Taxonomic structure and functional association of foxtail millet root microbiome

Understanding key component factors influencing the processing performance of extruded foxtail millet

This study elucidated the relationship among physicochemical properties of native/extruded foxtail millet, rheological properties, and processing performance of dough made from extruded millet through an in-depth exploration utilizing Pearson correlation, principal component, and K-means cluster analysis. Results showed that crude protein (8.21 %-9.27 %) and lipid content (1.47 %-5.19 %), peak time (4.6-5.3 min), and starch short-range order (R995/1016) of native millet (1.564-1.901) were pivotal in shaping the molecular dynamics, degradation and reorganization processes during extrusion. Notably, heightened levels of crude protein and lipid content, alongside increased short-range order of starch in native millet, hindered the disorder and reorganization of starch, resulting in decreased expansion ratio, degree of gelatinization, water solubility index, and R995/1016 value. These factors significantly impacted dough properties, leading to increased hardness and elasticity while concurrently reducing viscosity. K-means cluster analysis classified 11 foxtail millet varieties into three groups: those exhibiting high hardness (9.26 % protein, 3.77 % lipid and R995/1016 = 1.87) (1), exceptional springiness (8.58 % protein, 1.77 % lipid and R995/1016 = 1.75) (2), and advantageous viscosity (8.46 % protein, 1.82 % lipid and R995/1016 = 1.71) (3).

The role of soil chemical properties and microbial communities on Dendrocalamus brandisii bamboo shoot quality, Yunnan Province, China

Objective

To explore the effects of soil nutrients and microbial communities on the quality of Dendrocalamus brandisii shoots in different regions, providing a scientific basis for their development and utilization.

Methods

Using seven different geographic sources of D. brandisii from Yunnan Province as research subjects, this study employs chemical analysis and high-throughput sequencing to reveal the relationship between soil nutrients, microbial functional groups, and the nutritional quality of bamboo shoots.

Results

The results indicate that there are significant differences in soil nutrient content among the regions (p < 0.05), with bamboo shoots from Baoshan Changning (CN) exhibiting the best overall nutritional quality. The key factors influencing bacterial community changes include pH, available phosphorus (AP), and available potassium (AK). In contrast, the main factors affecting fungal community changes are pH, soil organic matter (SOM), available potassium (AK), and total nitrogen (TN). This version maintains clarity and logical flow, making it easier for readers to understand the different factors influencing bacterial and fungal community changes. The diversity indices of soil microbial communities among different sources of Dendrocalamus brandisii show significant differences (p < 0.05). The dominant groups in the seven regions include Proteobacteria, Acidobacteriota, Actinobacteriota, Chloroflexi, Ascomycota, and Basidiomycota. The soil microbial community in Baoshan Changning (CN) shows significant structural differences compared to the other six regions, with the highest relative abundances of Chloroflexi and Acidobacteriota. In contrast, the highest relative abundance of Proteobacteria is found in Honghe Shiping (SP), while Actinobacteriota has the highest relative abundance in Yuxi Xinping (XP). RDA analysis indicates that soil nutrients (SOM, pH, AP, TN) affect the water content, soluble sugar, and crude fat of bamboo shoots. Additionally, the bacterial communities including Actinobacteriota, Chloroflexi, Patescibacteria, GAL15, and Cyanobacteria influence the water content, soluble sugar, ash content, protein, and lignin of bamboo shoots.

Discussion

In the fungal community, Basidiomycota, Kickxellomycota, Mucoromycota, unclassified-k-Fungi, and Glomeromycota affect the water content and tannin levels in bamboo shoots. In summary, soil nutrients and soil microorganisms are interconnected and work together to influence the quality of bamboo shoots.

Independent origins and non-parallel selection signatures of triclabendazole resistance in Fasciola hepatica

Triclabendazole (TCBZ) is the primary treatment for fascioliasis, a global foodborne zoonosis caused by Fasciola hepatica. Widespread resistance to TCBZ (TCBZ-R) in livestock and a rapid rise in resistant human infections are significant concerns. To understand the genetic basis of TCBZ-R, we sequenced the genomes of 99 TCBZ-sensitive (TCBZ-S) and 210 TCBZ-R adult flukes from 146 bovine livers in Cusco, Peru. We identify genomic regions of high differentiation (FST outliers above the 99.9th percentile) that encod genes involved in the EGFR-PI3K-mTOR-S6K pathway and microtubule function. Transcript expression differences are observed in microtubule-related genes between TCBZ-S and -R flukes, both without drug treatment and in response to treatment. Using only 30 SNPs, it is possible to differentiate between TCBZ-S and -R parasites with ≥75% accuracy. Our outlier loci are distinct from the previously reported TCBZ-R-associated QTLs in the UK, suggesting an independent evolution of resistance alleles. Effective genetics-based TCBZ-R surveillance must consider the heterogeneity of loci under selection across diverse geographical populations.

Chromosome-level genome assembly of the threatened ornamental plant Hibiscus yunnanensis

Hibiscus yunnanensis S.Y. Hu is an endangered species of the genus Hibiscus (Malvaceae), which has high potential economic value. However, the absence of a high-quality reference genome impedes the study of the ecology and molecular biology of H. yunnanensis. Here, we present a high-quality chromosome-level assembly of H. yunnanensis using BGI-DIPSEQ, Nanopore, and Hi-C sequencing. The assembled genome size is 2.2 Gb with a contig N50 of 12.1 Mb and a scaffold N50 of 137.1 Mb. Approximately 99.2% of the assembly is anchored into 17 pseudochromosomes, and a BUSCO analysis indicates a completeness score of 99.6%. Furthermore, we identify 42,085 protein-coding genes, of which 96.4% are functionally annotated. This genome resource provides a foundation for future studies on unique traits, including drought-tolerant, savanna-adapted, and long-flowering traits. Its ability to flower in winter, along with its automatic selfing and lack of delayed inbreeding depression, makes it an excellent model for studying style curvature mechanism and its adaptive significance in the Malvaceae.

Performance evaluation of structural variation detection using DNBSEQ whole-genome sequencing

BACKGROUND

DNBSEQ platforms have been widely used for variation detection, including single-nucleotide variants (SNVs) and short insertions and deletions (INDELs), which is comparable to Illumina. However, the performance and even characteristics of structural variations (SVs) detection using DNBSEQ platforms are still unclear.

RESULTS

In this study, we assessed the detection of SVs using 40 tools on eight DNBSEQ whole-genome sequencing (WGS) datasets and two Illumina WGS datasets of NA12878. Our findings confirmed that the performance of SVs detection using the same tool on DNBSEQ and Illumina datasets was highly consistent, with correlations greater than 0.80 on metrics of number, size, precision and sensitivity, respectively. Furthermore, we constructed a "DNBSEQ" SV set (4,785 SVs) from the DNBSEQ datasets and an "Illumina" SV set (6,797 SVs) from the Illumina datasets. We found that these two SV sets were highly consistent of SV sites and genomic characteristics, including repetitive regions, GC distribution, difficult-to-sequence regions, and gene features, indicating the robustness of our comparative analysis and highlights the value of both platforms in understanding the genomic context of SVs.

CONCLUSIONS

Our study systematically analyzed and characterized germline SVs detected on WGS datasets sequenced from DNBSEQ platforms, providing a benchmark resource for further studies of SVs using DNBSEQ platforms.

A function-based mapping of sensory integration along the cortical hierarchy

Sensory information mainly travels along a hierarchy spanning unimodal to transmodal regions, forming multisensory integrative representations crucial for higher-order cognitive functions. Here, we develop an fMRI based two-dimensional framework to characterize sensory integration based on the anchoring role of the primary cortex in the organization of sensory processing. Sensory magnitude captures the percentage of variance explained by three primary sensory signals and decreases as the hierarchy ascends, exhibiting strong similarity to the known hierarchy and high stability across different conditions. Sensory angle converts associations with three primary sensory signals to an angle representing the proportional contributions of different sensory modalities. This dimension identifies differences between brain states and emphasizes how sensory integration changes flexibly in response to varying cognitive demands. Furthermore, meta-analytic functional decoding with our model highlights the close relationship between cognitive functions and sensory integration, showing its potential for future research of human cognition through sensory information processing.

