monocropping decreased LAP activity and increased potential fungal pathogens abundance, leading to reduction of peanut yield.

Microbiologic surveys for Baijiu fermentation are affected by experimental design

The traditional Chinese alcoholic beverage Baijiu is produced by spontaneous fermentation of grains under anaerobic conditions. While numerous studies have used metagenomic technology to investigate the microbiome of Baijiu brewing, the microbial succession mechanism of Baijiu brewing has not been fully clarified, and metagenomic strategies for microecology surveys have not been comprehensively evaluated. Using the fermentation process of strong-flavor Baijiu as a model, we compared the data for bacterial communities based on short read 16S rRNA variable regions, V3-V4, and full-length 16S regions, V1-V9, to whole metagenomic shotgun sequencing (WMS) to measure the effect of technology selection on phylogenetic and functional profiles. The results showed differences in bacterial compositions and their relation to volatiles and physicochemical variables between sequencing methods. Furthermore, the percentage of V3-V4 sequences assigned to species level was higher than the percentage of V1-V9 sequences according to SILVA taxonomy, but lower according to NCBI taxonomy (P < 0.05). In both SILVA and NCBI taxonomies, the relative abundances of bacterial communities at both the genus and family levels were more positively correlated with WMS data in the V3-V4 dataset than in the V1-V9 dataset. The WMS identified changes in abundances of multiple metabolic pathways during fermentation (P < 0.05), including "starch and sucrose metabolism," "galactose metabolism," and "fatty acid biosynthesis." Although functional predictions derived from 16S data show similar patterns to WMS, most metabolic pathway changes are uncorrelated (P > 0.05). This study provided new technical and biological insights into Baijiu production that may assist in selection of methodologies for studies of fermentation systems.

Non-invasive measurements to identify mungbean genotypes for waterlogging tolerance

As the best-fit leguminous crop for intercropping across time and space, mungbean promises to sustain soil health, carbon sequestration, and nutritional security across the globe. However, it is susceptible to waterlogging, a significant constraint that persists during heavy rains. Since the predicted climate change scenario features fewer but more intense rainy days. Hence, waterlogging tolerance in mungbean has been one of the major breeding objectives. The present experiment aimed to employ non-destructive tools to phenotype stress tolerance traits in mungbean genotypes exposed to waterlogging and estimate the association among the traits. A total of 12 mungbean genotypes were used in the present study to assess waterlogging tolerance at the seedling stage. Plant responses to stress were determined non-destructively using normalized difference vegetation index (NDVI) and chlorophyll fluorescence parameters at different time intervals. NDVI and grain yield were positively associated with control (r = 0.64) and stress (r = 0.59). Similarly, chlorophyll fluorescence (quantum yield of PS-II) also had a significant positive association with grain yield under both control (r = 0.52) and stress (r = 0.66) conditions. Hence, it is suggested that NDVI and chlorophyll fluorescence promise to serve as traits for non-destructive phenotyping waterlogging tolerance in mungbean genotypes. With the methods proposed in our study, it is possible to phenotype hundreds of plants for waterlogging tolerance efficiently.

Microbiome structure variation and soybean's defense responses during flooding stress and elevated CO2

Introduction

With current trends in global climate change, both flooding episodes and higher levels of CO2 have been key factors to impact plant growth and stress tolerance. Very little is known about how both factors can influence the microbiome diversity and function, especially in tolerant soybean cultivars. This work aims to (i) elucidate the impact of flooding stress and increased levels of CO2 on the plant defenses and (ii) understand the microbiome diversity during flooding stress and elevated CO2 (eCO2).

Methods

We used next-generation sequencing and bioinformatic methods to show the impact of natural flooding and eCO2 on the microbiome architecture of soybean plants' below- (soil) and above-ground organs (root and shoot). We used high throughput rhizospheric extra-cellular enzymes and molecular analysis of plant defense-related genes to understand microbial diversity in plant responses during eCO2 and flooding.

Results

Results revealed that bacterial and fungal diversity was substantially higher in combined flooding and eCO2 treatments than in non-flooding control. Microbial diversity was soil>root>shoot in response to flooding and eCO2. We found that sole treatment of eCO2 and flooding had significant abundances of Chitinophaga, Clostridium, and Bacillus. Whereas the combination of flooding and eCO2 conditions showed a significant abundance of Trichoderma and Gibberella. Rhizospheric extra-cellular enzyme activities were significantly higher in eCO2 than flooding or its combination with eCO2. Plant defense responses were significantly regulated by the oxidative stress enzyme activities and gene expression of Elongation factor 1 and Alcohol dehydrogenase 2 in floodings and eCO2 treatments in soybean plant root or shoot parts.

Conclusion

This work suggests that climatic-induced changes in eCO2 and submergence can reshape microbiome structure and host defenses, essential in plant breeding and developing stress-tolerant crops. This work can help in identifying core-microbiome species that are unique to flooding stress environments and increasing eCO2.

