Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing

Exploring microbial diversity in the rhizosphere: a comprehensive review of metagenomic approaches and their applications

The rhizosphere, the soil region influenced by plant roots, represents a dynamic microenvironment where intricate interactions between plants and microorganisms shape soil health, nutrient cycling, and plant growth. Soil microorganisms are integral players in the transformation of materials, the dynamics of energy flows, and the intricate cycles of biogeochemistry. Considerable research has been dedicated to investigating the abundance, diversity, and intricacies of interactions among different microbes, as well as the relationships between plants and microbes present in the rhizosphere. Metagenomics, a powerful suite of techniques, has emerged as a transformative tool for dissecting the genetic repertoire of complex microbial communities inhabiting the rhizosphere. The review systematically navigates through various metagenomic approaches, ranging from shotgun metagenomics, enabling unbiased analysis of entire microbial genomes, to targeted sequencing of the 16S rRNA gene for taxonomic profiling. Each approach's strengths and limitations are critically evaluated, providing researchers with a nuanced understanding of their applicability in different research contexts. A central focus of the review lies in the practical applications of rhizosphere metagenomics in various fields including agriculture. By decoding the genomic content of rhizospheric microbes, researchers gain insights into their functional roles in nutrient acquisition, disease suppression, and overall plant health. The review also addresses the broader implications of metagenomic studies in advancing our understanding of microbial diversity and community dynamics in the rhizosphere. It serves as a comprehensive guide for researchers, agronomists, and policymakers, offering a roadmap for harnessing metagenomic approaches to unlock the full potential of the rhizosphere microbiome in promoting sustainable agriculture.

Metagenomics approaches in unveiling the dynamics of Plant Growth-Promoting Microorganisms (PGPM) vis-à-vis Phytophthora sp. suppression in various crop ecological systems

Phytophthora species are destructive pathogens causing yield losses in different ecological systems, such as potato, black pepper, pepper, avocado, citrus, and tobacco. The diversity of plant growth-promoting microorganisms (PGPM) plays a crucial role in disease suppression. Knowledge of metagenomics approaches is essential for assessing the dynamics of PGPM and Phytophthora species across various ecosystems, facilitating effective management strategies for better crop protection. This review discusses the dynamic interplay between PGPM and Phytophthora sp. using metagenomics approaches that sheds light on the potential of PGPM strains tailored to specific crop ecosystems to bolster pathogen suppressiveness.

Effects of environmentally relevant concentrations of florfenicol on the glucose metabolism system, intestinal microbiome, and liver metabolome of zebrafish

Florfenicol, a widely used veterinary antibiotic, has now been frequently detected in various water environments and human urines, with high concentrations. Accordingly, the ecological risks and health hazards of florfenicol are attracting increasing attention. In recent years, antibiotic exposure has been implicated in the disruption of animal glucose metabolism. However, the specific effects of florfenicol on the glucose metabolism system and the underlying mechanisms are largely unknown. Herein, zebrafish as an animal model were exposed to environmentally relevant concentrations of florfenicol for 28 days. Using biochemical and molecular analyses, we found that exposure to florfenicol disturbed glucose homeostasis, as evidenced by the abnormal levels of blood glucose and hepatic/muscular glycogen, and the altered expression of genes involved in glycogenolysis, gluconeogenesis, glycogenesis, and glycolysis. Considering the efficient antibacterial activity of florfenicol and the crucial role of intestinal flora in host glucose metabolism, we then analyzed changes in the gut microbiome and its key metabolite short-chain fatty acids (SCFAs). Results indicated that exposure to florfenicol caused gut microbiota dysbiosis, inhibited the production of intestinal SCFAs, and ultimately affected the downstream signaling pathways of SCFA involved in glucose metabolism. Moreover, non-targeted metabolomics revealed that arachidonic acid and linoleic acid metabolic pathways may be associated with insulin sensitivity changes in florfenicol-exposed livers. Overall, this study highlighted a crucial aspect of the environmental risks of florfenicol to both non-target organisms and humans, and presented novel insights into the mechanistic elucidation of metabolic toxicity of antibiotics.

Prevalence of opportunistic pathogens and anti-microbial resistance in urban aquaculture ponds

Bacterial antimicrobial resistance (AMR) has emerged as a significant concern worldwide. The microbial community profile and potential AMR level in aquaculture ponds are often undervalued and attract less attention than other aquatic environments. We used amplicon and metagenomic shotgun sequencing to study microbial communities and AMR in six freshwater polyculture ponds in rural and urban areas of Bangladesh. Amplicon sequencing revealed different community structures between rural and urban ponds, with urban ponds having a higher bacterial diversity and opportunistic pathogens including Streptococcus, Staphylococcus, and Corynebacterium. Despite proteobacterial dominance, Firmicutes was the most interactive in the community network, especially in the urban ponds. Metagenomes showed that drug resistance was the most common type of AMR found, while metal resistance was only observed in urban ponds. AMR and metal resistance genes were found mainly in beta and gamma-proteobacteria in urban ponds, while AMR was found primarily in alpha-proteobacteria in rural ponds. We identified potential pathogens with a high profile of AMR and metal resistance in urban aquaculture ponds. As these ponds provide a significant source of protein for humans, our results raise significant concerns for the environmental sustainability of this food source and the dissemination of AMR into the food chain.

Environmental DNA: The next chapter

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.

Characteristics of Citrate-Esterified Starch and Enzymatically Debranched Starch and Their Effects on Diabetic Mice

Chickpea has significant benefits as an adjuvant treatment for type 2 diabetes mellitus (T2DM). The properties of chickpea resistant starches (RSs) and their abilities to reduce T2DM symptoms and control intestinal flora were investigated. The RS content in citrate-esterified starch (CCS; 74.18%) was greater than that in pullulanase-modified starch (enzymatically debranched starch (EDS); 38.87%). Compared with those of native chickpea starch, there were noticeable changes in the granular structure and morphology of the two modified starches. The CCS showed surface cracking and aggregation. The EDS particles exhibited irregular layered structures. The expansion force of the modified starches decreased. The CCS and EDS could successfully lower blood glucose, regulate lipid metabolism, lower the levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C), reduce the expressions of interleukin-6 (IL-6) and interleuki n-10 (IL-10), and decrease diabetes-related liver damage. Moreover, the CCS and EDS altered the intestinal flora makeup in mice with T2DM. The abundance of Bacteroidota increased. Both types of chickpea RSs exhibited significant hypoglycaemic and hypolipidaemic effects, contributing to the reduction in inflammatory levels and the improvement in gut microbiota balance.