Stochasticity-dominated rare fungal endophytes contribute to coexistence stability and saponin accumulation in Panax species

Fungal communities inhabiting plant tissues are complex systems of inter-species interactions, consisting of both the "abundant biosphere" and "rare biosphere". However, the composition, assembly, and stability of these subcommunities, as well as their contributions to productivity remain unclear. In this study, the taxonomic and functional composition, co-occurrence, and ecological assembly of abundant and rare fungal subcommunities in different tissues of three Panax species were investigated. Abundant subcommunities were dominated by potential plant pathogens belonging to Microbotryomycetes, while saprotrophic fungi like Agaricomycetes and Mortierellomycetes were more prevalent in rare subcommunities. The rare taxa played a central role in upholding the stability of the fungal networks as driven by Dothideomycetes and Sordariomycetes. Homogeneous selection played a larger role in the assembly of abundant fungal subcommunities compared to the rare counterparts, which was more dominated by stochastically ecological drift in all plant species. Rare biospheres played a larger role in the accumulation of saponin compared to their abundant counterparts, especially in the leaf endosphere, which was mainly affected by environmental factors (Mg, pH, OC, and etc.). Furthermore, we found that rare species belonging to unidentified saprotrophs were associated with saponin formation. This study provides hypotheses for future experiments to understand mechanisms accounting for the variations in the composition and function of rare fungal subcommunities across different Panax species.

C4 cereal and biofuel crop microbiomes

Microbiomes provide multiple life-support functions for plants, including nutrient acquisition and tolerance to abiotic and biotic stresses. Considering the importance of C4 cereal and biofuel crops for food security under climate change conditions, more attention has been given recently to C4 plant microbiome assembly and functions. Here, we review the current status of C4 cereal and biofuel crop microbiome research with a focus on beneficial microbial traits for crop growth and health. We highlight the importance of environmental factors and plant genetics in C4 crop microbiome assembly and pinpoint current knowledge gaps. Finally, we discuss the potential of foxtail millet as a C4 model species and outline future perspectives of C4 plant microbiome research.

Diversity and Activity of Bacteria Cultured from a Cup-The Sponge Calyx nicaeensis

Marine sponges are well-known for hosting rich microbial communities. Sponges are the most prolific source of marine bioactive compounds, which are frequently synthesized by their associated microbiota. Calyx nicaeensis is an endemic Mediterranean sponge with scarce information regarding its (bioactive) secondary metabolites. East Mediterranean specimens of mesophotic C. nicaeensis have never been studied. Moreover, no research has inspected its associated bacteria. Thus, we studied the sponge's bacterial diversity and examined bacterial interspecific interactions in search of a promising antibacterial candidate. Such novel antimicrobial agents are needed since extensive antibiotic use leads to bacterial drug resistance. Bacteria cultivation yielded 90 operational taxonomic units (OTUs). A competition assay enabled the testing of interspecific interactions between the cultured OTUs. The highest-ranked antagonistic bacterium, identified as Paenisporosarcina indica (previously never found in marine or cold habitats), was mass cultured, extracted, and separated using size exclusion and reversed-phase chromatographic methods, guided by antibacterial activity. A pure compound was isolated and identified as 3-oxy-anteiso-C15-fatty acid-lichenysin. Five additional active compounds await final cleaning; however, they are lichenysins and surfactins. These are the first antibacterial compounds identified from either the C. nicaeensis sponge or P. indica bacterium. It also revealed that the genus Bacillus is not an exclusive producer of lichenysin and surfactin.

Engineering agricultural soil microbiomes and predicting plant phenotypes

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

Interpretable machine learning decodes soil microbiome's response to drought stress

BACKGROUND

Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.

RESULTS

This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.

CONCLUSIONS

In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Dissecting the role of soybean rhizosphere-enriched bacterial taxa in modulating nitrogen-cycling functions

Crop roots selectively recruit certain microbial taxa that are essential for supporting their growth. Within the recruited microbes, some taxa are consistently enriched in the rhizosphere across various locations and crop genotypes, while others are unique to specific planting sites or genotypes. Whether these differentially enriched taxa are different in community composition and how they interact with nutrient cycling need further investigation. Here, we sampled bulk soil and the rhizosphere soil of five soybean varieties grown in Shijiazhuang and Xuzhou, categorized the rhizosphere-enriched microbes into shared, site-specific, and variety-specific taxa, and analyzed their correlation with the diazotrophic communities and microbial genes involved in nitrogen (N) cycling. The shared taxa were dominated by Actinobacteria and Thaumarchaeota, the site-specific taxa were dominated by Actinobacteria in Shijiazhuang and by Nitrospirae in Xuzhou, while the variety-specific taxa were more evenly distributed in several phyla and contained many rare operational taxonomic units (OTUs). The rhizosphere-enriched taxa correlated with most diazotroph orders negatively but with eight orders including Rhizobiales positively. Each group within the shared, site-specific, and variety-specific taxa negatively correlated with bacterial amoA and narG in Shijiazhuang and positively correlated with archaeal amoA in Xuzhou. These results revealed that the shared, site-specific, and variety-specific taxa are distinct in community compositions but similar in associations with rhizosphere N-cycling functions. They exhibited potential in regulating the soybean roots' selection for high-efficiency diazotrophs and the ammonia-oxidizing and denitrification processes. This study provides new insights into soybean rhizosphere-enriched microbes and their association with N cycling. KEY POINTS: • Soybean rhizosphere affected diazotroph community and enriched nifH, amoA, and nosZ. • Shared and site- and variety-specific taxa were dominated by different phyla. • Rhizosphere-enriched taxa were similarly associated with N-cycle functions.

Current Advances in the Functional Diversity and Mechanisms Underlying Endophyte-Plant Interactions

Plant phenotype is a complex entity largely controlled by the genotype and various environmental factors. Importantly, co-evolution has allowed plants to coexist with the biotic factors in their surroundings. Recently, plant endophytes as an external plant phenotype, forming part of the complex plethora of the plant microbial assemblage, have gained immense attention from plant scientists. Functionally, endophytes impact the plant in many ways, including increasing nutrient availability, enhancing the ability of plants to cope with both abiotic and biotic stress, and enhancing the accumulation of important plant secondary metabolites. The current state of research has been devoted to evaluating the phenotypic impacts of endophytes on host plants, including their direct influence on plant metabolite accumulation and stress response. However, there is a knowledge gap in how genetic factors influence the interaction of endophytes with host plants, pathogens, and other plant microbial communities, eventually controlling the extended microbial plant phenotype. This review will summarize how host genetic factors can impact the abundance and functional diversity of the endophytic microbial community, how endophytes influence host gene expression, and the host-endophyte-pathogen disease triangle. This information will provide novel insights into how breeders could specifically target the plant-endophyte extended phenotype for crop improvement.

Root microbiota analysis of Oryza rufipogon and Oryza sativa reveals an orientation selection during the domestication process

The root-associated microbiota has a close relation to the life activities of plants, and its composition is affected by the rhizospheric environment and plant genotypes. Rice (Oryza sativa) was domesticated from the ancestor species Oryza rufipogon. Many important agricultural traits and adversity resistance of rice have changed during a long time of natural domestication and artificial selection. However, the influence of rice genotypes on root microbiota in important agricultural traits remains to be explained. In this study, we performed 16S rRNA and internal transcribed spacer (ITS) gene amplicon sequencing to generate bacterial and fungal community profiles of O. rufipogon and O. sativa, both of which were planted in a farm in Guangzhou and had reached the reproductive stage. We compared their root microbiota in detail by alpha diversity, beta diversity, different species, core microbiota, and correlation analyses. We found that the relative abundance of bacteria was significantly higher in the cultivated rice than in the common wild rice, while the relative abundance of fungi was the opposite. Significant differences in agricultural traits between O. rufipogon and O. sativa showed a high correlation with core microorganisms in the two Oryza species, which only existed in either or had obviously different abundance in both two species, indicating that rice genotype/phenotype had a strong influence on recruiting specific microorganisms. Our study provides a theoretical basis for the in-depth understanding of rice root microbiota and the improvement of rice breeding from the perspective of the interaction between root microorganisms and plants.IMPORTANCEPlant root microorganisms play a vital role not only in plant growth and development but also in responding the biotic and abiotic stresses. Oryza sativa is domesticated from Oryza rufipogon which has many excellent agricultural traits especially containing resistance to biotic and abiotic stresses. To improve the yield and resistance of cultivated rice, it is particularly important to deeply research on differences between O. sativa and O. rufipogon and find beneficial microorganisms to remodel the root microbiome of O. sativa.

