Absolute quantitation of microbiota abundance in environmental samples

Three steps towards comparability and standardization among molecular methods for characterizing insect communities

Molecular methods are currently some of the best-suited technologies for implementation in insect monitoring. However, the field is developing rapidly and lacks agreement on methodology or community standards. To apply DNA-based methods in large-scale monitoring, and to gain insight across commensurate data, we need easy-to-implement standards that improve data comparability. Here, we provide three recommendations for how to improve and harmonize efforts in biodiversity assessment and monitoring via metabarcoding: (i) we should adopt the use of synthetic spike-ins, which will act as positive controls and internal standards; (ii) we should consider using several markers through a multiplex polymerase chain reaction (PCR) approach; and (iii) we should commit to the publication and transparency of all protocol-associated metadata in a standardized fashion. For (i), we provide a ready-to-use recipe for synthetic cytochrome c oxidase spike-ins, which enable between-sample comparisons. For (ii), we propose two gene regions for the implementation of multiplex PCR approaches, thereby achieving a more comprehensive community description. For (iii), we offer guidelines for transparent and unified reporting of field, wet-laboratory and dry-laboratory procedures, as a key to making comparisons between studies. Together, we feel that these three advances will result in joint quality and calibration standards rather than the current laboratory-specific proof of concepts. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Stable, fluorescent markers for tracking synthetic communities and assembly dynamics

BACKGROUND

After two decades of extensive microbiome research, the current forefront of scientific exploration involves moving beyond description and classification to uncovering the intricate mechanisms underlying the coalescence of microbial communities. Deciphering microbiome assembly has been technically challenging due to their vast microbial diversity but establishing a synthetic community (SynCom) serves as a key strategy in unravelling this process. Achieving absolute quantification is crucial for establishing causality in assembly dynamics. However, existing approaches are primarily designed to differentiate a specific group of microorganisms within a particular SynCom.

RESULTS

To address this issue, we have developed the differential fluorescent marking (DFM) strategy, employing three distinguishable fluorescent proteins in single and double combinations. Building on the mini-Tn7 transposon, DFM capitalises on enhanced stability and broad applicability across diverse Proteobacteria species. The various DFM constructions are built using the pTn7-SCOUT plasmid family, enabling modular assembly, and facilitating the interchangeability of expression and antibiotic cassettes in a single reaction. DFM has no detrimental effects on fitness or community assembly dynamics, and through the application of flow cytometry, we successfully differentiated, quantified, and tracked a diverse six-member SynCom under various complex conditions like root rhizosphere showing a different colonisation assembly dynamic between pea and barley roots.

CONCLUSIONS

DFM represents a powerful resource that eliminates dependence on sequencing and/or culturing, thereby opening new avenues for studying microbiome assembly. Video Abstract.

Metabarcoding read abundances of orchid mycorrhizal fungi are correlated to copy numbers estimated using ddPCR

Quantifying the abundances of fungi is key to understanding natural variation in mycorrhizal communities in relation to plant ecophysiology and environmental heterogeneity. High-throughput metabarcoding approaches have transformed our ability to characterize and compare complex mycorrhizal communities. However, it remains unclear how well metabarcoding read counts correlate with actual read abundances in the sample, potentially limiting their use as a proxy for species abundances. Here, we use droplet digital PCR (ddPCR) to evaluate the reliability of ITS2 metabarcoding data for quantitative assessments of mycorrhizal communities in the orchid species Neottia ovata sampled at multiple sites. We performed specific ddPCR assays for eight families of orchid mycorrhizal fungi and compared the results with read counts obtained from metabarcoding. Our results demonstrate a significant correlation between DNA copy numbers measured by ddPCR assays and metabarcoding read counts of major mycorrhizal partners of N. ovata, highlighting the usefulness of metabarcoding for quantifying the abundance of orchid mycorrhizal fungi. Yet, the levels of correlation between the two methods and the numbers of false zero values varied across fungal families, which warrants cautious evaluation of the reliability of low-abundance families. This study underscores the potential of metabarcoding data for more quantitative analyses of mycorrhizal communities and presents practical workflows for metabarcoding and ddPCR to achieve a more comprehensive understanding of orchid mycorrhizal communities.

Quantifying soil microbial effects on plant species coexistence: A conceptual synthesis

Soil microorganisms play a critical role in shaping the biodiversity dynamics of plant communities. These microbial effects can arise through direct mediation of plant fitness by pathogens and mutualists, and over the past two decades, numerous studies have shined a spotlight on the role of dynamic feedbacks between plants and soil microorganisms as key determinants of plant species coexistence. Such feedbacks occur when plants modify the composition of the soil community, which in turn affects plant performance. Stimulated by a theoretical model developed in the 1990s, a bulk of the empirical evidence for microbial controls over plant coexistence comes from experiments that quantify plant growth in soil communities that were previously conditioned by conspecific or heterospecific plants. These studies have revealed that soil microbes can generate strong negative to positive frequency-dependent dynamics among plants. Even as soil microbes have become recognized as a key player in determining plant coexistence outcomes, the past few years have seen a renewed interest in expanding the conceptual foundations of this field. New results include re-interpretations of key metrics from classic two-species models, extensions of plant-soil feedback theory to multispecies communities, and frameworks to integrate plant-soil feedbacks with processes like intra- and interspecific competition. Here, I review the implications of theoretical developments for interpreting existing empirical results and highlight proposed analyses and designs for future experiments that can enable a more complete understanding of microbial regulation of plant community dynamics.

Airborne antibiotic and metal resistance genes - A neglected potential risk at e-waste recycling facilities

Heavy metal-rich environments can promote the selection of metal-resistance genes (MRGs) in bacteria, often leading to the simultaneous selection of antibiotic-resistance genes (ARGs) through a process known as co-selection. To comprehensively evaluate the biological pollutants at electronic-waste (e-waste) recycling facilities, air, soil, and river samples were collected at four distinct Swiss e-waste recycling facilities and analyzed for ARGs, MRGs, mobile genetic elements (MGEs), endotoxins, and bacterial species, with correlations drawn to heavy metal occurrence. To our knowledge, the present work marks the first attempt to quantify these bio-pollutants in the air of e-waste recycling facilities, that might pose a significant health risk to workers. Although ARG and MRG's profiles varied among the different sample types, intl1 consistently exhibited high relative abundance rates, identifying it as the predominant MGE across all sample types and facilities. These findings underscore its pivol role in driving diverse bacterial adaptations to extreme heavy metal exposure by selection and dissemination of ARGs and MRGs. All air samples exhibited consistent profiles of ARGs and MRGs, with blaTEM emerging as the predominant ARG, alongside pbrT and nccA as the most prevalent MRGs. However, one facility, engaged in batteries recycling and characterized by exceptionally high concentrations of heavy metals, showcased a more diverse resistance gene profile, suggesting that bacteria in this environment required more complex resistance mechanisms to cope with extreme metal exposure. Furthermore, this study unveiled a strong association between gram-negative bacteria and ARGs and less with MRGs. Overall, this research emphasizes the critical importance of studying biological pollutants in the air of e-waste recycling facilities to inform robust safety measures and mitigate the risk of resistance gene dissemination among workers. These findings establish a solid foundation for further investigations into the complex interplay among heavy metal exposure, bacterial adaptation, and resistance patterns in such distinctive ecosystems.