Metagenomics insights into microbiome and antibiotic resistance genes from free living amoeba in chlorinated wastewater effluents

Free living amoeba (FLA) are among the organisms commonly found in wastewater and are well-established hosts for diverse microbial communities. Despite its clinical significance, there is little knowledge on the FLA microbiome and resistome, with previous studies relying mostly on conventional approaches. In this study we comprehensively analyzed the microbiome, antibiotic resistome and virulence factors (VFs) within FLA isolated from final treated effluents of two wastewater treatment plants (WWTPs) using shotgun metagenomics. Acanthamoeba has been identified as the most common FLA, followed by Entamoeba. The bacterial diversity showed no significant difference (p > 0.05) in FLA microbiomes obtained from the two WWTPs. At phylum level, the most dominant taxa were Proteobacteria, followed by Firmicutes and Actinobacteria. The most abundant genera identified were Enterobacter followed by Citrobacter, Paenibacillus, and Cupriavidus. The latter three genera are reported here for the first time in Acanthamoeba. In total, we identified 43 types of ARG conferring resistance to cephalosporins, phenicol, streptomycin, trimethoprim, quinolones, cephalosporins, tigecycline, rifamycin, and kanamycin. Similarly, a variety of VFs in FLA metagenomes were detected which included flagellar proteins, Type IV pili twitching motility proteins (pilH and rpoN), alginate biosynthesis genes AlgI, AlgG, AlgD and AlgW and Type VI secretion system proteins and general secretion pathway proteins (tssM, tssA, tssL, tssK, tssJ, fha, tssG, tssF, tssC and tssB, gspC, gspE, gspD, gspF, gspG, gspH, gspI, gspJ, gspK, and gspM). To the best of our knowledge, this is the first study of its kind to examine both the microbiomes and resistome in FLA, as well as their potential pathogenicity in treated effluents. Additionally, this study showed that FLA can host a variety of potentially pathogenic bacteria including Paenibacillus, and Cupriavidus that had not previously been reported, indicating that their relationship may play a role in the spread and persistence of antibiotic resistant bacteria (ARBs) and antibiotic resistance genes (ARGs) as well as the evolution of novel pathogens.

Dead in the water - Role of relic DNA and primer choice for targeted sequencing surveys of anaerobic sewage sludge intended for biological monitoring

DNA-based monitoring of microbial communities that are responsible for the performance of anaerobic digestion of sewage wastes has the potential to improve resource recoveries for wastewater treatment facilities. By treating sludge with propidium monoazide (PMA) prior to amplicon sequencing, this study explored how the presence of DNA from dead microbial biomass carried over with feed sludge may mislead process-relevant biomarkers, and whether primer choice impacts such assessments. Four common primers were selected for amplicon preparation, also to determine if universal primers have sufficient taxonomic or functional coverage for monitoring ecological performance; or whether two domain-specific primers for Bacteria and Archaea are necessary. Anaerobic sludges of three municipal continuously stirred-tank reactors in Victoria, Australia, were sampled at one time-point. A total of 240 amplicon libraries were sequenced on a Miseq using two universal and two domain-specific primer pairs. Untargeted metabolomics was chosen to complement biological interpretation of amplicon gene-based functional predictions. Diversity, taxonomy, phylogeny and functional potentials were systematically assessed using PICRUSt2, which can predict community wide pathway abundance. The two chosen universal primers provided similar diversity profiles of abundant Bacteria and Archaea, compared to the domain-specific primers. About 16 % of all detected prokaryotic genera covering 30 % of total abundances and 6 % of PICRUSt2-estimated pathway abundances were affected by PMA. This showed that dead biomass in the anaerobic digesters impacted DNA-based assessments, with implications for predicting active processes, such as methanogenesis, denitrification or the identification of organisms associated with biological foams. Hence, instead of running two sequencing runs with two different domain-specific primers, we propose conducting PMA-seq with universal primer pairs for routine performance monitoring. However, dead sludge biomass may have some predictive value. In principal component analysis the compositional variation of 239 sludge metabolites resembled that of 'dead-plus-alive' biomass, suggesting that dead organisms contributed to the potentially process-relevant sludge metabolome.

Longitudinal analysis of the skin microbiome in association with hand eczema, hand hygiene practices and moisturizer use

BACKGROUND

The skin microbiota maintains a physical and immunological barrier to the environment. Little is known about how the microbiome changes over time or the effect of hand hygiene practices and moisturizer use.

OBJECTIVES

To assess sex-specific changes in skin bacteria over time, and how the microbiome is related to self-reported hand eczema, hand hygiene practices and use of moisturizers.

METHODS

Swab samples from the dorsal hand were collected at baseline and 6.5 years later during the COVID-19 pandemic, in 168 participants from the RHINESSA study in Bergen, Norway. The skin samples were analysed by 16S rRNA amplicon sequencing.

RESULTS

The alpha diversity of the hand microbiome increased from baseline to follow-up, and beta diversity differed by sex at both time points. The relative abundance increased for several bacteria from baseline to follow-up, with sex-specific differences. Current hand eczema and aggravating hand eczema during the COVID-19 pandemic were associated with an increase in Staphylococcus. High hand washing frequency at home was associated with lower alpha diversity and with higher abundance of Staphylococcus, Corynebacterium, Finegoldia, and Pseudomonas and lower abundance of Propionibacterium and Pelomonas. The alpha diversity increased with increasing time passing between hand washing and sampling, whereas more frequent moisturizer use was associated with significantly lower alpha diversity, and a change in abundance for some bacteria, such as more Pseudomonas.

CONCLUSIONS

This longitudinal study revealed an overall increase in skin microbial diversity over a 6-year period, which was unexpected since follow-up was performed during the COVID-19 pandemic when vigorous hand hygienic practices were introduced. Sex-specific differences were identified at both time points. Individuals with hand eczema seem to develop a more dysbiotic skin bacterial community over time. Hand washing and use of moisturizers, with typically gender-specific habitual patterns, may lead to change in bacterial composition.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Analysis of fecal microbiome and metabolome changes in goats with pregnant toxemia

BACKGROUND

Pregnancy toxemia is a common disease, which occurs in older does that are pregnant with multiple lambs in the third trimester. Most of the sick goats die within a few days, which can seriously impact the economic benefits of goat breeding enterprises. The disease is believed to be caused by malnutrition, stress, and other factors, that lead to the disorder of lipid metabolism, resulting in increased ketone content, ketosis, ketonuria, and neurological symptoms. However, the changes in gut microbes and their metabolism in this disease are still unclear. The objective of this experiment was to evaluate the effect of toxemia of pregnancy on the fecal microbiome and metabolomics of does.