Soil, rhizosphere, and root microbiome in kiwifruit vine decline, an emerging multifactorial disease

Kiwifruit vine decline syndrome (KVDS) is characterized by severe root system impairment, which leads to irreversible wilting of the canopy. Plants usually collapse rapidly from the appearance of the first aboveground symptoms, without recovery even in the following seasons. The syndrome has been negatively impacting kiwifruit yield in different areas of Italy, the main producing European country, since its first outbreak in 2012. To date, a unique, common causal factor has yet to be found, and the syndrome is referred to as multifactorial. In this article, we investigated the whole biotic community (fungi, bacteria, and oomycetes) associated with the development of KVDS in three different belowground matrices/compartments (soil, rhizosphere, and root). Sampling was performed at both healthy and affected sites located in the main kiwifruit-producing area of Northwestern Italy. To address the multifactorial nature of the syndrome and to investigate the potential roles of abiotic factors in shaping these communities, a physicochemical analysis of soils was also performed. This study investigates the associations among taxonomic groups composing the microbiome and also between biotic and abiotic factors. Dysbiosis was considered as a driving event in shaping KVDS microbial communities. The results obtained from this study highlight the role of the oomycete genus Phytopythium, which resulted predominantly in the oomycete community composition of diseased matrices, though it was also present in healthy ones. Both bacterial and fungal communities resulted in a high richness of genera and were highly correlated to the sampling site and matrix, underlining the importance of multiple location sampling both geographically and spatially. The rhizosphere community associated with KVDS was driven by a dysbiotic process. In addition, analysis of the association network in the diseased rhizosphere revealed the presence of potential cross-kingdom competition for plant-derived carbon between saprobes, oomycetes, and bacteria.

Trichoderma harzianum prevents red kidney bean root rot by increasing plant antioxidant enzyme activity and regulating the rhizosphere microbial community

Root rot is one of the main reasons for yield losses of red kidney bean (Phaseolus vulgaris) production. Pre-inoculation with Trichoderma harzianum can effectively lower the incidence of red kidney bean root rot. In this study, four treatments including CK (control), Fu13 (Fusarium oxysporum), T891 (T. harzianum) and T891 + Fu13 (T. harzianum + F. oxysporum) were arranged in a pot experiment to investigate how T891 affected the incidence and severity of root rot, plant growth, and changes of defense enzyme activity in red kidney bean plants. Community composition and diversity of the rhizosphere microbiota was evaluated through high-throughput sequencing, and co-occurrence network was analyzed. The results showed that when compared to the Fu13 treatment, pre-inoculation with T891 reduced the incidence and severity of red kidney bean root rot by 40.62 and 68.03% (p < 0.05), increased the root length, shoot length, total dry biomass by 48.63, 97.72, 122.17%. Upregulated activity of super-oxide dismutase (SOD), peroxidase (POD), catalase (CAT) by 7.32, 38.48, 98.31% (p < 0.05), and reduced malondialdehyde (MDA) by 23.70% (p < 0.05), respectively. Microbiological analyses also showed that F. oxysporum reduced alpha diversity resulting in alteration the composition of the rhizosphere microbial community in red kidney bean. T891 significantly reduced abundance of F. oxysporum, allowing the enrichment of potentially beneficial bacteria Porphyrobacter (ASV 46), Lysobacter (ASV 85), Microbacteriaceae (ASV 105), and Gemmatimonas (ASV 107), resulting in a more stable structure of the microbial network. The results of random forest analysis further revealed that ASV 46 (Porphyrobacter) was the primary influencing factor for the incidence of root rot after inoculation with T891, while ASV 85 (Lysobacter) was the primary influencing factor for the biomass of red kidney bean. In conclusion, T. harzianum promotes the growth of red kidney bean and inhibits root rot by improving plant antioxidant enzyme activity and regulating the rhizosphere microbial community.

Single-cell multi-omics analysis of lineage development and spatial organization in the human fetal cerebellum

Human cerebellum encompasses numerous neurons, exhibiting a distinct developmental paradigm from cerebrum. Here we conducted scRNA-seq, scATAC-seq and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13 to 18 to explore the emergence of cellular diversity and developmental programs in the developing human cerebellum. We identified transitory granule cell progenitors that are conserved across species. Special patterns in both granule cells and Purkinje cells were dissected multidimensionally. Species-specific gene expression patterns of cerebellar lobes were characterized and we found that PARM1 exhibited inconsistent distribution in human and mouse granule cells. A novel cluster of potential neuroepithelium at the rhombic lip was identified. We also resolved various subtypes of Purkinje cells and unipolar brush cells and revealed gene regulatory networks controlling their diversification. Therefore, our study offers a valuable multi-omics landscape of human fetal cerebellum and advances our understanding of development and spatial organization of human cerebellum.

Profiling pathogenic protozoan and their functional pathways in wastewater using 18S rRNA and shotgun metagenomics

Despite extensive research, little is known about the composition of eukaryotic protists in environmental samples. This is due to low parasite concentrations, the complexity of parasite diversity, and a lack of suitable reference databases and standardized protocols. To bridge this knowledge gap, this study used 18S rRNA short amplicon and shotgun metagenomic sequencing approaches to profile protozoan microbial communities as well as their functional pathways in treated and untreated wastewater samples collected from different regions of South Africa. Results demonstrated that protozoan diversity (Shannon index P-value = 0.03) and taxonomic composition (PERMANOVA, P-value = 0.02) was mainly driven by the type of wastewater samples (treated & untreated) and geographic location. However, these WWTPs were also found to contain a core community of protozoan parasites. The untreated wastewater samples revealed a predominant presence of free-living, parasitic, and potentially pathogenic protists typically found in humans and animals, ranging from Alveolata (27 %) phylum (Apicomplexa and Ciliophora) to Excavata (3.88 %) (Discoba and Parasalia) and Amoebozoa (2.84 %) (Entamoeba and Acanthamoeba). Shotgun metagenomics analyses in a subset of the untreated wastewater samples confirmed the presence of public health-importance protozoa, including Cryptosporidium species (3.48 %), Entamoeba hystolitica (6.58 %), Blastocystis hominis (2.91 %), Naegleria gruberi (2.37 %), Toxoplasma gondii (1.98 %), Cyclospora cayetanensis (1.30 %), and Giardia intestinalis (0.31 %). Virulent gene families linked to pathogenic protozoa, such as serine/threonine protein phosphatase and mucin-desulfating sulfatase were identified. Additionally, enriched pathways included thiamine diphosphate biosynthesis III, heme biosynthesis, Methylerythritol 4-Phosphate Pathway, methyl erythritol phosphate (MEP), and pentose phosphate pathways. These findings suggest that protozoan pathogens may possess metabolic and growth potential within WWTPs, posing a severe risk of transmission to humans and animals if inadequately disinfected before release. This study provides a baseline for the future investigation of diverse protozoal communities in wastewater, which are of public health importance.

Effects of intercropping teak with Alpinia katsumadai Hayata and Amomum longiligulare T.L. Wu on rhizosphere soil nutrients and bacterial community diversity, structure, and network

Teak is a precious hardwood species in tropical and subtropical regions with a long growth cycle and slow economic returns. Intercropping medicinal plants is an effective method for obtaining early returns during the growth period of teak. However, currently, we lack sufficient knowledge about the impact of intercropping on the soil microenvironment, especially on rhizosphere soil bacterial communities. We selected two medicinal plants Alpinia katsumadai Hayata and Amomum longiligulare T.L. Wu, for an intercropping experiment with teak, and the non-intercropping teak forest area was used for comparison. By collecting soil rhizosphere samples and conducting 16S rDNA sequencing and property analysis, we aimed to investigate the influence of teak intercropping on soil microbial communities. The results showed that intercropping significantly improved soil nutrients contents, such as soil organic matter, soil total potassium and soil available nitrogen, and significantly altered bacterial community structure. Co-occurrence network analysis revealed that intercropping tightened the connections of the soil bacterial network and increased its complexity (by increasing the number of nodes and the proportion of positive edges). Teak intercropping with Amomum longiligulare T.L. Wu resulted in tighter network connections than teak intercropping with A. katsumadai Hayata. Changes in the soil bacterial community structure may related to environmental factors such as total potassium content and pH. These results demonstrated that the introduction of medicinal plants exerts a significant impact on the soil bacterial community of teak, fostering the enrichment of specific bacterial taxa (such as Firmicutes and Methylomirabilota), and makes the rhizosphere bacterial network denser and more complex. This study provides valuable insights for the management of teak plantations.