Application Progress of Culturomics in the Isolated Culture of Rhizobacteria: A Review

Comprehending the structure and function of rhizobacteria components and their regulation are crucial for sustainable agricultural management. However, obtaining comprehensive species information for most bacteria in the natural environment, particularly rhizobacteria, presents a challenge using traditional culture methods. To obtain diverse and pure cultures of rhizobacteria, this study primarily reviews the evolution of rhizobacteria culturomics and associated culture methods. Furthermore, it explores new strategies for enhancing the application of culturomics, providing valuable insights into efficiently enriching and isolate target bacterial strains/groups from the environment. The findings will help improve rhizobacteria's culturability and enrich the functional bacterial library.

Chromosomal barcodes for simultaneous tracking of near-isogenic bacterial strains in plant microbiota

DNA-amplicon-based microbiota profiling can estimate species diversity and abundance but cannot resolve genetic differences within individuals of the same species. Here we report the development of modular bacterial tags (MoBacTags) encoding DNA barcodes that enable tracking of near-isogenic bacterial commensals in an array of complex microbiome communities. Chromosomally integrated DNA barcodes are then co-amplified with endogenous marker genes of the community by integrating corresponding primer binding sites into the barcode. We use this approach to assess the contributions of individual bacterial genes to Arabidopsis thaliana root microbiota establishment with synthetic communities that include MoBacTag-labelled strains of Pseudomonas capeferrum. Results show reduced root colonization for certain mutant strains with defects in gluconic-acid-mediated host immunosuppression, which would not be detected with traditional amplicon sequencing. Our work illustrates how MoBacTags can be applied to assess scaling of individual bacterial genetic determinants in the plant microbiota.

Metagenomic absolute quantification of antibiotic resistance genes and virulence factor genes-carrying bacterial genomes in anaerobic digesters

Sewage treatment works have been considered as hotspots for the dissemination of antibiotic resistance genes (ARGs). Anaerobic digestion (AD) has emerged as a promising approach for controlling the spread of ARGs while destroying biomass in sludge. Evaluating the impact of AD on ARG removal relies on the absolute quantification of ARGs. In this study, we quantified the ARG concentrations in both full-scale and lab-scale AD systems using a cellular spike-ins based absolute quantification approach. Results demonstrated that AD effectively removed 68 ± 18 %, 55 ± 12 %, and 57 ± 19 % of total ARGs in semi-continuous AD digesters, with solid retention times of 15, 20, and 25 days, respectively. The removal efficiency of total ARGs increased as the AD process progressed in the batch digesters over 40 days. A significant negative correlation was observed between digestion time and the concentrations of certain ARG types, such as beta-lactam, sulfonamide, and tetracycline. However, certain potential pathogenic antibiotic resistant bacteria (PARB) and multi-resistant high-risk ARGs-carrying populations robustly persisted throughout the AD process, regardless of the operating conditions. This study highlighted the influence of the AD process and its operating parameters on ARG removal, and revealed the broad spectrum and persistence of PARB in AD systems. These findings provided critical insights for the management of microbial hazards.

The prevalence of controls in phyllosphere microbiome research: a methodological review

DNA contamination can critically confound microbiome studies. Here, we take a systematic approach to review the current literature and investigate the prevalence of contamination controls in phyllosphere microbiome research over the past decade. By utilising systematic review principles for this review, we were able to conduct a thorough investigation, screening 450 articles from three databases for eligibility and extracting data in a controlled and methodical manner. Worryingly, we observed a surprisingly low usage of both positive and negative contamination controls in phyllosphere research. As a result, we propose a set of minimum standards to combat the effects of contamination in future phyllosphere research.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Dynamic root microbiome sustains soybean productivity under unbalanced fertilization

Root-associated microbiomes contribute to plant growth and health, and are dynamically affected by plant development and changes in the soil environment. However, how different fertilizer regimes affect quantitative changes in microbial assembly to effect plant growth remains obscure. Here, we explore the temporal dynamics of the root-associated bacteria of soybean using quantitative microbiome profiling (QMP) to examine its response to unbalanced fertilizer treatments (i.e., lacking either N, P or K) and its role in sustaining plant growth after four decades of unbalanced fertilization. We show that the root-associated bacteria exhibit strong succession during plant development, and bacterial loads largely increase at later stages, particularly for Bacteroidetes. Unbalanced fertilization has a significant effect on the assembly of the soybean rhizosphere bacteria, and in the absence of N fertilizer the bacterial community diverges from that of fertilized plants, while lacking P fertilizer impedes the total load and turnover of rhizosphere bacteria. Importantly, a SynCom derived from the low-nitrogen-enriched cluster is capable of stimulating plant growth, corresponding with the stabilized soybean productivity in the absence of N fertilizer. These findings provide new insights in the quantitative dynamics of the root-associated microbiome and highlight a key ecological cluster with prospects for sustainable agricultural management.

Cutting edge tools in the field of soil microbiology

The study of the whole of the genetic material contained within the microbial populations found in a certain environment is made possible by metagenomics. This technique enables a thorough knowledge of the variety, function, and interactions of microbial communities that are notoriously difficult to research. Due to the limitations of conventional techniques such as culturing and PCR-based methodologies, soil microbiology is a particularly challenging field. Metagenomics has emerged as an effective technique for overcoming these obstacles and shedding light on the dynamic nature of the microbial communities in soil. This review focuses on the principle of metagenomics techniques, their potential applications and limitations in soil microbial diversity analysis. The effectiveness of target-based metagenomics in determining the function of individual genes and microorganisms in soil ecosystems is also highlighted. Targeted metagenomics, including high-throughput sequencing and stable-isotope probing, is essential for studying microbial taxa and genes in complex ecosystems. Shotgun metagenomics may reveal the diversity of soil bacteria, composition, and function impacted by land use and soil management. Sanger, Next Generation Sequencing, Illumina, and Ion Torrent sequencing revolutionise soil microbiome research. Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)'s third and fourth generation sequencing systems revolutionise long-read technology. GeoChip, clone libraries, metagenomics, and metabarcoding help comprehend soil microbial communities. The article indicates that metagenomics may improve environmental management and agriculture despite existing limitations.Metagenomics has revolutionised soil microbiology research by revealing the complete diversity, function, and interactions of microorganisms in soil. Metagenomics is anticipated to continue defining the future of soil microbiology research despite some limitations, such as the difficulty of locating the appropriate sequencing method for specific genes.