RESULTS

Eight pregnant does suspected of having toxemia of pregnancy (PT group) and eight healthy does during the same pregnancy (NC group) were selected. Clinical symptoms and pathological changes at necropsy were observed, and liver tissue samples were collected for pathological sections. Jugular venous blood was collected before morning feeding to detect biochemical indexes. Autopsy revealed that the liver of the pregnancy toxemia goat was enlarged and earthy yellow, and the biochemical results showed that the serum levels of aspartate aminotransferase (AST) and β-hydroxybutyric acid (B-HB) in the PT group were significantly increased, while calcium (Ca) levels were significantly reduced. Sections showed extensive vacuoles in liver tissue sections. The microbiome analysis found that the richness and diversity of the PT microbiota were significantly reduced. Metabolomic analysis showed that 125 differential metabolites were screened in positive ion mode and enriched in 12 metabolic pathways. In negative ion mode, 100 differential metabolites were screened and enriched in 7 metabolic pathways.

CONCLUSIONS

Evidence has shown that the occurrence of pregnancy toxemia is related to gut microbiota, and further studies are needed to investigate its pathogenesis and provide research basis for future preventive measures of this disease.

Bioinformatic Workflows for Metaproteomics

The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.

Genetic Diversity of Grapevine Virus A in Three Australian Vineyards Using Amplicon High Throughput Sequencing (Amplicon-HTS)

Shiraz disease (SD) is one of the most destructive viral diseases of grapevines in Australia and is known to cause significant economic loss to local growers. Grapevine virus A (GVA) was reported to be the key pathogen associated with this disease. This study aimed to better understand the diversity of GVA variants both within and between individual SD and grapevine leafroll disease (LRD) affected grapevines located at vineyards in South Australia. Amplicon high throughput sequencing (Amplicon-HTS) combined with median-joining networks (MJNs) was used to analyze the variability in specific gene regions of GVA variants. Several GVAII variant groups contain samples from both vineyards studied, suggesting that these GVAII variants were from a common origin. Variant groups analyzed by MJNs using the overall data set denote that there may be a possible relationship between variant groups of GVA and the geographical location of the grapevines.

A systematic review exploring the association between the human gut microbiota and brain connectivity in health and disease

A body of pre-clinical evidence shows how the gut microbiota influence brain functioning, including brain connectivity. Linking measures of brain connectivity to the gut microbiota can provide important mechanistic insights into the bi-directional gut-brain communication. In this systematic review, we therefore synthesized the available literature assessing this association, evaluating the degree of consistency in microbiota-connectivity associations. Following the PRISMA guidelines, a PubMed search was conducted, including studies published up to September 1, 2022. We identified 16 studies that met the inclusion criteria. Several bacterial genera, including Prevotella, Bacteroides, Ruminococcus, Blautia, and Collinsella were most frequently reported in association with brain connectivity. Additionally, connectivity of the salience (specifically the insula and anterior cingulate cortex), default mode, and frontoparietal networks were most frequently associated with the gut microbiota, both in terms of microbial diversity and composition. There was no discernible pattern in the association between microbiota and brain connectivity. Altogether, based on our synthesis, there is evidence for an association between the gut microbiota and brain connectivity. However, many findings were poorly replicated across studies, and the specificity of the association is yet unclear. The current studies show substantial inter-study heterogeneity in methodology and reporting, limiting the robustness and reproducibility of the findings and emphasizing the need to harmonize methodological approaches. To enhance comparability and replicability, future research should focus on further standardizing processing pipelines and employing data-driven multivariate analysis strategies.

Processing and storage methods affect oral and gut microbiome composition

In microbiome studies, fecal and oral samples are stored and processed in different ways, which could affect the observed microbiome composition. In this study, we compared storage and processing methods applied to samples prior to DNA extraction to determine how each affected microbial community diversity as assessed by 16S rRNA gene sequencing. We collected dental swabs, saliva, and fecal samples from 10 individuals, with three technical replicates per condition. We assessed four methods of storing and processing fecal samples prior to DNA extraction. We also compared different fractions of thawed saliva and dental samples to fresh samples. We found that lyophilized fecal samples, fresh whole saliva samples, and the supernatant fraction of thawed dental samples had the highest levels of alpha diversity. The supernatant fraction of thawed saliva samples had the second highest evenness compared to fresh saliva samples. Then, we investigated the differences in observed community composition at the domain and phylum levels and identified the amplicon sequence variants (ASVs) that significantly differed in relative abundance between the conditions. Lyophilized fecal samples had a greater prevalence of Archaea as well as a greater ratio of Firmicutes to Bacteroidetes compared to the other conditions. Our results provide practical considerations not only for the selection of storage and processing methods but also for comparing results across studies. Differences in processing and storage methods could be a confounding factor influencing the presence, absence, or differential abundance of microbes reported in conflicting studies.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Impact of Experimental Bias on Compositional Analysis of Microbiome Data

Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Advances and perspectives of using stable isotope probing (SIP)-based technologies in contaminant biodegradation

Stable isotope probing (SIP) is a powerful tool to study microbial community structure and function in both nature and engineered environments. Coupling with advanced genomics and other techniques, SIP studies have generated substantial information to allow researchers to draw a clearer picture of what is occurring in complex microbial ecosystems. This review provides an overview of the advances of SIP-based technologies over time, summarizes the status of SIP applications to contaminant biodegradation, provides critical perspectives on ecological interactions within the community, and important factors (controllable and non-controllable) to be considered in SIP experimental designs and data interpretation. Current trend and perspectives of adapting SIP techniques for environmental applications are also discussed.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Dynamic of active microbial diversity in rhizosphere sediments of halophytes used for bioremediation of earthen shrimp ponds

BACKGROUND

In New-Caledonia, at the end of each shrimp production cycle, earthen ponds are drained and dried to enhance microbial decomposition of nutrient-rich waste trapped in the sediment during the rearing. However, excessive ponds drying may not be suitable for the decomposition activities of microorganisms. Halophytes, salt tolerant plants, naturally grow at vicinity of shrimp ponds; due to their specificity, we explored whether halophytes cultivation during the pond drying period may be suitable for pond bioremediation. In addition, plants are closely associated with microorganisms, which may play a significant role in organic matter decomposition and therefore in bioremediation. Thus, in this study we aimed to determine the impact of 3 halophyte species (Suaeda australis, Sarcocornia quinqueflora and Atriplex jubata) on active sediment microbial communities and their implications on organic matter degradation.