Fungal endophyte promotes plant growth and disease resistance of Arachis hypogaea L. by reshaping the core root microbiome under monocropping conditions

Fungal endophytes play critical roles in helping plants adapt to adverse environmental conditions. The root endophyte Phomopsis liquidambaris can promote the growth and disease control of peanut plants grown under monocropping systems; however, how such beneficial traits are produced is largely unknown. Since the plant endophytic microbiome is directly linked to plant growth and health, and the composition of which has been found to be potentially influenced by microbial inoculants, this study aims to clarify the roles of root endophytic bacterial communities in P. liquidambaris-mediated plant fitness enhancement under monocropping conditions. Here, we found that P. liquidambaris inoculation induced significant changes in the root bacterial community: enriching some beneficial bacteria such as Bradyrhizobium sp. and Streptomyces sp. in the roots, and improving the core microbial-based interaction network. Next, we assembled and simplified a synthetic community (SynII) based on P. liquidambaris-derived key taxa, including Bacillus sp. HB1, Bacillus sp. HB9, Burkholderia sp. MB7, Pseudomonas sp. MB2, Streptomyces sp. MB6, and Bradyrhizobium sp. MB15. Furthermore, the application of the simplified synthetic community suppressed root rot caused by Fusarium oxysporum, promoted plant growth, and increased peanut yields under continuous monocropping conditions. The resistance of synII to F. oxysporum is related to the increased activity of defense enzymes. In addition, synII application significantly increased shoot and root biomass, and yield by 35.56%, 81.19%, and 34.31%, respectively. Collectively, our results suggest that the reshaping of root core microbiota plays an important role in the probiotic-mediated adaptability of plants under adverse environments.

Association between ambient air pollution exposure in pregnant women with antiphospholipid syndrome in Nanjing, China

Antenatal exposure to air pollutants is thought to be associated with a variety of maternal blood markers as well as adverse birth outcomes. However, the dysgenic influence of air pollutants on the antiphospholipid syndrome (APS) in mothers and their pregnancy outcomes remains unclear. In the current study, 371 mother-infant pairs (189 healthy: 182 APS) from Nanjing Maternal and Child Health Hospital as well as air pollutants concentration from their living environment were used to investigate correlations between air pollution with maternal blood indicators and fetal birth weight in the groups of APS and healthy mothers. Generalized linear model was used to evaluate the contributions of air pollutant exposure during pregnancy to the blood indicators variation. The relationships between birth weight with specific air pollutant and blood index were analyzed using ridge regression. Results showed that APS fetal birth weight was significantly impacted by air pollutant exposure during pregnancy, in particular, the birth weight decreased significantly along with increasing fine particulate matter 2.5 (PM2.5) and fine particulate matter 10 (PM10) exposure concentrations throughout pregnancy. In contrast, birth weight increased significantly with sulfur dioxide (SO2) exposure. In addition, APS-related blood indicators comprised of platelet distribution width (PDW), total bilirubin (TBIL), mean platelet volume (MPV), platelet-larger cell ratio (P_LCR), homocysteine (HCY), alkaline phosphatase (ALP), direct bilirubin (DBIL), basophilic granulocyte (BAS), platelet thrombocytocrit (PCT), preprandial glucose levels (OGTT0), monocytes (MON), and monocytes ratio (MON_ratio) were also strongly related with prenatal exposure to PM2.5 and PM10, in which PDW levels showed most strongly negative impaction on fetal birth weight. Together, we showed that prenatal exposure to air pollutant (PM2.5 and PM10) may exacerbate the poor birth outcomes of low birth weight by impacting APS maternal blood indicators especially for PDW.

Composition, functional, and niche differentiation of fungal communities in the rhizosphere and root endosphere of Cinnamomum migao in southwestern China

AIMS

Roots are key parts of plant material circulation and energy flow, creating two distinct niches for the plant microbiome, such as the rhizosphere and root endosphere, which interact to maintain plant growth and health. In this study, two niches of plant rhizosphere and root endosphere were selected to study the composition and differences of fungi communities in order to better understand how differences in the structure and function of plant fungi communities affect plant health.

METHODS AND RESULTS

The community structure, diversity, and ecological function of fungi in the rhizosphere and root endosphere of Cinnamomum migao were studied using high-throughput sequencing techniques, traditional culture methods, and the FUNGuild database. The results of the analysis of alpha diversity showed that the diversity of rhizosphere fungal communities in C. migao was much higher than that of root endosphere communities. Some similarities and differences were noted between the two niche fungal communities, and some fungi had niche preferences. Functional prediction results demonstrated that fungi in the rhizosphere and root endosphere adopt multiple trophic modes, mostly saprophytic fungi.

CONCLUSIONS

This study provided a basis for an in-depth understanding of the structural variation, niche differentiation, and function of plant root-related fungal microbiota. We believe that it could provide guidance on the subsequent development of beneficial fungi.

Different techniques reveal the difference of community structure and function of fungi from root and rhizosphere of Salvia miltiorrhiza Bunge

Fungi have essential functions in plant health and performance. However, the plant-associated functions of many cultured fungi have not been established in detail. Here, the fungal species diversity in Salvia miltiorrhiza roots and rhizosphere was assessed for the first time using culturomics and high-throughput sequencing. We present a comprehensive functional metagenomic analysis of these fungi and verified activity of cellulase and chitinase predicted in the metagenomic analysis. We first collected and cultured fungi from the root and rhizosphere of S. miltiorrhiza. We found 92 species across 37 families and five phyla, with Ascomycota being dominant. Many rDNA internal transcribed spacer sequences could not be assigned to lower taxonomic levels. There were 19 genera of endophytic fungi and 37 genera of rhizosphere fungi. The culturomics approach had lower taxonomic diversity than high-throughput sequencing, but some fungi were only found in cultures. Structural analyses indicated that the dominant species differed in cultured and non-cultured samples at other levels, apart from the phylum level. Functional analysis mapped 223 carbohydrate enzyme families and 393 pathways in the CAZy and KEGG databases, respectively. The most abundant families were glycoside hydrolases and those involved in carbohydrate metabolism. As predicted by metagenomics, we experimentally verified cellulase and chitinase activity for 29 and 74 fungi, respectively. We provide the first evidence of biomass recycling by fungi that are associated with plants. Culturing is essential to reveal the hidden microbial community and critical functions in plant-microbe interactions.

Exploring the Core Bacteria and Functional Traits in Pecan (Carya illinoinensis) Rhizosphere

Pecan (Carya illinoinensis) and Chinese hickory (Carya cathayensis) are important commercially cultivated nut trees. They are phylogenetically closely related plants; however, they exhibit significantly different phenotypes in response to abiotic stress and development. The rhizosphere selects core microorganisms from bulk soil, playing a pivotal role in the plant's resistance to abiotic stress and growth. In this study, we used metagenomic sequencing to compare the selection capabilities of seedling pecan and seedling hickory at taxonomic and functional levels in bulk soil and the rhizosphere. We observed that pecan has a stronger capacity to enrich rhizosphere plant-beneficial microbe bacteria (e.g., Rhizobium, Novosphingobium, Variovorax, Sphingobium, and Sphingomonas) and their associated functional traits than hickory. We also noted that the ABC transporters (e.g., monosaccharide transporter) and bacterial secretion systems (e.g., type IV secretion system) are the core functional traits of pecan rhizosphere bacteria. Rhizobium and Novosphingobium are the main contributors to the core functional traits. These results suggest that monosaccharides may help Rhizobium to efficiently enrich this niche. Novosphingobium may use a type IV secretion system to interact with other bacteria and thereby influence the assembly of pecan rhizosphere microbiomes. Our data provide valuable information to guide core microbial isolation and expand our knowledge of the assembly mechanisms of plant rhizosphere microbes. IMPORTANCE The rhizosphere microbiome has been identified as a fundamental factor in maintaining plant health, helping plants to fight the deleterious effects of diseases and abiotic stresses. However, to date, studies on the nut tree microbiome have been scarce. Here, we observed a significant "rhizosphere effect" on the seedling pecan. We furthermore demonstrated the core rhizosphere microbiome and function in the seedling pecan. Moreover, we deduced possible factors that help the core bacteria, such as Rhizobium, to efficiently enrich the pecan rhizosphere and the importance of the type IV system for the assembly of pecan rhizosphere bacterial communities. Our findings provide information for understanding the mechanism of the rhizosphere microbial community enrichment process.