Development of a novel mycobiome diagnostic for fungal infection

BACKGROUND

Amplicon-based mycobiome analysis has the potential to identify all fungal species within a sample and hence could provide a valuable diagnostic assay for use in clinical mycology settings. In the last decade, the mycobiome has been increasingly characterised by targeting the internal transcribed spacer (ITS) regions. Although ITS targets give broad coverage and high sensitivity, they fail to provide accurate quantitation as the copy number of ITS regions in fungal genomes is highly variable even within species. To address these issues, this study aimed to develop a novel NGS fungal diagnostic assay using an alternative amplicon target.

METHODS

Novel universal primers were designed to amplify a highly diverse single copy and uniformly sized DNA target (Tef1) to enable mycobiome analysis on the Illumina iSeq100 which is a low cost, small footprint and simple to use next-generation sequencing platform. To enable automated analysis and rapid results, a streamlined bioinformatics workflow and sequence database were also developed. Sequencing of mock fungal communities was performed to compare the Tef1 assay and established ITS1-based method. The assay was further evaluated using clinical respiratory samples and the feasibility of using internal spike-in quantitative controls was assessed.

RESULTS

The Tef1 assay successfully identified and quantified Aspergillus, Penicillium, Candida, Cryptococcus, Rhizopus, Fusarium and Lomentospora species from mock communities. The Tef1 assay was also capable of differentiating closely related species such as A. fumigatus and A. fischeri. In addition, it outperformed ITS1 at identifying A. fumigatus and other filamentous pathogens in mixed fungal communities (in the presence or absence of background human DNA). The assay could detect as few as 2 haploid genome equivalents of A. fumigatus from clinical respiratory samples. Lastly, spike-in controls were demonstrated to enable semi-quantitation of A. fumigatus load in clinical respiratory samples using sequencing data.

CONCLUSIONS

This study has developed and tested a novel metabarcoding target and found the assay outperforms ITS1 at identifying clinically relevant filamentous fungi. The assay is a promising diagnostic candidate that could provide affordable NGS analysis to clinical mycology laboratories.

Quantifying bias introduced by sample collection in relative and absolute microbiome measurements

To gain insight into the accuracy of microbial measurements, it is important to evaluate sources of bias related to sample condition, preservative method and bioinformatic analyses. There is increasing evidence that measurement of the total count and concentration of microbes in the gut, or 'absolute abundance', provides a richer source of information than relative abundance and can correct some conclusions drawn from relative abundance data. However, little is known about how preservative choice can affect these measurements. In this study, we investigated how two common preservatives and short-term storage conditions impact relative and absolute microbial measurements. OMNIgene GUT OMR-200 yields lower metagenomic taxonomic variation between different storage temperatures, whereas Zymo DNA/RNA Shield yields lower metatranscriptomic taxonomic variation. Absolute abundance quantification reveals two different causes of variable Bacteroidetes:Firmicutes ratios across preservatives. Based on these results, we recommend OMNIgene GUT OMR-200 preservative for field studies and Zymo DNA/RNA Shield for metatranscriptomics studies, and we strongly encourage absolute quantification for microbial measurements.

A novel synthetic nucleic acid mixture for quantification of microbes by mNGS

Metagenomic next-generation sequencing (mNGS) provides considerable advantages in identifying emerging and re-emerging, difficult-to-detect and co-infected pathogens; however, the clinical application of mNGS remains limited primarily due to the lack of quantitative capabilities. This study introduces a novel approach, KingCreate-Quantification (KCQ) system, for quantitative analysis of microbes in clinical specimens by mNGS, which co-sequence the target DNA extracted from the specimens along with a set of synthetic dsDNA molecules used as Internal-Standard (IS). The assay facilitates the conversion of microbial reads into their copy numbers based on IS reads utilizing a mathematical model proposed in this study. The performance of KCQ was systemically evaluated using commercial mock microbes with varying IS input amounts, different proportions of human genomic DNA, and at varying amounts of sequence analysis data. Subsequently, KCQ was applied in microbial quantitation in 36 clinical specimens including blood, bronchoalveolar lavage fluid, cerebrospinal fluid and oropharyngeal swabs. A total of 477 microbe genetic fragments were screened using the bioinformatic system. Of these 83 fragments were quantitatively compared with digital droplet PCR (ddPCR), revealing a correlation coefficient of 0.97 between the quantitative results of KCQ and ddPCR. Our study demonstrated that KCQ presents a practical approach for the quantitative analysis of microbes by mNGS in clinical samples.

Review: Research progress on seasonal succession of phyllosphere microorganisms

Phyllosphere microorganisms have recently attracted the attention of scientists studying plant microbiomes. The origin, diversity, functions, and interactions of phyllosphere microorganisms have been extensively explored. Many experiments have demonstrated seasonal cycles of phyllosphere microbes. However, a comprehensive comparison of these separate investigations to characterize seasonal trends in phyllosphere microbes of woody and herbaceous plants has not been conducted. In this review, we explored the dynamic changes of phyllosphere microorganisms in woody and non-woody plants with the passage of the season, sought to find the driving factors, summarized these texts, and thought about future research trends regarding the application of phyllosphere microorganisms in agricultural production. Seasonal trends in phyllosphere microorganisms of herbaceous and woody plants have similarities and differences, but extensive experimental validation is needed. Climate, insects, hosts, microbial interactions, and anthropogenic activities are the diverse factors that influence seasonal variation in phyllosphere microorganisms.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

PCR-based analytics of gene therapies using adeno-associated virus vectors: Considerations for cGMP method development

The field of gene therapy has evolved and improved so that today the treatment of thousands of genetic diseases is now possible. An integral aspect of the drug development process is generating analytical methods to be used throughout clinical and commercial manufacturing. Enumeration and identification assays using genetic testing are critical to ensure the safety, efficacy, and stability of many active pharmaceutical ingredients. While nucleic acid-based methods are already reliable and rapid, there are unique biological, technological, and regulatory aspects in gene therapies that must be considered. This review surveys aspects of method development and validation using nucleic acid-based testing of gene therapies by focusing on adeno-associated virus (AAV) vectors and their co-transfection factors. Key differences between quantitative PCR and droplet digital technologies are discussed to show how improvements can be made while still adhering to regulatory guidance. Example validation parameters for AAV genome titers are described to demonstrate the scope of analytical development. Finally, several areas for improving analytical testing are presented to inspire future innovation, including next-generation sequencing and artificial intelligence. Reviewing the broad characteristics of gene therapy assessment serves as an introduction for new researchers, while clarifying processes for professionals already involved in pharmaceutical manufacturing.