RESULTS

Drying significantly decreased the microbial diversity index compared to those of wet sediment or sediment with halophytes. Microbial profiles varied significantly over time and according to the experimental conditions (wet, dry sediment or sediment with halophyte species). Halophytes species seemed to promote putative microbial metabolism activities in the sediment. Taxa related to nitrogen removal, carbon mineralisation, sulphur reduction and sulphide oxidation were significant biomarkers in sediment harbouring halophytes and may be relevant for bioremediation. Whereas microbial communities of dry sediment were marked by soil limited-moisture taxa with no identification of microbial metabolic functions. Nitrogen reduction in sediments was evidenced in wet sediment and in sediments with halophytes cultures, along with putative microbial denitrification activities. The greatest nitrogen reduction was observed in halophytes culture.

CONCLUSION

The efficiency of sediment bioremediation by halophytes appears to be the result of both rhizosphere microbial communities and plant nutrition. Their cultures during the pond drying period may be used as aquaculture diversification by being a sustainable system.

Processing and Storage Methods Affect Oral and Gut Microbiome Composition

Across microbiome studies, fecal and oral samples are stored and processed in different ways, which could affect the observed microbiome composition. Here, we compared treatment methods, which included both storage conditions and processing methods, applied to samples prior to DNA extraction to determine how each affects microbial community diversity as assessed by 16S rRNA gene sequencing. We collected dental swab, saliva, and fecal samples from 10 individuals, with three technical replicates per treatment method. We assessed four methods of processing fecal samples prior to DNA extraction. We also compared different fractions of frozen saliva and dental samples to fresh samples. We found that lyophilized fecal samples, fresh whole saliva samples, and the supernatant fraction of thawed dental samples retained the highest levels of alpha diversity in samples. The supernatant fraction of thawed saliva samples had the second highest alpha diversity compared to fresh. Then we investigated the differences in microbes between different treatments at the domain and phylum levels as well as identified the amplicon sequence variants (ASVs) that were significantly different between the methods producing the highest alpha diversity and the other treatment methods. Lyophilized fecal samples had a greater prevalence of Archaea as well as a greater ratio of Firmicutes to Bacteroidetes compared to the other treatment methods. Our results provide practical considerations, not only for selection of processing method, but also for comparing results across studies that use these methods. Our findings also indicate differences in treatment method could be a confounding factor influencing the presence, absence, or differential abundance of microbes reported in conflicting studies.

Sample Collection, DNA Extraction, and Library Construction Protocols of the Human Microbiome Studies in the International Human Phenome Project

The human microbiome plays a crucial role in human health. In the past decade, advances in high-throughput sequencing technologies and analytical software have significantly improved our knowledge of the human microbiome. However, most studies concerning the human microbiome did not provide repeatable protocols to guide the sample collection, handling, and processing procedures, which impedes obtaining valid and timely microbial taxonomic and functional results. This protocol provides detailed operation methods of human microbial sample collection, DNA extraction, and library construction for both the amplicon sequencing-based measurements of the microbial samples from the human nasal cavity, oral cavity, and skin, as well as the shotgun metagenomic sequencing-based measurements of the human stool samples among adult participants. This study intends to develop practical procedure standards to improve the reproducibility of microbiota profiling of human samples.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00097-y.

Molecular insights informing factors affecting low temperature anaerobic applications: Diversity, collated core microbiomes and complexity stability relationships in LCFA-fed systems

Fats, oil and grease, and their hydrolyzed counterparts-long chain fatty acids (LCFA) make up a large fraction of numerous wastewaters and are challenging to degrade anaerobically, more so, in low temperature anaerobic digestion (LtAD) systems. Herein, we perform a comparative analysis of publicly available Illumina 16S rRNA datasets generated from LCFA-degrading anaerobic microbiomes at low temperatures (10 and 20 °C) to comprehend the factors affecting microbial community dynamics. The various factors considered were the inoculum, substrate and operational characteristics, the reactor operation mode and reactor configuration, and the type of nucleic acid sequenced. We found that LCFA-degrading anaerobic microbiomes were differentiated primarily by inoculum characteristics (inoculum source and morphology) in comparison to the other factors tested. Inoculum characteristics prominently shaped the species richness, species evenness and beta-diversity patterns in the microbiomes even after long term operation of continuous reactors up to 150 days, implying the choice of inoculum needs careful consideration. The generalised additive models represented through beta diversity contour plots revealed that psychrophilic bacteria RBG-13-54-9 from family Anaerolineae, and taxa WCHB1-41 and Williamwhitmania were highly abundant in LCFA-fed microbial niches, suggesting their role in anaerobic treatment of LCFAs at low temperatures of 10-20 °C. Overall, we showed that the following bacterial genera: uncultured Propionibacteriaceae, Longilinea, Christensenellaceae R7 group, Lactivibrio, candidatus Caldatribacterium, Aminicenantales, Syntrophus, Syntrophomonas, Smithella, RBG-13-54-9, WCHB1-41, Trichococcus, Proteiniclasticum, SBR1031, Lutibacter and Lentimicrobium have prominent roles in LtAD of LCFA-rich wastewaters at 10-20 °C. This study provides molecular insights of anaerobic LCFA degradation under low temperatures from collated datasets and will aid in improving LtAD systems for treating LCFA-rich wastewaters.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Single-Cell Techniques in Environmental Microbiology

Environmental microbiology has been an essential part of environmental research because it provides effective solutions to most pollutants. Hence, there is an interest in investigating microorganism behavior, such as observation, identification, isolation of pollutant degraders, and interactions between microbial species. To comprehensively understand cell heterogeneity, diverse approaches at the single-cell level are demanded. Thus far, the traditional bulk biological tools such as petri dishes are technically challenging for single cells, which could mask the heterogeneity. Single-cell technologies can reveal complex and rare cell populations by detecting heterogeneity among individual cells, which offers advantages of higher resolution, higher throughput, more accurate analysis, etc. Here, we overviewed several single-cell techniques on observation, isolation, and identification from aspects of methods and applications. Microscopic observation, sequencing identification, flow cytometric identification and isolation, Raman spectroscopy-based identification and isolation, and their applications are mainly discussed. Further development on multi-technique integrations at the single-cell level may highly advance the research progress of environmental microbiology, thereby giving more indication in the environmental microbial ecology.