Biodiversity of the beneficial soil-borne fungi steered by Trichoderma-amended biofertilizers stimulates plant production

The soil microbiota is critical to plant performance. Improving the ability of plant-associated soil probiotics is thus essential for establishing dependable and sustainable crop yields. Although fertilizer applications may provide an effective way of steering soil microbes, it is still unknown how the positive effects of soil-borne probiotics can be maximized and how their effects are mediated. This work aims to seek the ecological mechanisms involved in cabbage growth using bio-organic fertilizers. We conducted a long-term field experiment in which we amended soil with non-sterilized organic or sterilized organic fertilizer either containing Trichoderma guizhouense NJAU4742 or lacking this inoculum and tracked cabbage plant growth and the soil fungal community. Trichoderma-amended bio-organic fertilizers significantly increased cabbage plant biomass and this effect was attributed to changes in the resident fungal community composition, including an increase in the relative abundance and number of indigenous soil growth-promoting fungal taxa. We specifically highlight the fundamental role of the biodiversity and population density of these plant-beneficial fungal taxa in improving plant growth. Together, our results suggest that the beneficial effects of bio-organic fertilizer seem to be a combination of the biological inoculum within the organic amendment as well as the indirect promotion through effects on the diversity and composition of the soil resident plant-beneficial fungal microbiome.

Additive fungal interactions drive biocontrol of Fusarium wilt disease

Host-associated fungi can help protect plants from pathogens, and empirical evidence suggests that such microorganisms can be manipulated by introducing probiotic to increase disease suppression. However, we still generally lack the mechanistic knowledge of what determines the success of probiotic application, hampering the development of reliable disease suppression strategies. We conducted a three-season consecutive microcosm experiment in which we amended banana Fusarium wilt disease-conducive soil with Trichoderma-amended biofertilizer or lacking this inoculum. High-throughput sequencing was complemented with cultivation-based methods to follow changes in fungal microbiome and explore potential links with plant health. Trichoderma application increased banana biomass by decreasing disease incidence by up to 72%, and this effect was attributed to changes in fungal microbiome, including the reduction in Fusarium oxysporum density and enrichment of pathogen-suppressing fungi (Humicola). These changes were accompanied by an expansion in microbial carbon resource utilization potential, features that contribute to disease suppression. We further demonstrated the disease suppression actions of Trichoderma-Humicola consortia, and results suggest niche overlap with pathogen and induction of plant systemic resistance may be mechanisms driving the observed biocontrol effects. Together, we demonstrate that fungal inoculants can modify the composition and functioning of the resident soil fungal microbiome to suppress soilborne disease.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Core root-associated prokaryotic community and its relationship to host traits across wheat varieties

The root-associated microbiomes play important roles in plant growth. However, it is largely unknown how wheat variety evolutionary relatedness shapes each subcommunity in the root microbiome and, in turn, how these microbes affect wheat yield and quality. Here we studied the prokaryotic communities associated with the rhizosphere and root endosphere in 95 wheat varieties at regreening and heading stages. The results indicated that the less diverse but abundant core prokaryotic taxa occurred among all varieties. Among these core taxa, we identified 49 and 108 heritable amplicon sequence variants, whose variations in relative abundances across the root endosphere and rhizosphere samples were significantly affected by wheat variety. The significant correlations between phylogenetic distance of wheat varieties and prokaryotic community dissimilarity were only observed in non-core and abundant subcommunities in the endosphere samples. Again, wheat yield was only significantly associated with root endosphere microbiota at the heading stage. Additionally, wheat yield could be predicted using the total abundance of 94 prokaryotic taxa as an indicator. Our results demonstrated that the prokaryotic communities in the root endosphere had higher correlations with wheat yield and quality than those in the rhizosphere; thus, managing root endosphere microbiota, especially core taxa, through agronomic practices and crop breeding, is important for promoting wheat yield and quality.

A Synthetic Microbial Community of Plant Core Microbiome Can Be a Potential Biocontrol Tool

Microbes are accepted as the foremost drivers of the rhizosphere ecology that influences plant health in direct or indirect ways. In recent years, the rapid development of gene sequencing technology has greatly facilitated the study of plant microbiome structure and function, and various plant-associated microbiomes have been categorized. Additionally, there is growing research interest in plant-disease-related microbes, and some specific microflora beneficial to plant health have been identified. This Review discusses the plant-associated microbiome's biological control pathways and functions to modulate plant defense against pathogens. How do plant microbiomes enhance plant resistance? How does the plant core microbiome-associated synthetic microbial community (SynCom) improve plant health? This Review further points out the primary need to develop smart agriculture practices using SynComs against plant diseases. Finally, this Review provides ideas for future opportunities in plant disease control and mining new microbial resources.

Integrating nanotechnology with plant microbiome for next-generation crop health

Nanotechnology has enormous potential for sustainable agriculture, such as improving nutrient use efficiency, plant health, and food production. Nanoscale modulation of the plant-associated microbiota offers an additional valuable opportunity to increase global crop production and ensure future food and nutrient security. Nanomaterials (NMs) applied to agricultural crops can impact plant and soil microbiota, which offers valuable services to host plants, including the acquisition of nutrients, abiotic stress tolerance, and disease suppression. Dissecting the complex interactions between NMs and plants by integrating multi-omic approaches is providing new insights into how NMs can activate host responses and functionality as well as influence native microbial communities. Such nexus and moving beyond descriptive microbiome studies to hypothesis-driven research will foster microbiome engineering and open up opportunities for the development of synthetic microbial communities to provide agronomic solutions. Herein, we first summarize the significant role of NMs and the plant microbiome in crop productivity and then focus on NMs effects on plant-associated microbiota. We outline three urgent priority research areas and call for a transdisciplinary collaborative approach, involving plant scientists, soil scientists, environmental scientists, ecologists, microbiologists, taxonomists, chemists, physicists, and stakeholders, to advance nano-microbiome research. Detailed understanding of the nanomaterial-plant-microbiome interactions and the mechanisms underlying NMs-mediated shifts in the microbiome assembly and functions may help to exploit the services of both nano-objects and microbiota for next-generation crop health.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

Core rhizosphere microbiome of Panax notoginseng and its associations with belowground biomass and saponin contents

The core rhizosphere microbiome is critical for plant fitness. However, its contribution to the belowground biomass and saponin contents of Panax notoginseng remains unclear. High-throughput sequencing of amplicon and metagenome was performed to obtain the microbiome profiles and functional traits in P. notoginseng rhizosphere across a large spatial scale. We obtained 639 bacterial and 310 fungal core OTUs, which were mainly affected by soil pH and organic matter (OM). The core taxa were grouped into four ecological clusters (i.e. high pH, low pH, high OM and low OM) for sharing similar habitat preferences. Furthermore, structural equation modelling (SEM) and correlation analyses revealed that the diversity and composition of core microbiomes, as well as the metagenome-derived microbial functions, were related to belowground biomass and saponin contents. Key microbial genera related to the two plant indicators were also identified. In short, this study explored the main driving environmental factors of core microbiomes in the P. notoginseng rhizosphere and revealed that the core microbiomes and microbial functions potentially contributed to the belowground biomass and saponin contents of the plant. This work may enhance our understanding of interactions between microbes and perennial plants and improve our ability to manage root microbiota for the sustainable production of herbal medicine.