Land use alters bacterial growth dynamics in soil

Microbial growth and mortality are major determinants of soil carbon cycling. We measured in situ growth dynamics of individual bacterial taxa in cropped and successional soils in response to a resource pulse. We hypothesized that land use imposes selection pressures on growth characteristics. We estimated growth and death for 453 and 73 taxa, respectively. The average generation time was 5.04 ± 6.28 (SD; range 0.7-63.5) days. Lag times were shorter in cultivated than successional soils and resource amendment decreased lag times. Taxa exhibiting the greatest growth response also exhibited the greatest mortality, indicative of boom-and-bust dynamics. We observed a bimodal growth rate distribution, representing fast- and slow-growing clusters. Both clusters grew more rapidly in successional soils, which had more organic matter, than cultivated soils. Resource amendment increased the growth rate of the slower growing but not the faster-growing cluster via a mixture of increased growth rates and species turnover, indicating that competitive dynamics constrain growth rates in situ. These two clusters show that copiotrophic bacteria in soils may be subdivided into different life history groups and that these subgroups respond independently to land use and resource availability.

A quantitative sequencing method using synthetic internal standards including functional and phylogenetic marker genes

The method of spiking synthetic internal standard genes (ISGs) to samples for amplicon sequencing, generating sequences and converting absolute gene numbers from read counts has been used only for phylogenetic markers and has not been applied to functional markers. In this study, we developed ISGs, including gene sequences of the 16S rRNA, pmoA, encoding a subunit of particulate methane monooxygenase and amoA, encoding a subunit of ammonia monooxygenase. We added ISGs to the samples, amplified the target genes and performed amplicon sequencing. For the mock community, the copy numbers converted from read counts using ISGs were equivalent to those obtained by the quantitative real-time polymerase chain reaction (4.0 × 104 versus 4.1 × 104 and 3.0 × 103 versus 4.0 × 103 copies μL-DNA-1 for 16S rRNA and pmoA genes, respectively), but we also identified underestimation, possibly due to primer coverage (7.8 × 102 versus 3.7 × 103  μL-DNA-1 for amoA gene). We then applied this method to environmental samples and analysed phylogeny, functional diversity and absolute quantities. One Methylocystis population was most abundant in the sludge samples [16S rRNA gene (3.8 × 109 copies g-1 ) and the pmoA gene (2.3 × 109 copies g-1 )] and were potentially interrelated. This study demonstrates that ISG spiking is useful for evaluating sequencing data processing and quantifying functional markers.

Microbe-associated molecular pattern recognition receptors have little effect on endophytic Arabidopsis thaliana microbiome assembly in the field

Plant microbiome structure affects plant health and productivity. A limited subset of environmental microbes successfully establishes within plant tissues, but the forces underlying this selectivity remain poorly characterized. Transmembrane pattern recognition receptors (PRRs), used by plants to detect microbe-associated molecular patterns (MAMPs), are strong candidates for achieving this selectivity because PRRs can potentially interact with many members of the microbiome. Indeed, MAMPs found in many microbial taxa, including beneficials and commensals, can instigate a robust immune response that affects microbial growth. Surprisingly, we found that MAMP-detecting PRRs have little effect on endophytic bacterial and fungal microbiome structure in the field. We compared the microbiomes of four PRR knockout lines of Arabidopsis thaliana to wild-type plants in multiple tissue types over several developmental stages and detected only subtle shifts in fungal, but not bacterial, β-diversity in one of the four PRR mutants. In one developmental stage, lore mutants had slightly altered fungal β-diversity, indicating that LORE may be involved in plant-fungal interactions in addition to its known role in detecting certain bacterial lipids. No other effects of PRRs on α-diversity, microbiome variability, within-individual homogeneity, or microbial load were found. The general lack of effect suggests that individual MAMP-detecting PRRs are not critical in shaping the endophytic plant microbiome. Rather, we suggest that MAMP-detecting PRRs must either act in concert and/or are individually maintained through pleiotropic effects or interactions with coevolved mutualists or pathogens. Although unexpected, these results offer insights into the role of MAMP-detecting PRRs in plant-microbe interactions and help direct future efforts to uncover host genetic elements that control plant microbiome assembly.

Relative abundance data can misrepresent heritability of the microbiome

BACKGROUND

Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h2) of the microbiome-the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample.

RESULTS

We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates.

CONCLUSIONS

We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. Video Abstract.

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

The respiratory tract microbiome, the pathogen load, and clinical interventions define severity of bacterial pneumonia

Bacterial pneumonia is a considerable problem worldwide. Here, we follow the inter-kingdom respiratory tract microbiome (RTM) of a unique cohort of 38 hospitalized patients (n = 97 samples) with pneumonia caused by Legionella pneumophila. The RTM composition is characterized by diversity drops early in hospitalization and ecological species replacement. RTMs with the highest bacterial and fungal loads show low diversity and pathogen enrichment, suggesting high biomass as a biomarker for secondary and/or co-infections. The RTM structure is defined by a "commensal" cluster associated with a healthy RTM and a "pathogen" enriched one, suggesting that the cluster equilibrium drives the microbiome to recovery or dysbiosis. Legionella biomass correlates with disease severity and co-morbidities, while clinical interventions influence the RTM dynamics. Fungi, archaea, and protozoa seem to contribute to progress of pneumonia. Thus, the interplay of the RTM equilibrium, the pathogen load dynamics, and clinical interventions play a critical role in patient recovery.

Effect of cadmium stress on the bacterial community in the rhizosphere of mulberry (Morus alba L.)

Mulberry has a good tolerance to cadmium (Cd) and is considered a candidate plant for phytoremediation. The rhizosphere microbial community plays an important role in phytoremediation. Nevertheless, little information on the rhizosphere microbial community mechanisms in mulberry during the phytoremediation of Cd-contaminated soil is available. In this study, the remediation efficiency of mulberry in pots subjected to three simulated Cd pollution levels and their rhizosphere bacterial communities during the remediation process were analyzed. "Yuesang 11" was used as the test mulberry variety, and three simulated Cd pollution levels were set by adding three concentrations of Cd (Cd5, 5 mg kg-1; Cd3, 3 mg kg-1; Cd2, 2 mg kg-1). The results showed that the elimination rates of Cd in the rhizosphere soil were 81.7%, 85.3%, and 57.9% under the stress of the Cd2, Cd3, and Cd5 conditions, respectively. Meanwhile, 3,082,583 high-quality sequence reads and 976 operational taxonomic units were successfully obtained from the mulberry rhizosphere soil by high-throughput absolute quantification sequencing and further assigned to 11 bacterial phyla and 26 families. Of these, decreased abundances of 19 bacteria at the family level and increased abundances of seven bacteria under Cd stress were revealed by comparative analysis. Based on the alpha diversity indices (Chaol, Shannon and Simpson) and principal component analysis, the rhizosphere bacterial diversity of the Cd5 condition was significantly decreased, but that of the Cd2 and Cd3 conditions was not different from that of soil without Cd (CK). Likewise, redundancy analysis showed that the abundances of Acidobacteria Gp2, Acidobacteria Gp13, and Sphingobacteria were significantly positively associated with the elimination rates of Cd. This study suggested that the mulberry rhizosphere contains a relatively stable bacterial community consisting of diverse Cd-resistant bacteria, providing a scientific basis for remediating heavy-metal polluted soils using mulberry.