Environmental DNA Sequencing to Monitor Restoration Practices on Soil Bacterial and Archaeal Communities in Soils Under Desertification in the Brazilian Semiarid

Soils from Brazilian semiarid regions are highly vulnerable to desertification due to their geology, climate, human actions, and intensive land use that contribute to desertification. Therefore, areas under desertification have increased in the Brazilian semiarid region and it has negatively changed the soil bacterial and archaeal communities and their functionality. On the other hand, although restoration strategies are expensive and there are few soils restoration programs, some practices have been applied to restore these soils under desertification. For instance, conservationist practices and grazing exclusion have been strategically implemented, and they created a new altered soil condition for soil microbial communities, boosting soil microbial diversity. Here, we discuss the potential of these restoration strategies to recover the richness and diversity of soil bacterial and archaeal communities that were described through environmental DNA (eDNA) sequencing of soil samples. eDNA sequencing results show that areas where restoration strategies have been applied in regions under desertification in the Brazilian semiarid have increased species richness, diversity, and structure of the bacterial and archaeal community. In addition, network connectivity and functionality of the soil microorganisms have been improved over time. Altogether, we show that management strategies for soil restoration have positive effects on soil microbial communities and these effects can be monitored using the eDNA sequencing approach.

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data

Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn's disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn's disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set. IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer.

Distinct Bacterial Communities in São Jorge Cheese with Protected Designation of Origin (PDO)

São Jorge cheese is an iconic product of the Azores, produced from raw cow's milk and natural whey starter (NWS). Although it is produced according to Protected Designation of Origin (PDO) specifications, the granting of the PDO label depends crucially on sensory evaluation by trained tasters. The aim of this work was to characterize the bacterial diversity of this cheese using next-generation sequencing (NGS) and to identify the specific microbiota that contributes most to its uniqueness as a PDO by distinguishing the bacterial communities of PDO and non-PDO cheeses. The NWS and curd microbiota was dominated by Streptococcus and Lactococcus, whereas Lactobacillus and Leuconostoc were also present in the core microbiota of the cheese along with these genera. Significant differences (p < 0.05) in bacterial community composition were found between PDO cheese and non-certified cheese; Leuconostoc was found to play the chief role in this regard. Certified cheeses were richer in Leuconostoc, Lactobacillus and Enterococcus, but had fewer Streptococcus (p < 0.05). A negative correlation was found between contaminating bacteria, e.g., Staphylococcus and Acinetobacter, and the development of PDO-associated bacteria such as Leuconostoc, Lactobacillus and Enterococcus. A reduction in contaminating bacteria was found to be crucial for the development of a bacterial community rich in Leuconostoc and Lactobacillus, thus justifying the PDO seal of quality. This study has helped to clearly distinguish between cheeses with and without PDO based on the composition of the bacterial community. The characterization of the NWS and the cheese microbiota can contribute to a better understanding of the microbial dynamics of this traditional PDO cheese and can help producers interested in maintaining the identity and quality of São Jorge PDO cheese.

Investigation of the Influence of Stress on Label-Free Bacterial Surface-Enhanced Raman Spectra

Label-free surface-enhanced Raman spectroscopy (SERS) has been proposed as a promising bacterial detection technique. However, the quality of the collected bacterial spectra can be affected by the time between sample acquisition and the SERS measurement. This study evaluated how storage stress stimuli influence the label-free SERS spectra of Pseudomonas syringae samples stored in phosphate buffered saline. The results indicate that when faced with nutrient limitations and changes in osmatic pressure, samples at room temperature (25 °C) exhibit more significant spectral changes than those stored at cold temperature (4 °C). At higher temperatures, bacterial communities secrete extracellular biomolecules that induce programmed cell death and result in increases in the supernatant SERS signals. Surviving cells consume cellular components to support their metabolism, thus leading to measurable declines in cell SERS intensity. Two-dimensional correlation spectroscopy analysis suggests that cellular component signatures decline sequentially in the following order: proteins, nucleic acids, and lipids. Extracellular nucleic acids, proteins, and carbohydrates are secreted in turn. After subtracting the SERS changes resulting from storage, we evaluated bacterial response to viral infection. P. syringae SERS profile changes enable accurate bacteriophage Phi6 quantification over the range of 10⁴-10¹⁰ PFU/mL. The results indicate that storage conditions impact bacterial label-free SERS signals and that such influences need to be accounted for and if possible avoided when detecting bacteria or evaluating bacterial response to stress stimuli.

Bark-inhabiting fungal communities of European chestnut undergo substantial alteration by canker formation following chestnut blight infection

Background

Chestnut forests are severely threatened by chestnut blight caused by the fungal pathogen Cryphonectria parasitica and the infected trees exhibit bark canker in the later stage of the disease. European chestnut (Castanea sativa) is further infected by Gnomoniopsis smithogilvyi, another canker-causing fungal pathogen. We explored whether and how chestnut blight is reflected in bark-inhabiting fungal communities of European chestnut and also assessed the co-occurrence of C. parasitica and G. smithogilvyi.

Materials and methods

We initially investigated the fungal communities of European chestnut bark tissues and further monitored changes in these fungal communities with regard to disease progression from infection to canker formation by analyzing bark samples from asymptomatic trees, asymptomatic trees with latent C. parasitica infection, and infected trees with canker tissues, using amplicon sequencing of the ITS2 region of rDNA.

Results

The results showed that fungal community composition and diversity differed between the sample types. The fungal community composition was substantially reshaped by canker formation, whereas latent C. parasitica infection and more specifically pre-canker infection period per se had a weak effect. Fungal communities of canker samples was less diverse and more dissimilar to those of other sample types. C. parasitica dominated the mycobiome of canker samples, whereas G. smithogilvyi was found in only 9% of canker samples at very low abundances. However, G. smithogilvyi was a dominant fungus in the bark of healthy plants.

Conclusion

This study highlights that canker formation is the principal driver of decreasing diversity and altered composition of the mycobiome in bark tissues of European chestnut infected by C. parasitica infection. It additionally emphasizes the scarce co-occurrence of C. parasitica and G. smithogilvyi on European chestnut.

Signal and noise in metabarcoding data

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing-both of which we can model-but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Amplicon Sequencing Pipelines in Metagenomics

Taxonomic profiling among a large number of samples is a fundamental task during amplicon sequencing analysis. The heterogeneity and technical noises in the sample handling, library preparation, and sequencing present a major challenge to how the biological conclusions are drawn from the data analysis, and accordingly, many tools have been developed to address specific issues related to each step of the data analysis. Nowadays, several sophisticated computational pipelines with flexible parameters are made available to provide one-stop comprehensive solutions by integrating various tools, which significantly mitigate the burden imposed by the complexity of the metagenomics data analysis. This chapter discusses the best practices related to the data generation and describes bioinformatics approaches to achieving greater accuracy from data processing. It offers two independent stepwise pipelines using mothur and DADA2 in a parallel way, presents the basic principles in the key steps of the analysis, and enables the comparisons between the two pipelines straightforwardly.