Taxonomical and functional bacterial community profiling in disease-resistant and disease-susceptible soybean cultivars

Highly varied bacterial communities inhabiting the soybean rhizosphere perform important roles in its growth and production; nevertheless, little is known about the changes that occur in these communities under disease-stress conditions. The present study investigated the bacterial diversity and their metabolic profile in the rhizosphere of disease-resistant (JS-20-34) and disease-susceptible (JS-335) soybean (Glycine max (L.) Merr.) cultivars using 16S rRNA amplicon sequencing and community-level physiological profiling (CLPP). In disease-resistant soybean (AKADR) samples, the most dominating phyla were Actinobacteria (40%) followed by Chloroflexi (24%), Proteobacteria (20%), and Firmicutes (12%), while in the disease-susceptible (AKADS) sample, the most dominating phyla were Proteobacteria (35%) followed by Actinobacteria (27%) and Bacteroidetes (17%). Functional profiling of bacterial communities was done using the METAGENassist, and PICRUSt2 software, which shows that AKADR samples have more ammonifying, chitin degrading, nitrogen-fixing, and nitrite reducing bacteria compared to AKADS rhizosphere samples. The bacterial communities present in disease-resistant samples were significantly enriched with genes involved in nitrogen fixation, carbon fixation, ammonification, denitrification, and antibiotic production. Furthermore, the CLPP results show that carbohydrates and carboxylic acids were the most frequently utilized nutrients by the microbes. The principal component analysis (PCA) revealed that the AKADR soils had higher functional activity (strong association with the Shannon-Wiener index, richness index, and hydrocarbon consumption) than AKADS rhizospheric soils. Overall, our findings suggested that the rhizosphere of resistant varieties of soybean comprises of beneficial bacterial population over susceptible varieties.

Multi-omics intervention in Setaria to dissect climate-resilient traits: Progress and prospects

Millets constitute a significant proportion of underutilized grasses and are well known for their climate resilience as well as excellent nutritional profiles. Among millets, foxtail millet (Setaria italica) and its wild relative green foxtail (S. viridis) are collectively regarded as models for studying broad-spectrum traits, including abiotic stress tolerance, C₄ photosynthesis, biofuel, and nutritional traits. Since the genome sequence release, the crop has seen an exponential increase in omics studies to dissect agronomic, nutritional, biofuel, and climate-resilience traits. These studies have provided first-hand information on the structure, organization, evolution, and expression of several genes; however, knowledge of the precise roles of such genes and their products remains elusive. Several open-access databases have also been instituted to enable advanced scientific research on these important crops. In this context, the current review enumerates the contemporary trend of research on understanding the climate resilience and other essential traits in Setaria, the knowledge gap, and how the information could be translated for the crop improvement of related millets, biofuel crops, and cereals. Also, the review provides a roadmap for studying other underutilized crop species using Setaria as a model.

Differential microbial assembly processes and co-occurrence networks in the soil-root continuum along an environmental gradient

Microorganisms of the soil-root continuum play key roles in ecosystem function. The Loess Plateau is well known for its severe soil erosion and thick loess worldwide, where mean annual precipitation (MAP) and soil nutrients decrease from the southeast to the northwest. However, the relative influence of environmental factors on the microbial community in four microhabitats (bulk soil, rhizosphere, rhizoplane, and endosphere) in the soil-root continuum along the environmental gradient in the Loess Plateau remains unclear. In this study, we investigated 82 field sites from warm-temperate to desert grasslands across the Loess Plateau, China, to assess the bacterial diversity, composition, community assembly, and co-occurrence networks in the soil-root continuum along an environmental gradient using bacterial 16S recombinant DNA amplicon sequencing. We discovered that the microhabitats explained the largest source of variations in the bacterial diversity and community composition in this region. Environmental factors (e.g., MAP, soil organic carbon, and pH) impacted the soil, rhizosphere, and rhizoplane bacterial communities, but their effects on the bacterial community decreased with increased proximity to roots from the soil to the rhizoplane, and the MAP enlarged the dissimilarity of microbial communities from the rhizosphere and rhizoplane to bulk soil. Additionally, stochastic assembly processes drove the endosphere communities, whereas the soil, rhizosphere, and rhizoplane communities were governed primarily by the variable selection of deterministic processes, which showed increased importance from warm-temperate to desert grasslands. Moreover, the properties of the microbial networks in the rhizoplane community indicate more stable networks in desert grasslands, likely conferring the resistance of microbial communities in higher stress environments. Collectively, our results showed that the bacterial communities in the soil-root continuum had different sensitivities and assembly mechanisms along an environmental gradient. These patterns are shaped simultaneously by the intertwined dimensions of proximity to roots and environmental stress change in the Loess Plateau.

Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth

Microbial communities in the rhizosphere are distinct from those in soils and are influenced by stochastic and deterministic processes during plant development. These communities contain bacteria capable of promoting growth in host plants through various strategies. While some interactions are characterized in mechanistic detail using model systems, others can be inferred from culture-independent methods, such as 16S amplicon sequencing, using machine learning methods that account for this compositional data type. To characterize assembly processes and identify community members associated with plant growth amid the spatiotemporal variability of the rhizosphere, we grew Brassica rapa in a greenhouse time series with amended and reduced microbial treatments. Inoculation with a native soil community increased plant leaf area throughout the time series by up to 28%. Despite identifying spatially and temporally variable amplicon sequence variants (ASVs) in both treatments, inoculated communities were more highly connected and assembled more deterministically overall. Using a generalized linear modeling approach controlling for spatial variability, we identified 43 unique ASVs that were positively or negatively associated with leaf area, biomass, or growth rates across treatments and time stages. ASVs of the genus Flavobacterium dominated rhizosphere communities and showed some of the strongest positive and negative correlations with plant growth. Members of this genus, and growth-associated ASVs more broadly, exhibited variable connectivity in networks independent of growth association (positive or negative). These findings suggest host-rhizobacterial interactions vary temporally at narrow taxonomic scales and present a framework for identifying rhizobacteria that may work independently or in concert to improve agricultural yields.

IMPORTANCE The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques. We identified 43 specific bacterial sequences associated with the growth of the plant Brassica rapa in different soil microbial treatments and at different stages of plant development. Most associations between bacterial abundances and plant growth were positive, although similar bacterial groups sometimes had different effects on growth. Why this happens will require more research, but overall, this study provides a way to identify native bacteria from plant roots that might be isolated and applied to boost agricultural yields.

Genomic architecture of fetal central nervous system anomalies using whole-genome sequencing

Structural anomalies of the central nervous system (CNS) are one of the most common fetal anomalies found during prenatal imaging. However, the genomic architecture of prenatal imaging phenotypes has not yet been systematically studied in a large cohort. Patients diagnosed with fetal CNS anomalies were identified from medical records and images. Fetal samples were subjected to low-pass and deep whole-genome sequencing (WGS) for aneuploid, copy number variation (CNV), single-nucleotide variant (SNV, including insertions/deletions (indels)), and small CNV identification. The clinical significance of variants was interpreted based on a candidate gene list constructed from ultrasound phenotypes. In total, 162 fetuses with 11 common CNS anomalies were enrolled in this study. Primary diagnosis was achieved in 62 cases, with an overall diagnostic rate of 38.3%. Causative variants included 18 aneuploids, 17 CNVs, three small CNVs, and 24 SNVs. Among the 24 SNVs, 15 were novel mutations not reported previously. Furthermore, 29 key genes of diagnostic variants and critical genes of pathogenic CNVs were identified, including five recurrent genes: i.e., TUBA1A, KAT6B, CC2D2A, PDHA1, and NF1. Diagnostic variants were present in 34 (70.8%) out of 48 fetuses with both CNS and non-CNS malformations, and in 28 (24.6%) out of 114 fetuses with CNS anomalies only. Hypoplasia of the cerebellum (including the cerebellar vermis) and holoprosencephaly had the highest primary diagnosis yields (>70%), while only four (11.8%) out of 34 neural tube defects achieved genetic diagnosis. Compared with the control group, rare singleton loss-of-function variants (SLoFVs) were significantly accumulated in the patient cohort.