RSim: A reference-based normalization method via rank similarity

Microbiome sequencing data normalization is crucial for eliminating technical bias and ensuring accurate downstream analysis. However, this process can be challenging due to the high frequency of zero counts in microbiome data. We propose a novel reference-based normalization method called normalization via rank similarity (RSim) that corrects sample-specific biases, even in the presence of many zero counts. Unlike other normalization methods, RSim does not require additional assumptions or treatments for the high prevalence of zero counts. This makes it robust and minimizes potential bias resulting from procedures that address zero counts, such as pseudo-counts. Our numerical experiments demonstrate that RSim reduces false discoveries, improves detection power, and reveals true biological signals in downstream tasks such as PCoA plotting, association analysis, and differential abundance analysis.

The conserved iol gene cluster in Pseudomonas is involved in rhizosphere competence

The Pseudomonas genus has shown great potential as a sustainable solution to support agriculture through its plant-growth-promoting and biocontrol activities. However, their efficacy as bioinoculants is limited by unpredictable colonization in natural conditions. Our study identifies the iol locus, a gene cluster in Pseudomonas involved in inositol catabolism, as a feature enriched among superior root colonizers in natural soil. Further characterization revealed that the iol locus increases competitiveness, potentially caused by an observed induction of swimming motility and the production of fluorescent siderophore in response to inositol, a plant-derived compound. Public data analyses indicate that the iol locus is broadly conserved in the Pseudomonas genus and linked to diverse host-microbe interactions. Together, our findings suggest the iol locus as a potential target for developing more effective bioinoculants for sustainable agriculture.

Unintended consequences: Disrupting microbial communities of Nilaparvata lugens with non-target pesticides

Insects are frequently exposed to a range of insecticides that can alter the structure of the commensal microbiome. However, the effects of exposure to non-target pesticides (including non-target insecticides and fungicides) on insect pest microbiomes are still unclear. In the present study, we exposed Nilaparvata lugens to three target insecticides (nitenpyram, pymetrozine, and avermectin), a non-target insecticide (chlorantraniliprole), and two fungicides (propiconazole and tebuconazole), and observed changes in the microbiome's structure and function. Our results showed that both non-target insecticide and fungicides can disrupt the microbiome's structure. Specifically, symbiotic bacteria of N. lugens were more sensitive to non-target insecticide compared to target insecticide, while the symbiotic fungi were more sensitive to fungicides. We also found that the microbiome in the field strain was more stable under pesticides exposure than the laboratory strain (a susceptible strain), and core microbial species g_Pseudomonas, s_Acinetobacter soli, g_Lactobacillus, s_Metarhizium minus, and s_Penicillium citrinum were significantly affected by specifically pesticides. Furthermore, the functions of symbiotic bacteria in nutrient synthesis were predicted to be significantly reduced by non-target insecticide. Our findings contribute to a better understanding of the impact of non-target pesticides on insect microbial communities and highlight the need for scientific and rational use of pesticides.

Soil and Phytomicrobiome for Plant Disease Suppression and Management under Climate Change: A Review

The phytomicrobiome plays a crucial role in soil and ecosystem health, encompassing both beneficial members providing critical ecosystem goods and services and pathogens threatening food safety and security. The potential benefits of harnessing the power of the phytomicrobiome for plant disease suppression and management are indisputable and of interest in agriculture but also in forestry and landscaping. Indeed, plant diseases can be mitigated by in situ manipulations of resident microorganisms through agronomic practices (such as minimum tillage, crop rotation, cover cropping, organic mulching, etc.) as well as by applying microbial inoculants. However, numerous challenges, such as the lack of standardized methods for microbiome analysis and the difficulty in translating research findings into practical applications are at stake. Moreover, climate change is affecting the distribution, abundance, and virulence of many plant pathogens, while also altering the phytomicrobiome functioning, further compounding disease management strategies. Here, we will first review literature demonstrating how agricultural practices have been found effective in promoting soil health and enhancing disease suppressiveness and mitigation through a shift of the phytomicrobiome. Challenges and barriers to the identification and use of the phytomicrobiome for plant disease management will then be discussed before focusing on the potential impacts of climate change on the phytomicrobiome functioning and disease outcome.

Combining 16S Sequencing and qPCR Quantification Reveals Staphylococcus aureus Driven Bacterial Overgrowth in the Skin of Severe Atopic Dermatitis Patients

Atopic dermatitis (AD) is an inflammatory skin disease with a microbiome dysbiosis towards a high relative abundance of Staphylococcus aureus. However, information is missing on the actual bacterial load on AD skin, which may affect the cell number driven release of pathogenic factors. Here, we combined the relative abundance results obtained by next-generation sequencing (NGS, 16S V1-V3) with bacterial quantification by targeted qPCR (total bacterial load = 16S, S. aureus = nuc gene). Skin swabs were sampled cross-sectionally (n = 135 AD patients; n = 20 healthy) and longitudinally (n = 6 AD patients; n = 6 healthy). NGS and qPCR yielded highly inter-correlated S. aureus relative abundances and S. aureus cell numbers. Additionally, intra-individual differences between body sides, skin status, and consecutive timepoints were also observed. Interestingly, a significantly higher total bacterial load, in addition to higher S. aureus relative abundance and cell numbers, was observed in AD patients in both lesional and non-lesional skin, as compared to healthy controls. Moreover, in the lesional skin of AD patients, higher S. aureus cell numbers significantly correlated with the higher total bacterial load. Furthermore, significantly more severe AD patients presented with higher S. aureus cell number and total bacterial load compared to patients with mild or moderate AD. Our results indicate that severe AD patients exhibit S. aureus driven increased bacterial skin colonization. Overall, bacterial quantification gives important insights in addition to microbiome composition by sequencing.

Genome-wide mapping of gene-microbe interactions in the murine lung microbiota based on quantitative microbial profiling

BACKGROUND

Mammalian lungs comprise a complex microbial ecosystem that interacts with host physiology. Previous research demonstrates that the environment significantly contributes to bacterial community structure in the upper and lower respiratory tract. However, the influence of host genetics on the makeup of lung microbiota remains ambiguous, largely due to technical difficulties related to sampling, as well as challenges inherent to investigating low biomass communities. Thus, innovative approaches are warranted to clarify host-microbe interactions in the mammalian lung.

RESULTS

Here, we aimed to characterize host genomic regions associated with lung bacterial traits in an advanced intercross mouse line (AIL). By performing quantitative microbial profiling (QMP) using the highly precise method of droplet digital PCR (ddPCR), we refined 16S rRNA gene amplicon-based traits to identify and map candidate lung-resident taxa using a QTL mapping approach. In addition, the two abundant core taxa Lactobacillus and Pelomonas were chosen for independent microbial phenotyping using genus-specific primers. In total, this revealed seven significant loci involving eight bacterial traits. The narrow confidence intervals afforded by the AIL population allowed us to identify several promising candidate genes related to immune and inflammatory responses, cell apoptosis, DNA repair, and lung functioning and disease susceptibility. Interestingly, one genomic region associated with Lactobacillus abundance contains the well-known anti-inflammatory cytokine Il10, which we confirmed through the analysis of Il10 knockout mice.