Gut microbiota links with cognitive impairment in amyotrophic lateral sclerosis: a multi-omics study

Recently, cognitive impairments (CI) and behavioral abnormalities in patients with amyotrophic lateral sclerosis (ALS) have been reported. However, the underlying mechanisms have been poorly understood. In the current study, we explored the role of gut microbiota in CI of ALS patients. We collected fecal samples from 35 ALS patients and 35 healthy controls. The cognitive function of the ALS patients was evaluated using the Edinburgh Cognitive and Behavioral ALS Screen. We analyzed these samples by using 16S rRNA gene sequencing as well as both untargeted and targeted (bile acids) metabolite mapping between patients with CI and patients with normal cognition (CN). We found altered gut microbial communities and a lower ratio of Firmicutes/ Bacteroidetes in the CI group, compared with the CN group. In addition, the untargeted metabolite mapping revealed that 26 and 17 metabolites significantly increased and decreased, respectively, in the CI group, compared with the CN group. These metabolites were mapped to the metabolic pathways associated with bile acids. We further found that cholic acid and chenodeoxycholic acid were significantly lower in the CI group than in the CN group. In conclusion, we found that the gut microbiota and its metabolome profile differed between ALS patients with and without CI and that the altered bile acid profile in fecal samples was significantly associated with CI in ALS patients. These results need to be replicated in larger studies in the future.

A shift from inorganic to organic nitrogen-dominance shapes soil microbiome composition and co-occurrence networks

Soil microbiomes are characterized by their composition and networks, which are linked to soil nitrogen (N) availability. In nature, inorganic N dominates at one end and organic N dominates at the other end along soil N gradients; however, little is known about how this shift influences soil microbiome composition and co-occurrence networks, as well as their controls. To this end, we conducted an experiment with the host plant Solidago canadensis, which was subject to three N regimes: inorganic N-dominated, co-dominated by inorganic and organic N (CIO), and organic N-dominated. Organic N dominance exhibited stronger effects on the composition and co-occurrence networks of soil microbiomes than inorganic N dominance. The predominant control was plant traits for bacterial and fungal richness, and soil pH for keystone species. Relative to the CIO regime, inorganic N dominance did not affect fungal richness and increased keystone species; organic N dominance decreased fungal richness and keystone species. Pathogenic fungi and arbuscular mycorrhizal fungi were suppressed by organic N dominance but not by inorganic N dominance. These findings suggest that the shift from soil inorganic N-dominance to soil organic N-dominance could strongly shape soil microbiome composition and co-occurrence networks by altering species diversity and topological properties.

Food origin influences microbiota and stable isotope enrichment profiles of cold-adapted Collembola (Desoria ruseki)

Collembola are a group of globally distributed microarthropods that can tolerate low temperature and are active in extremely cold environments. While it is well known that animal diets can shape their microbiota, the microbiota of soil animals is not well described, particularly for animals with limited food resources, such as Collembola active in winter at low temperatures. In this study, we explored the effects of three different food sources; corn litter (agriculture grain residuals), Mongolian oak litter (natural plant residuals), and yeast (common food for Collembola culture), on the microbiota of a winter-active Collembola species, Desoria ruseki. We found that microbial diversity and community composition of the Collembola were strongly altered after feeding with different food sources for 30 days. Collembola individuals fed on corn litter harbored the highest bacterial richness and were dominated by a representative of Microbacteriaceae. In contrast, those fed on yeast exhibited the lowest bacterial richness and were primarily colonized by Pseudomonas. The microbial communities associated with the winter-active Collembola differed significantly from those observed in the food. Collembola nutrient turnover also differed when cultured with different food sources, as indicated by the C and N stable isotopic signatures. Our study highlights microbial associations with stable isotopic enrichments of the host. Specifically, the Arthrobacter was positively correlated with δ¹³C enrichment in the host. Representatives of Microbacteriaceae, Micrococcaceae, TM7a, Devosia, and Rathayibacter were positively correlated with δ¹⁵N enrichment of the host. Our study indicates that food sources are major determinants for Collembola microbiota that simultaneously alter consumers' isotopic niches, thereby improving our understanding of the roles played by host-microbiota interactions in sustaining soil biodiversity during the winter.

Arbuscular mycorrhizal fungi contribute to wheat yield in an agroforestry system with different tree ages

Intercropping achieved through agroforestry is increasingly being recognized as a sustainable form of land use. In agroforestry, the roots of trees and crops are intermingled, and their interactions and the production of exudates alter the soil environment and soil microbial community. Although tree–crop interactions vary depending on the stand age of the trees, how stand age affects beneficial microorganisms, including arbuscular mycorrhizal fungi (AMF), and whether changes in soil microorganisms feed back on crop growth in agroforestry systems are unknown. We therefore conducted a long-term field study to compare changes in the soil microbial and AMF communities in a jujube/wheat agroforestry system containing trees of different stand ages: 3-year-old jujube, 8-year-old jujube, and 13-year-old jujube. Our results showed that by changing soil moisture and available phosphorus content, the stand age of the trees had a significant effect on the soil microbial and AMF communities. Soil moisture altered the composition of soil bacteria, in particular the proportions of Gram-positive and Gram-negative species, and available phosphorus had significant effects on the AMF community. A network analysis showed that older stands of trees reduced both AMF diversity and network complexity. An ordinary least squares regression analysis indicated that AMF diversity, network complexity, and stability contributed to wheat yield. Finally, structural equation modeling showed that changes in edaphic factors induced by tree age brought about significant variation in the soil microbial and AMF communities, in turn, affecting crop growth. Our study highlights the crucial roles of soil microorganisms, in particular AMF, in supporting plant growth in agroforestry systems as well as the need to consider stand age in the establishment of these systems.

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

Oral-gut bacterial profiles discriminate between periodontal health and diseases

OBJECTIVE

This investigation explored oral-gut microbial signatures with potential to distinguish among periodontal conditions.

BACKGROUND DATA

The interplay between the oral and gut microbiomes may be a critical pathway linking periodontal diseases and systemic inflammatory disorders. The mechanisms by which oral microorganisms translocate to the gut and cause microbial dysbiosis, favoring an inflammatory state, are still unknown. As a first approach, characterization of oral-gut microbial profiles associated with periodontal health and diseases can provide insights on such mechanisms of etiology and pathogenesis.