Bioprospecting Microbiome for Soil and Plant Health Management Amidst Huanglongbing Threat in Citrus: A Review

Microorganisms have dynamic and complex interactions with their hosts. Diverse microbial communities residing near, on, and within the plants, called phytobiome, are an essential part of plant health and productivity. Exploiting citrus-associated microbiomes represents a scientific approach toward sustained and environment-friendly module of citrus production, though periodically exposed to several threats, with Huanglongbing (HLB) predominantly being most influential. Exploring the composition and function of the citrus microbiome, and possible microbial redesigning under HLB disease pressure has sparked renewed interest in recent times. A concise account of various achievements in understanding the citrus-associated microbiome, in various niche environments viz., rhizosphere, phyllosphere, endosphere, and core microbiota alongside their functional attributes has been thoroughly reviewed and presented. Efforts were also made to analyze the actual role of the citrus microbiome in soil fertility and resilience, interaction with and suppression of invading pathogens along with native microbial communities and their consequences thereupon. Despite the desired potential of the citrus microbiota to counter different pathogenic diseases, utilizing the citrus microbiome for beneficial applications at the field level is yet to be translated as a commercial product. We anticipate that advancement in multiomics technologies, high-throughput sequencing and culturing, genome editing tools, artificial intelligence, and microbial consortia will provide some exciting avenues for citrus microbiome research and microbial manipulation to improve the health and productivity of citrus plants.

Deletion and tandem duplications of biosynthetic genes drive the diversity of triterpenoids in Aralia elata

Araliaceae species produce various classes of triterpene and triterpenoid saponins, such as the oleanane-type triterpenoids in Aralia species and dammarane-type saponins in Panax, valued for their medicinal properties. The lack of genome sequences of Panax relatives has hindered mechanistic insight into the divergence of triterpene saponins in Araliaceae. Here, we report a chromosome-level genome of Aralia elata with a total length of 1.05 Gb. The loss of 12 exons in the dammarenediol synthase (DDS)-encoding gene in A. elata after divergence from Panax might have caused the lack of dammarane-type saponin production, and a complementation assay shows that overexpression of the PgDDS gene from Panax ginseng in callus of A. elata recovers the accumulation of dammarane-type saponins. Tandem duplication events of triterpene biosynthetic genes are common in the A. elata genome, especially for AeCYP72As, AeCSLMs, and AeUGT73s, which function as tailoring enzymes of oleanane-type saponins and aralosides. More than 13 aralosides are de novo synthesized in Saccharomyces cerevisiae by overexpression of these genes in combination. This study sheds light on the diversity of saponins biosynthetic pathway in Araliaceae and will facilitate heterologous bioproduction of aralosides.

The Microbiome Structure of a Rice-Crayfish Integrated Breeding Model and Its Association with Crayfish Growth and Water Quality

The rice-crayfish (RC) integrated breeding model is an important and special agricultural ecosystem that provides a unique ecological environment for exploring the microbial diversity, composition, and functional capacity. To date, little is known about the effect of the breeding model on microbiome assembly and breeding model-specific microbiome composition and the association of the microbiome with water quality and crayfish growth. In the present study, we assessed the taxonomic shifts in gut and water microbiomes and their associations with water quality and crayfish growth in the RC and crayfish monoculture (CM) breeding models across six time points of rice growth, including seedling (a), tillering and jointing (b), blooming (c), filling (d), fruiting (e), and rotting of rice residues (f). Dominant bacterial phyla, such as Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, were detected in both gut and water microbiomes across breeding models. Notably, the diversity and structure of the gut and water microbiomes significantly (P < 0.001) differed between the RC and CM models, with higher microbial diversity being noted in the RC model than in the CM model. The taxa enriched in the RC model included Bacillus sp., Streptomyces sp., Lactobacillus sp., Prevotella sp., Rhodobacter sp., Bifidobacterium sp., Akkermansia sp., and Lactococcus sp., some of which are potentially beneficial to animals. However, opportunistic pathogens, such as Citrobacter sp. and Aeromonas sp., were depleted in the RC model. Furthermore, in the RC model, the enriched taxa that formed complex cooccurrence networks showed a significant positive correlation with water quality and crayfish growth, whereas the depleted taxa showed a significant negative correlation with water quality and crayfish growth. These results suggest that the RC model has a better microbiome composition and that RC model-specific microbes could play important roles in improving crayfish growth and water quality.

IMPORTANCE The present study comprehensively compared two different breeding models in terms of their microbiome composition and the associations of the microbiomes with crayfish growth and water quality. RC model-specific microbiome composition was identified; these microbes were found to have a positive association with water quality and crayfish growth. These results provide valuable information for guiding microbial isolation and culture and for potentially harnessing the power of the microbiome to improve crayfish production and health and water quality.

Possible effects of temperature on bacterial communities in the rhizosphere of rice under different climatic regions

Global warming is an indisputable fact. However, the effect of warming on the rhizosphere bacterial community of crops is not well understood. Therefore, we carried out pot experiments with three rice (Oryza sativa L.) varieties in black soil across three climatic regions of northeast China to simulate temperature change, and analyzed the response of the rhizosphere bacterial community to different temperatures. Results showed that climate had stronger effects on rhizosphere bacterial communities than rice variety. The rhizosphere bacterial diversity differed significantly among the three climatic regions and positively correlated with the mean daily average temperature (MAveT), mean daily maximum temperature (MMaxT), and mean daily minimum temperature (MMinT), and negatively correlated with the daily temperature range (DTR). Principal co-ordinate analysis revealed that bulk soil bacterial communities maintained a high similarity across the three climatic regions, while rhizosphere bacterial communities notably varied. This change was significantly correlated with MAveT, MMaxT, MMinT, and DTR. Compared with bulk soil, Proteobacteria and Bacteroidetes were enriched in the rhizosphere, while Actinobacteria was depleted. Moreover, these changes were strengthened by increasing the temperature and decreasing DTR. Additionally, correlation analysis revealed that changes in rhizosphere bacterial communities were closely related to the formation of rice yields. Our study revealed that the increasing temperature indirectly reshapes the rhizosphere bacterial community that may promote rice production in areas with lower temperatures.

Evolutionarily conservative and non-conservative regulatory networks during primate interneuron development revealed by single-cell RNA and ATAC sequencing

The differences in size and function between primate and rodent brains, and the association of disturbed excitatory/inhibitory balance with many neurodevelopmental disorders highlight the importance to study primate ganglionic eminences (GEs) development. Here we used single-cell RNA and ATAC sequencing to characterize the emergence of cell diversity in monkey and human GEs where most striatal and cortical interneurons are generated. We identified regional and temporal diversity among progenitor cells which give rise to a variety of interneurons. These cells are specified within the primate GEs by well conserved gene regulatory networks, similar to those identified in mice. However, we detected, in human, several novel regulatory pathways or factors involved in the specification and migration of interneurons. Importantly, comparison of progenitors between our human and published mouse GE datasets led to the discovery and confirmation of outer radial glial cells in GEs in human cortex. Our findings reveal both evolutionarily conservative and nonconservative regulatory networks in primate GEs, which may contribute to their larger brain sizes and more complex neural networks compared with mouse.

Chromosome-scale Echinococcus granulosus (genotype G1) genome reveals the Eg95 gene family and conservation of the EG95-vaccine molecule

Cystic echinococcosis is a socioeconomically important parasitic disease caused by the larval stage of the canid tapeworm Echinococcus granulosus, afflicting millions of humans and animals worldwide. The development of a vaccine (called EG95) has been the most notable translational advance in the fight against this disease in animals. However, almost nothing is known about the genomic organisation/location of the family of genes encoding EG95 and related molecules, the extent of their conservation or their functions. The lack of a complete reference genome for E. granulosus genotype G1 has been a major obstacle to addressing these areas. Here, we assembled a chromosomal-scale genome for this genotype by scaffolding to a high quality genome for the congener E. multilocularis, localised Eg95 gene family members in this genome, and evaluated the conservation of the EG95 vaccine molecule. These results have marked implications for future explorations of aspects such as developmentally-regulated gene transcription/expression (using replicate samples) for all E. granulosus stages; structural and functional roles of non-coding genome regions; molecular ‘cross-talk’ between oncosphere and the immune system; and defining the precise function(s) of EG95. Applied aspects should include developing improved tools for the diagnosis and chemotherapy of cystic echinococcosis of humans.

A high-quality genome for the parasitic tapeworm, Echinococcus granulosus, provides further insight into the EG95 vaccine target for cystic echinococcosis.