CONCLUSIONS

Our study provides the first evidence for a role of host genetic variation contributing to variation in the lung microbiota. This was in large part made possible through the careful curation of 16S rRNA gene amplicon data and the incorporation of a QMP-based methods. This approach to evaluating the low biomass lung environment opens new avenues for advancing lung microbiome research using animal models.

Methods in Nutrition & Gut Microbiome Research: An American Society for Nutrition Satellite Session [13 October 2022]

The microbial cells colonizing the human body form an ecosystem that is integral to the regulation and maintenance of human health. Elucidation of specific associations between the human microbiome and health outcomes is facilitating the development of microbiome-targeted recommendations and treatments (e.g., fecal microbiota transplant; pre-, pro-, and post-biotics) to help prevent and treat disease. However, the potential of such recommendations and treatments to improve human health has yet to be fully realized. Technological advances have led to the development and proliferation of a wide range of tools and methods to collect, store, sequence, and analyze microbiome samples. However, differences in methodology at each step in these analytic processes can lead to variability in results due to the unique biases and limitations of each component. This technical variability hampers the detection and validation of associations with small to medium effect sizes. Therefore, the American Society for Nutrition (ASN) Nutritional Microbiology Group Engaging Members (GEM), sponsored by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), hosted a satellite session on methods in nutrition and gut microbiome research to review currently available methods for microbiome research, best practices, as well as tools and standards to aid in comparability of methods and results. This manuscript summarizes the topics and research discussed at the session. Consideration of the guidelines and principles reviewed in this session will increase the accuracy, precision, and comparability of microbiome research and ultimately the understanding of the associations between the human microbiome and health.

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.

The Effect of Lactiplantibacillus plantarum ZZU203, Cellulase-Producing Bacillus methylotrophicus, and Their Combinations on Alfalfa Silage Quality and Bacterial Community

This study assessed the effects of Lactiplantibacillus plantarum (ZZU203), cellulase-producing Bacillus methylotrophicus (CB), or their combination (ZZU203_CB) on the fermentation parameters of alfalfa after 10 and 60 days of ensiling. Additionally, the bacterial community compositions were analyzed using absolute quantification 16S-seq (AQS). The results showed that CB silage displayed a higher lactic acid (LA) concentration at 10 d, a higher abundance of Lactobacillus, and lower abundance of Pediococcus, Enterococcus, and Weissella than those in the control (CK) silage. Compared with CK silage, the ZZU203 silage increased LA concentration, fructose and rhamnose concentrations, and the abundance of Lactobacillus, and decreased pH value, ammoniacal nitrogen, acetic acid, neutral detergent fiber and acid detergent fiber concentrations, and the abundance of Pediococcus, Enterococcus, Weissella, Hafnia, and Garciella after 60 days of ensiling. In addition, ZZU203 and ZZU203_CB silage had a similar silage quality and bacterial community, while the inoculation of ZZU203_CB significantly promoted LA accumulation and the numbers of Lactobacillus at 10 d compared with ZZU203 silage. Therefore, ZZU203 or a combination of ZZU203 and CB can be used as potential silage additives to improve the silage quality of alfalfa.

Fermentation Properties and Bacterial Community Composition of Mixed Silage of Mulberry Leaves and Smooth Bromegrass with and without Lactobacillus plantarum Inoculation

To evaluate the fermentation properties and bacterial community composition of mulberry leaves when ensiled with smooth bromegrass, and the effects of Lactobacillus plantarum inoculation on the mixed silage of mulberry leaves and smooth bromegrass, mulberry leaves were mixed with smooth bromegrass at ratios of 100:0, 90:10, 80:20, 70:30 and 60:40, and ensiled for 60 d with and without L. plantarum inoculant. The results showed that the sole fermentation of mulberry leaves failed to achieve optimum fermentation quality. Silage with a mulberry leaf ratio of 80% performed better fermentation quality compared with other non-inoculated groups, indicated by lower pH value, adequate lactic acid accumulation, and enriched proportion of Lactobacillus in the bacterial community. L. plantarum inoculation dramatically improved fermentation quality of mulberry leaf silage compared with the non-inoculated control. However, the fermentation quality of the inoculated silage decreased along with the reduction in the ratio of mulberry leaves. In conclusion, L. plantarum inoculation has the capability to improve the silage quality of mulberry leaves. Combined ensiling with smooth bromegrass could also aid in improving silage quality of mulberry leaves, with the optimum ratio of mulberry leaves being 80%.

Composition and variability of core phyllosphere fungal mycobiota on field-grown broccoli

BACKGROUND

Fresh vegetables harbor an assemblage of different microorganisms on their surfaces. The phyllosphere microbiota is important for maintaining plant health and managing crop quality before and after harvest. However, the diversity and ecology of fungal communities are largely unexplored in fresh vegetables. This study investigated the phyllosphere mycobiota of field-grown broccoli florets (n = 66) collected from 22 farms across four regions in Korea, using culturing, amplicon sequencing of the internal transcribed spacer region, and microbial network analysis.

RESULTS

Microbial network analysis identified core genera (Purpureocillium, Filobasidium, Cystofilobasidium, Papiliotrema, Aureobasidium, and unclassified genera of Capnodiales) specific to the broccoli phyllosphere. The composition and network complexity of core and unique populations varied among farming regions, and was associated with local agro-meteorological conditions. The complexity of microbial associations was higher in mature communities than in immature communities, but complexity was lost upon development of plant pathogenic disease. Broccoli mycobiota were classified according to the dominance of Purpurecillium. While Purpurecillium-type microbiota were prevalent in normal samples, Filobasidium-type microbiota were frequently observed in immature, damaged, or postharvest samples.

CONCLUSIONS

Together, fungal communities were important components of phyllosphere microbiota on fresh vegetables, and have substantial potential for exploitation to enhance and stabilize plant health and growth.

Optimization of Molecular Methods for Detecting Duckweed-Associated Bacteria

The bacterial colonization dynamics of plants can differ between phylogenetically similar bacterial strains and in the context of complex bacterial communities. Quantitative methods that can resolve closely related bacteria within complex communities can lead to a better understanding of plant-microbe interactions. However, current methods often lack the specificity to differentiate phylogenetically similar bacterial strains. In this study, we describe molecular strategies to study duckweed-associated bacteria. We first systematically optimized a bead-beating protocol to co-isolate nucleic acids simultaneously from duckweed and bacteria. We then developed a generic fingerprinting assay to detect bacteria present in duckweed samples. To detect specific duckweed-bacterium associations, we developed a genomics-based computational pipeline to generate bacterial strain-specific primers. These strain-specific primers differentiated bacterial strains from the same genus and enabled the detection of specific duckweed-bacterium associations present in a community context. Moreover, we used these strain-specific primers to quantify the bacterial colonization of duckweed by normalization to a plant reference gene and revealed differences in colonization levels between strains from the same genus. Lastly, confocal microscopy of inoculated duckweed further supported our PCR results and showed bacterial colonization of the duckweed root-frond interface and root interior. The molecular methods introduced in this work should enable the tracking and quantification of specific plant-microbe associations within plant-microbial communities.