METHODS

Fecal and saliva samples from individuals with periodontal health (PH, 8), gingivitis (GG, 17), and periodontitis (PD, 24) were analyzed for their microbial composition by 16S rRNA gene sequencing. Microbial taxa were compared and correlated to periodontal parameters. Multivariate discriminant analysis (MDA) was carried out to identify profiles related to health and disease.

RESULTS

Few significant differences in oral-gut taxa were detected among clinical groups, although increase in fecal Fusobacterium nucleatum ss vincentii and salivary Aggregatibacter actinomycetemcomitans, Parvimonas micra, and Fretibacterium sp. HMT358 were strongly correlated with deep pockets and inflammation (p < .01). Over 50% of the fecal microbiota comprised microorganisms shared between oral and gut sites, whereas oral taxa were detected in approximately 9%, particularly enriched in GG fecal samples (p = .04). Trends for lower fecal richness and higher salivary diversity in PD compared to PH were observed. MDA was able to classify correctly 82% of the patients into the clinical groups. Main classifiers of periodontitis were high BMI, older age, and enrichment of oral-fecal Leptotrichia sp. HMT4, Peptostreptococcus stomatis, Dialister invisus, and a novel Lautropia sp. HMTC89-like organism.

CONCLUSION

Within the limitations of an exploratory investigation, specific profiles of oral-gut taxa, including known and potential novel organisms, combined with social-demographic features were able to discriminate individuals with periodontal diseases in this study population.

Wastewater microorganisms impact microbial diversity and important ecological functions of stream periphyton

Effluents of wastewater treatment plants can impact microbial communities in the receiving streams. However, little is known about the role of microorganisms in wastewater as opposed to other wastewater constituents, such as nutrients and micropollutants. We aimed therefore at determining the impact of wastewater microorganisms on the microbial diversity and function of periphyton, key microbial communities in streams. We used a flow-through channel system to grow periphyton upon exposure to a mixture of stream water and unfiltered or ultra-filtered wastewater. Impacts were assessed on periphyton biomass, activities and tolerance to micropollutants, as well as on microbial diversity. Our results showed that wastewater microorganisms colonized periphyton and modified its community composition, resulting for instance in an increased abundance of Chloroflexi and a decreased abundance of diatoms and green algae. This led to shifts towards heterotrophy, as suggested by the changes in nutrient stoichiometry and the increased mineralization potential of carbon substrates. An increased tolerance towards micropollutants was only found for periphyton exposed to unfiltered wastewater but not to ultra-filtered wastewater, suggesting that wastewater microorganisms were responsible for this increased tolerance. Overall, our results highlight the need to consider the role of wastewater microorganisms when studying potential impacts of wastewater on the receiving water body.

Emergent phases of ecological diversity and dynamics mapped in microcosms

From tropical forests to gut microbiomes, ecological communities host notably high numbers of coexisting species. Beyond high biodiversity, communities exhibit a range of complex dynamics that are difficult to explain under a unified framework. Using bacterial microcosms, we performed a direct test of theory predicting that simple community-level features dictate emergent behaviors of communities. As either the number of species or the strength of interactions increases, we show that microbial ecosystems transition between three distinct dynamical phases, from a stable equilibrium in which all species coexist to partial coexistence to emergence of persistent fluctuations in species abundances, in the order predicted by theory. Under fixed conditions, high biodiversity and fluctuations reinforce each other. Our results demonstrate predictable emergent patterns of diversity and dynamics in ecological communities.

What Can We Learn about the Bias of Microbiome Studies from Analyzing Data from Mock Communities?

It is known that data from both 16S and shotgun metagenomics studies are subject to biases that cause the observed relative abundances of taxa to differ from their true values. Model community analyses, in which the relative abundances of all taxa in the sample are known by construction, seem to offer the hope that these biases can be measured. However, it is unclear whether the bias we measure in a mock community analysis is the same as we measure in a sample in which taxa are spiked in at known relative abundance, or if the biases we measure in spike-in samples is the same as the bias we would measure in a real (e.g., biological) sample. Here, we consider these questions in the context of 16S rRNA measurements on three sets of samples: the commercially available Zymo cells model community; the Zymo model community mixed with Swedish Snus, a smokeless tobacco product that is virtually bacteria-free; and a set of commercially available smokeless tobacco products. Each set of samples was subject to four different extraction protocols. The goal of our analysis is to determine whether the patterns of bias observed in each set of samples are the same, i.e., can we learn about the bias in the commercially available smokeless tobacco products by studying the Zymo cells model community?

Unexpected diversity found within benthic microbial mats at hydrothermal springs in Crater Lake, Oregon

Crater Lake, Oregon is an oligotrophic freshwater caldera lake fed by thermally and chemically enriched hydrothermal springs. These vents distinguish Crater Lake from other freshwater systems and provide a unique ecosystem for study. This study examines the community structure of benthic microbial mats occurring with Crater Lake hydrothermal springs. Small subunit rRNA gene amplicon sequencing from eight bacterial mats was used to assess community structure. These revealed a relatively homogeneous, yet diverse bacterial community. High alpha diversity and low beta diversity indicate that these communities are likely fueled by homogeneous hydrothermal fluids. An examination of autotrophic taxa abundance indicates the potential importance of iron and sulfur inputs to the primary productivity of these mats. Chemoautotrophic potential within the mats was dominated by iron oxidation from Gallionella and Mariprofundus and by sulfur oxidation from Sulfuricurvum and Thiobacillus with an additional contribution of nitrite oxidation from Nitrospira. Metagenomic analysis showed that cbbM genes were identified as Gallionella and that aclB genes were identified as Nitrospira, further supporting these taxa as autotrophic drivers of the community. The detection of several taxa containing arsC and nirK genes suggests that arsenic detoxification and denitrification processes are likely co-occurring in addition to at least two modes of carbon fixation. These data link the importance of the detected autotrophic metabolisms driven by fluids derived from benthic hydrothermal springs to Crater Lake’s entire lentic ecosystem.