Silicon Application for the Modulation of Rhizosphere Soil Bacterial Community Structures and Metabolite Profiles in Peanut under Ralstonia solanacearum Inoculation

Silicon (Si) has been shown to promote peanut growth and yield, but whether Si can enhance the resistance against peanut bacterial wilt (PBW) caused by Ralstonia solanacearum, identified as a soil-borne pathogen, is still unclear. A question regarding whether Si enhances the resistance of PBW is still unclear. Here, an in vitro R. solanacearum inoculation experiment was conducted to study the effects of Si application on the disease severity and phenotype of peanuts, as well as the microbial ecology of the rhizosphere. Results revealed that Si treatment significantly reduced the disease rate, with a decrement PBW severity of 37.50% as compared to non-Si treatment. The soil available Si (ASi) significantly increased by 13.62-44.87%, and catalase activity improved by 3.01-3.10%, which displayed obvious discrimination between non-Si and Si treatments. Furthermore, the rhizosphere soil bacterial community structures and metabolite profiles dramatically changed under Si treatment. Three significantly changed bacterial taxa were observed, which showed significant abundance under Si treatment, whereas the genus Ralstonia genus was significantly suppressed by Si. Similarly, nine differential metabolites were identified to involve into unsaturated fatty acids via a biosynthesis pathway. Significant correlations were also displayed between soil physiochemical properties and enzymes, the bacterial community, and the differential metabolites by pairwise comparisons. Overall, this study reports that Si application mediated the evolution of soil physicochemical properties, the bacterial community, and metabolite profiles in the soil rhizosphere, which significantly affects the colonization of the Ralstonia genus and provides a new theoretical basis for Si application in PBW prevention.

Plant genotype influence the structure of cereal seed fungal microbiome

Plant genotype is a crucial factor for the assembly of the plant-associated microbial communities. However, we still know little about the variation of diversity and structure of plant microbiomes across host species and genotypes. Here, we used six species of cereals (Avena sativa, Hordeum vulgare, Secale cereale, Triticum aestivum, Triticum polonicum, and Triticum turgidum) to test whether the plant fungal microbiome varies across species, and whether plant species use different mechanisms for microbiome assembly focusing on the plant ears. Using ITS2 amplicon metagenomics, we found that host species influences the diversity and structure of the seed-associated fungal communities. Then, we tested whether plant genotype influences the structure of seed fungal communities across different cultivars of T. aestivum (Aristato, Bologna, Rosia, and Vernia) and T. turgidum (Capeiti, Cappelli, Mazzancoio, Trinakria, and Timilia). We found that cultivar influences the seed fungal microbiome in both species. We found that in T. aestivum the seed fungal microbiota is more influenced by stochastic processes, while in T. turgidum selection plays a major role. Collectively, our results contribute to fill the knowledge gap on the wheat seed microbiome assembly and, together with other studies, might contribute to understand how we can manipulate this process to improve agriculture sustainability.

16S-FASAS: an integrated pipeline for synthetic full-length 16S rRNA gene sequencing data analysis

Background

The full-length 16S rRNA sequencing can better improve the taxonomic and phylogenetic resolution compared to the partial 16S rRNA gene sequencing. The 16S-FAS-NGS (16S rRNA full-length amplicon sequencing based on a next-generation sequencing platform) technology can generate high-quality, full-length 16S rRNA gene sequences using short-read sequencers, together with assembly procedures. However there is a lack of a data analysis suite that can help process and analyze the synthetic long read data.

Results

Herein, we developed software named 16S-FASAS (16S full-length amplicon sequencing data analysis software) for 16S-FAS-NGS data analysis, which provided high-fidelity species-level microbiome data. 16S-FASAS consists of data quality control, de novo assembly, annotation, and visualization modules. We verified the performance of 16S-FASAS on both mock and fecal samples. In mock communities, we proved that taxonomy assignment by MegaBLAST had fewer misclassifications and tended to find more low abundance species than the USEARCH-UNOISE3-based classifier, resulting in species-level classification of 85.71% (6/7), 85.71% (6/7), 72.72% (8/11), and 70% (7/10) of the target bacteria. When applied to fecal samples, we found that the 16S-FAS-NGS datasets generated contigs grouped into 60 and 56 species, from which 71.62% (43/60) and 76.79% (43/56) were shared with the Pacbio datasets.

Conclusions

16S-FASAS is a valuable tool that helps researchers process and interpret the results of full-length 16S rRNA gene sequencing. Depending on the full-length amplicon sequencing technology, the 16S-FASAS pipeline enables a more accurate report on the bacterial complexity of microbiome samples. 16S-FASAS is freely available for use at https://github.com/capitalbio-bioinfo/FASAS.