Temporal assessment of N-cycle microbial functions in a tropical agricultural soil using gene co-occurrence networks

Microbial nitrogen (N) cycling pathways are largely responsible for producing forms of N that are available for plant uptake or lost from the system as gas or leachate. The temporal dynamics of microbial N pathways in tropical agroecosystems are not well defined, even though they are critical to understanding the potential impact of soil conservation strategies. We aimed to 1) characterize temporal changes in functional gene associations across a seasonal gradient, 2) identify keystone genes that play a central role in connecting N cycle functions, and 3) detect gene co-occurrences that remained stable over time. Soil samples (n = 335) were collected from two replicated field trials in Rwanda between September 2020 and March 2021. We found high variability among N-cycle gene relationships and network properties that was driven more by sampling timepoint than by location. Two nitrification gene targets, hydroxylamine oxidoreductase and nitrite oxidoreductase, co-occurred across all timepoints, indicating that they may be ideal year-round targets to limit nitrification in rainfed agricultural soils. We also found that gene keystoneness varied across time, suggesting that management practices to enhance N-cycle functions such as the application of nitrification inhibitors could be adapted to seasonal conditions. Our results mark an important first step in employing gene networks to infer function in soil biogeochemical cycles, using a tropical seasonal gradient as a model system.

Metagenomics reveals the response of antibiotic resistance genes to elevated temperature in the Yellow River

Climate warming may aggravate the threat of antibiotic resistance genes (ARGs) to environmental and human health. However, whether temperature can predict ARGs and influence their assembly processes remains unknown. Here, we used metagenomic sequencing to explore how gradually elevated water temperature (23 °C, 26 °C, 29 °C, 32 °C, 35 °C) influences ARG and mobile genetic element (MGE) profiles in the Yellow River. In total, 30 ARG types including 679 subtypes were detected in our water samples. Gradually increased temperature remarkably reduced ARG diversity but increased ARG abundance. Approximately 37 % of ARGs and 42 % of MGEs were predicted by temperature, while most others were not sensitive to temperature. For each 1 °C increase in temperature, the ARG abundance rose by 2133 TPM (Transcripts Per kilobase of exon model per Million mapped reads) abundance, and multidrug, tetracycline and peptide resistance genes had the fastest increases. Proteobacteria and Actinobacteria were the primary ARG hosts, with 558 and 226 ARG subtypes, respectively. Although ARG profiles were mainly governed by stochastic process, elevated temperature increased the deterministic process of ARGs in the Yellow River. The abundance of five high-risk ARGs (tetM, mecA, bacA, vatE and tetW) significantly increased with elevated water temperature, and these ARGs co-occurred with several opportunistic pathogens (Delftia, Legionella and Pseudomonas), implying that antibiotic resistance risk may increase under climate warming. Our study explored the possibility of predicting resistomes and their health risks through temperature, providing a novel approach to predict and control ARGs in water environments under climate warming.

Absolute quantification of viable bacteria abundances in food by next-generation sequencing: Quantitative NGS of viable microbes

Next-generation sequencing (NGS) is an important tool for taxonomical bacteria identification. Recent technological developments have led to its improvement and availability. Despite the undeniable advantages of this approach, it has several limitations and shortcomings. The usual outcome of microbiota sequencing is a relative abundance of bacterial taxa. The information about bacteria viability or enumeration is missing. However, this knowledge is crucial for many applications. In the current study, we elaborated the complete workflow for the absolute quantification of living bacteria based on 16S rRNA gene amplicon sequencing. A fluorescent PMAxx reagent penetrating a damaged cell membrane was used to discriminate between the total and viable bacterial population. Bacteria enumeration was estimated by the spike-in technique or qPCR quantification. For method optimization, twenty bacterial species were taken, and the results of the workflow were validated by widely accepted methodologies: flow cytometry, microbiological plating, and viability-qPCR. Despite the minor discrepancy between all methods used, they all showed compatible results. Finally, we tested the workflow with actual food samples and received a good correlation between the methods regarding the estimation of the number of viable bacteria. Overall, the elaborated and integrated NGS approach could be the next step in perceiving a holistic picture of a sample microbiota.

Effects of continuous cropping Jiashi muskmelon on rhizosphere microbial community

Introduction

The continuous cropping of crops can result in the deterioration of the soil environment and cause a decline in plant health and yield, which complicates agricultural production. However, the effects of continuous melon cropping on rhizospheric microbial communities remain poorly understood.

Methods

In this study, high-throughput absolute quantification 16S rRNA gene amplicon sequencing was employed to analyze the bacterial community structure of greenhouse rhizosphere soil from Jiashi muskmelon replanted for 0, 1, 2, and 3 years (CK, 1a, 2a, and 3a, respectively).

Results

The results showed that long- term continuous cropping caused significant changes in soil physicochemical properties. The bacterial absolute abundances increased, but the bacterial community richness and diversity were significantly lost (p < 0.05). The composition of bacterial community was more similar after 2 and 3 years of continuous cropping. The longer the continuous cropping years were, the greater the shift in the bacterial diversity and abundance. Analysis of potential functional components revealed that different bacterial groups were enriched in different continuous cropping years. The significant reduction of the taxa associated with nitrate reduction may be responsible for the loss of soil nitrogen in continuous cropping soil.

Discussion

In summary, continuous cropping had a significant impact on the bacterial community structure of Jiashi muskmelon rhizospheric soil, and these results will provide a reference for soil management and scientific fertilization of melon and other crops under a continuous cropping regime.