Effective prediction of biosynthetic pathway genes involved in bioactive polyphyllins in Paris polyphylla

The genes in polyphyllins pathway mixed with other steroid biosynthetic genes form an extremely complex biosynthetic network in Paris polyphylla with a giant genome. The lack of genomic data and tissue specificity causes the study of the biosynthetic pathway notably difficult. Here, we report an effective method for the prediction of key genes of polyphyllin biosynthesis. Full-length transcriptome from eight different organs via hybrid sequencing of next generation sequencingand third generation sequencing platforms annotated two 2,3-oxidosqualene cyclases (OSCs), 216 cytochrome P450s (CYPs), and 199 UDP glycosyltransferases (UGTs). Combining metabolic differences, gene-weighted co-expression network analysis, and phylogenetic trees, the candidate ranges of OSC, CYP, and UGT genes were further narrowed down to 2, 15, and 24, respectively. Beside the three previously characterized CYPs, we identified the OSC involved in the synthesis of cycloartenol and the UGT (PpUGT73CR1) at the C-3 position of diosgenin and pennogenin in P. polyphylla. This study provides an idea for the investigation of gene cluster deficiency biosynthesis pathways in medicinal plants.

Xin Hua, Wei Song, and ZheYong Xue et al. report an effective method to predict key genes involved in polyphyllin biosynthesis in plants. Their results provide further insight into biosynthesis pathways in Paris polyphylla, and the approach may be relevant to other medicinal plants.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Taxonomic Structure of Rhizosphere Bacterial Communities and Its Association With the Accumulation of Alkaloidal Metabolites in Sophora flavescens

Plant secondary metabolites (SMs) play a crucial role in plant defense against pathogens and adaptation to environmental stresses, some of which are produced from medicinal plants and are the material basis of clinical efficacy and vital indicators for quality evaluation of corresponding medicinal materials. The influence of plant microbiota on plant nutrient uptake, production, and stress tolerance has been revealed, but the associations between plant microbiota and the accumulation of SMs in medicinal plants remain largely unknown. Plant SMs can vary among individuals, which could be partly ascribed to the shift in microbial community associated with the plant host. In the present study, we sampled fine roots and rhizosphere soils of Sophora flavescens grown in four well-separated cities/counties in China and determined the taxonomic composition of rhizosphere bacterial communities using Illumina 16S amplicon sequencing. In addition, the association of the rhizosphere bacterial microbiota with the accumulation of alkaloids in the roots of S. flavescens was analyzed. The results showed that S. flavescens hosted distinct bacterial communities in the rhizosphere across geographic locations and plant ages, also indicating that geographic location was a larger source of variation than plant age. Moreover, redundancy analysis revealed that spatial, climatic (mean annual temperature and precipitation), and edaphic factors (pH and available N and P) were the key drivers that shape the rhizosphere bacterial communities. Furthermore, the results of the Mantel test demonstrated that the rhizosphere bacterial microbiota was remarkably correlated with the contents of oxymatrine, sophoridine, and matrine + oxymatrine in roots. Specific taxa belonging to Actinobacteria and Chloroflexi were identified as potential beneficial bacteria associated with the total accumulation of matrine and oxymatrine by a random forest machine learning algorithm. Finally, the structural equation modeling indicated that the Actinobacteria phylum had a direct effect on the total accumulation of matrine and oxymatrine. The present study addresses the association between the rhizosphere bacterial communities and the accumulation of alkaloids in the medicinal plant S. flavescens. Our findings may provide a basis for the quality improvement and sustainable utilization of this medicinal plant thorough rhizosphere microbiota manipulation.

Deciphering the microbial and molecular responses of geographically diverse Setaria accessions grown in a nutrient-poor soil

The microbial and molecular characterization of the ectorhizosphere is an important step towards developing a more complete understanding of how the cultivation of biofuel crops can be undertaken in nutrient poor environments. The ectorhizosphere of Setaria is of particular interest because the plant component of this plant-microbe system is an important agricultural grain crop and a model for biofuel grasses. Importantly, Setaria lends itself to high throughput molecular studies. As such, we have identified important intra- and interspecific microbial and molecular differences in the ectorhizospheres of three geographically distant Setaria italica accessions and their wild ancestor S. viridis. All were grown in a nutrient-poor soil with and without nutrient addition. To assess the contrasting impact of nutrient deficiency observed for two S. italica accessions, we quantitatively evaluated differences in soil organic matter, microbial community, and metabolite profiles. Together, these measurements suggest that rhizosphere priming differs with Setaria accession, which comes from alterations in microbial community abundances, specifically Actinobacteria and Proteobacteria populations. When globally comparing the metabolomic response of Setaria to nutrient addition, plants produced distinctly different metabolic profiles in the leaves and roots. With nutrient addition, increases of nitrogen containing metabolites were significantly higher in plant leaves and roots along with significant increases in tyrosine derived alkaloids, serotonin, and synephrine. Glycerol was also found to be significantly increased in the leaves as well as the ectorhizosphere. These differences provide insight into how C₄ grasses adapt to changing nutrient availability in soils or with contrasting fertilization schemas. Gained knowledge could then be utilized in plant enhancement and bioengineering efforts to produce plants with superior traits when grown in nutrient poor soils.

Soil bacterial microbiota predetermines rice yield in reclaiming saline-sodic soils leached with brackish ice

BACKGROUND

Saline-sodic lands threaten the food supply and ecological security in the western Songnen Plain of northeast China, and the gypsum is commonly adopted for restoration. However, the dynamics of soil bacterial community and the correlation with crop yield during restoring processes remain poorly understood. Here, we elucidated the soil chemical properties and bacterial communities and their associations with rice yield under different flue gas desulphurization gypsum (FGDG) application rates combined with brackish ice leaching.

RESULTS

The increased application rate of FGDG generally improved soil reclamation effects, as indicated by soil chemical properties, bacterial diversity, and rice yield. Compared with fresh ice irrigation, the rice yield in brackish ice treatment increased by 15.84%, and the soil alkalinity and sodium adsorption ratio (SAR) decreased by 35.19% and 10.30%, respectively. The bacterial alpha diversity significantly correlated and predicted rice yield as early as brackish ice melt, suggesting the bacterial diversity was a sensitive indicator in predicting rice yield. Meanwhile, the bacterial communities in the control possessed a high abundance of oligotrophic Firmicutes, while eutrophic bacterial taxa (e.g. Proteobacteria) were enriched after brackish water irrigation and FGDG application. Moreover, we also established a Random Forest model and identified a bacterial consortium that explained an 80.0% variance of rice yield.

CONCLUSION

Together, our results highlight the reclaiming effect of brackish ice in the saline-sodic field and demonstrate the sensitivity and importance of the soil bacterial community in predicting crop yield, which would provide essential knowledge on the soil quality indication and bio-fertilizer development for soil reclamation. © 2021 Society of Chemical Industry.

Application of Deep Learning in Plant-Microbiota Association Analysis

Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.

Diversity of the Bacterial Microbiome Associated With the Endosphere and Rhizosphere of Different Cassava (Manihot esculenta Crantz) Genotypes

Root-associated microbial communities play important roles in plant growth and development. However, little attention has been paid to the microbial community structures associated with cassava, which is a staple food for approximately 800 million people worldwide. Here, we studied the diversity and structure of tuber endosphere and rhizosphere bacterial communities in fourteen cassava genotypes: SC5, SC8, SC9, SC205, KU50, R72, XL1, FX01, SC16, 4612, 587, 045, S0061, and 1110. The results of bacterial 16S rDNA sequencing showed that the richness and diversity of bacteria in the rhizosphere were higher than those in the tuber endosphere across the 14 cassava genotypes. After sequencing, 21 phyla and 310 genera were identified in the tuberous roots, and 36 phyla and 906 genera were identified in the rhizosphere soils. The dominant phylum across all tuber samples was Firmicutes, and the dominant phyla across all rhizosphere samples were Actinobacteria, Proteobacteria, and Acidobacteria. The numbers of core bacterial taxa within the tuber endospheres and the rhizospheres of all cassava genotypes were 11 and 236, respectively. Principal coordinate analysis and hierarchical cluster analysis demonstrated significant differences in the compositions of rhizosphere soil microbiota associated with the different cassava genotypes. Furthermore, we investigated the metabolic changes in tuber roots of three genotypes, KU50, SC205, and SC9. The result showed that the abundances of Firmicutes, Proteobacteria, and Actinobacteria in tuber samples were positively correlated with organic acids and lipids and negatively correlated with vitamins and cofactors. These results strongly indicate that there are clear differences in the structure and diversity of the bacterial communities associated with different cassava genotypes.