Factors affecting N2O fluxes from heavy metal-contaminated mangrove soils in a subtropical estuary

A 1-year field monitoring program was carried out to observe seasonal variation in N₂O fluxes at two typical mangrove wetlands in a subtropical estuary. The soils in the island-type mangrove wetland had a higher level of heavy metal(loid) contamination and a lower level of salinity compared to the small bay-type mangrove wetland. While there was a high level of similarity in the seasonal variation pattern of N₂O fluxes between the two investigated sites with both being significantly higher in summer than in other seasons, the average of N₂O fluxes in the island-type mangrove wetland was 7.19 μg·m^-2·h^-1， which tended to be lower compared to the small bay-type mangrove wetland (15.63 μg·m^-2·h^-1). Overall, N₂O flux was closely related to soil-borne heavy metal(loid)s, showing a trend to decrease with increasing concentration of these heavy metal(loid)s. The N₂O fluxes increased with decreasing abundance of either denitrifiers or nitrifiers. But the opposite was observed for the anammox bacteria present in the soils. The anammox bacteria were more sensitive to heavy metal(loid) stress but more tolerated high salinity encountered in the investigated soils compared to the denitrifiers or nitrifiers. It appears that anammox reactions mediated by anammox bacteria played a key role in affecting the spatial variation in N₂O fluxes from the mangrove soils in the study area. And an increased level of ammonium in soils tended to promote the activity of anammox bacteria and consequently enhanced N₂O emission from the mangrove soils.

synDNA-a Synthetic DNA Spike-in Method for Absolute Quantification of Shotgun Metagenomic Sequencing

Microbiome studies have the common goal of determining which microbial taxa are present, respond to specific conditions, or promote phenotypic changes in the host. Most of these studies rely on relative abundance measurements to drive conclusions. Inherent limitations of relative values are the inability to determine whether an individual taxon is more or less abundant and the magnitude of this change between the two samples. These limitations can be overcome by using absolute abundance quantifications, which can allow for a more complete understanding of community dynamics by measuring variations in total microbial loads. Obtaining absolute abundance measurements is still technically challenging. Here, we developed synthetic DNA (synDNA) spike-ins that enable precise and cost-effective absolute quantification of microbiome data by adding defined amounts of synDNAs to the samples. We designed 10 synDNAs with the following features: 2,000-bp length, variable GC content (26, 36, 46, 56, or 66% GC), and negligible identity to sequences found in the NCBI database. Dilution pools were generated by mixing the 10 synDNAs at different concentrations. Shotgun metagenomic sequencing showed that the pools of synDNAs with different percentages of GC efficiently reproduced the serial dilution, showing high correlation (r = 0.96; R² ≥ 0.94) and significance (P < 0.01). Furthermore, we demonstrated that the synDNAs can be used as DNA spike-ins to generate linear models and predict with high accuracy the absolute number of bacterial cells in complex microbial communities. IMPORTANCE The synDNAs designed in this study enable accurate and reproducible measurements of absolute amount and fold changes of bacterial species in complex microbial communities. The method proposed here is versatile and promising as it can be applied to bacterial communities or genomic features like genes and operons, in addition to being easily adaptable by other research groups at a low cost. We also made the synDNAs' sequences and the plasmids available to encourage future application of the proposed method in the study of microbial communities.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Challenges and perspectives of quantitative microbiome profiling in food fermentations

Spontaneously fermented foods are consumed and appreciated for thousands of years although they are usually produced with fluctuated productivity and quality, potentially threatening both food safety and food security. To guarantee consistent fermentation productivity and quality, it is essential to control the complex microbiota, the most crucial factor in food fermentations. The prerequisite for the control is to comprehensively understand the structure and function of the microbiota. How to quantify the actual microbiota is of paramount importance. Among various microbial quantitative methods evolved, quantitative microbiome profiling, namely to quantify all microbial taxa by absolute abundance, is the best method to understand the complex microbiota, although it is still at its pioneering stage for food fermentations. Here, we provide an overview of microbial quantitative methods, including the development from conventional methods to the advanced quantitative microbiome profiling, and the application examples of these methods. Moreover, we address potential challenges and perspectives of quantitative microbiome profiling methods, as well as future research needs for the ultimate goal of rational and optimal control of microbiota in spontaneous food fermentations. Our review can serve as reference for the traditional food fermentation sector for stable fermentation productivity, quality and safety.

Nodulation and nitrogen fixation in Medicago truncatula strongly alters the abundance of its root microbiota and subtly affects its structure

The plant common symbiosis signalling (SYM) pathway has shared function between interactions with rhizobia and arbuscular mycorrhizal fungi, the two most important symbiotic interactions between plants and microorganisms that are crucial in plant and agricultural yields. Here, we determine the role of the plant SYM pathway in the structure and abundance of the microbiota in the model legume Medicago truncatula and whether this is controlled by the nitrogen or phosphorus status of the plant. We show that SYM mutants (dmi3) differ substantially from the wild type (WT) in the absolute abundance of the root microbiota, especially under nitrogen limitation. Changes in the structure of the microbiota were less pronounced and depended on both plant genotype and nutrient status. Thus, the SYM pathway has a major impact on microbial abundance in M. truncatula and also subtly alters the composition of the microbiota.

Diverse MarR bacterial regulators of auxin catabolism in the plant microbiome

Chemical signalling in the plant microbiome can have drastic effects on microbial community structure, and on host growth and development. Previously, we demonstrated that the auxin metabolic signal interference performed by the bacterial genus Variovorax via an auxin degradation locus was essential for maintaining stereotypic root development in an ecologically relevant bacterial synthetic community. Here, we dissect the Variovorax auxin degradation locus to define the genes iadDE as necessary and sufficient for indole-3-acetic acid (IAA) degradation and signal interference. We determine the crystal structures and binding properties of the operon's MarR-family repressor with IAA and other auxins. Auxin degradation operons were identified across the bacterial tree of life and we define two distinct types on the basis of gene content and metabolic products: iac-like and iad-like. The structures of MarRs from representatives of each auxin degradation operon type establish that each has distinct IAA-binding pockets. Comparison of representative IAA-degrading strains from diverse bacterial genera colonizing Arabidopsis plants show that while all degrade IAA, only strains containing iad-like auxin-degrading operons interfere with auxin signalling in a complex synthetic community context. This suggests that iad-like operon-containing bacterial strains, including Variovorax species, play a key ecological role in modulating auxins in the plant microbiome.

Absolute quantification and genome-centric analyses elucidate the dynamics of microbial populations in anaerobic digesters

Anaerobic digestion (AD) relies on myriads of functions performed by complex microbial communities in customized settings, thus, a comprehensive investigation on the AD microbiome is central to the fine-tuned control. Most current AD microbiome studies are based on relative abundance, which hinders the interpretation of microbes' dynamics and inter-sample comparisons. Here, we developed an absolute quantification (AQ) approach that integrated cellular spike-ins with metagenomic sequencing to elucidate microbial community variations and population dynamics in four anaerobic digesters. Using this method, 253 microbes were defined as decaying populations with decay rates ranging from -0.05 to -5.85 d^-1, wherein, a population from Flavobacteriaceae family decayed at the highest rates of -3.87 to -5.85 d^-1 in four digesters. Meanwhile, 25 microbes demonstrated the growing trend in the AD processes with growth rates ranging from 0.11 to 1.77 d^-1, and genome-centric analysis assigned some of the populations to the functional niches of hydrolysis, short-chain fatty acids metabolism, and methane generation. Additionally, we observed that the specific activity of methanogens was lower in the prolonged digestion stage, and redundancy analysis revealed that the feedstock composition and the digestion duration were the two key parameters in governing the AD microbial compositions.