Compositional and Functional Changes in Microbial Communities of Composts Due to the Composting-Related Factors and the Presence of Listeria monocytogenes

Listeria monocytogenes is a leading foodborne pathogen that can contaminate fresh produce in farm environment, resulting in deadly outbreaks. Composts contain a diversity of microorganisms, and some of them may be compost-adapted competitive exclusion microorganisms against L. monocytogenes. To understand interactions between compost microflora and the pathogen, both dairy- and poultry-wastes based composts (n = 12) were inoculated with L. monocytogenes, and then analyzed by next-generation sequencing approaches along with culturing methods. DNA extraction and enumeration of L. monocytogenes were performed at 0 and 72 h post-incubation at room temperature. The major bacterial phyla were identified as Firmicutes (23%), Proteobacteria (23%), Actinobacteria (19%), Chloroflexi (13%), Bacteroidetes (12%), Gemmatimonadetes (2%), and Acidobacteria (2%). The top three indicator genera enriched in different compost types were identified by LEfSe with LDA score > 2. The interactions between L. monocytogenes and indigenous microflora were limited as no significant changes in the dominant microbial members in compost ecosystem, but some discriminatory species such as Bacillus, Geobacillus, and Brevibacterium were identified by Random Forest analysis. Besides, changes in metabolic pathways and the increased abundance of bacteriocins category in the compost samples containing L. monocytogenes after 72 h postinoculation were revealed by metatranscriptomic sequencing. Taken together, the compost-related factors such as compost types, composting stages, and the collection farms are major drivers that affect compost microbial compositions, and the analysis of compost metagenome implied that interactions between L. monocytogenes and compost microflora may include competition for nutrients and the presence of antimicrobials. IMPORTANCE Listeria monocytogenes has been recognized as the etiological agent causing foodborne disease outbreaks, with fresh produce as vulnerable for contamination at even preharvest stage. Owing to the richness in microbial community, compost may mediate suppression of pathogens. In this study, the impact of compost-related factors and L. monocytogenes intrusion on dynamic changes in compost microbiome was investigated by next generation sequencing techniques. The compost-related factors such as compost types, composting stages, and the collection farms are major drivers that affect compost microbiome. The interactions between L. monocytogenes and compost microflora may include the competition for nutrients and the presence of antimicrobials produced by native compost microorganisms as potential competitive exclusion microorganisms. Findings from this study are important for the composting industry to understand the composition and functionality of microbial community in their products and help developing organic fertilizers fortified with anti-L. monocytogenes competitive exclusion microorganisms.

Systematic profiling of the chicken gut microbiome reveals dietary supplementation with antibiotics alters expression of multiple microbial pathways with minimal impact on community structure

BACKGROUND

The emergence of antimicrobial resistance is a major threat to global health and has placed pressure on the livestock industry to eliminate the use of antibiotic growth promotants (AGPs) as feed additives. To mitigate their removal, efficacious alternatives are required. AGPs are thought to operate through modulating the gut microbiome to limit opportunities for colonization by pathogens, increase nutrient utilization, and reduce inflammation. However, little is known concerning the underlying mechanisms. Previous studies investigating the effects of AGPs on the poultry gut microbiome have largely focused on 16S rDNA surveys based on a single gastrointestinal (GI) site, diet, and/or timepoint, resulting in an inconsistent view of their impact on community composition.

METHODS

In this study, we perform a systematic investigation of both the composition and function of the chicken gut microbiome, in response to AGPs. Birds were raised under two different diets and AGP treatments, and 16S rDNA surveys applied to six GI sites sampled at three key timepoints of the poultry life cycle. Functional investigations were performed through metatranscriptomics analyses and metabolomics.

RESULTS

Our study reveals a more nuanced view of the impact of AGPs, dependent on age of bird, diet, and intestinal site sampled. Although AGPs have a limited impact on taxonomic abundances, they do appear to redefine influential taxa that may promote the exclusion of other taxa. Microbiome expression profiles further reveal a complex landscape in both the expression and taxonomic representation of multiple pathways including cell wall biogenesis, antimicrobial resistance, and several involved in energy, amino acid, and nucleotide metabolism. Many AGP-induced changes in metabolic enzyme expression likely serve to redirect metabolic flux with the potential to regulate bacterial growth or produce metabolites that impact the host.

CONCLUSIONS

As alternative feed additives are developed to mimic the action of AGPs, our study highlights the need to ensure such alternatives result in functional changes that are consistent with site-, age-, and diet-associated taxa. The genes and pathways identified in this study are therefore expected to drive future studies, applying tools such as community-based metabolic modeling, focusing on the mechanistic impact of different dietary regimes on the microbiome. Consequently, the data generated in this study will be crucial for the development of next-generation feed additives targeting gut health and poultry production. Video Abstract.

Organic management enhances soil quality and drives microbial community diversity in cocoa production systems

Maintaining soil quality for agricultural production is a critical challenge, especially in the tropics. Due to the focus on environmental performance and the provision of soil ecosystem services, organic farming and agroforestry systems are proposed as alternative options to conventional monoculture farming. Soil processes underlying ecosystem services are strongly mediated by microbes; thus, increased understanding of the soil microbiome is crucial for the development of sustainable agricultural practices. Therefore, we measured and related soil quality indicators to bacterial and fungal community structures in five cocoa production systems, managed either organically or conventionally for 12 years, with varying crop diversity, from monoculture to agroforestry. In addition, a successional agroforestry system was included, which uses exclusively on-site pruning residues as soil inputs. Organic management increased soil organic carbon, nitrogen and labile carbon contents compared to conventional. Soil basal respiration and nitrogen mineralisation rates were highest in the successional agroforestry system. Across the field sites, fungal richness exceeded bacterial richness and fungal community composition was distinct between organic and conventional management, as well as between agroforestry and monoculture. Bacterial community composition differed mainly between organic and conventional management. Indicator species associated with organic management were taxonomically more diverse compared to taxa associated with conventionally managed systems. In conclusion, our results highlight the importance of organic management for maintaining soil quality in agroforestry systems for cocoa production.

Large-scale microbiome analysis reveals bacterial community characteristics in typical Chinese herbal slices

Chinese herbal slices (CHSs) are closely associated to microorganisms, whether they are endophytic or epiphytic in plants, or introduced during processing. In this study, the structures and predicted functions of microbial communities in 150 batches of samples from five types of CHSs were investigated by combining pure culture and 16 S rDNA amplicon sequencing. Bile-salt-tolerant gram-negative bacteria were detected in 56.0% of samples, and Salmonella was detected in two batches of Glycyrrhiza slices and in one batch of Rheum slices. The main genera from the Enterobacteriaceae, Bacillaceae, Fibrobacteraceae, and Pseudomonadaceae families were assessed in typical colonies. Amplicon sequencing identified 1200 bacterial genera, including some pharmacopeial-controlled bacteria and many beneficial endophytes of medicinal plants. Around 65% of the genera co-occurred in all five CHSs. In clustering based on different algorithms, the samples from each CHS type were relatively clustered, with some overlap. Ranked from highest to lowest diversity, the CHSs were Rheum, Angelica, Astragalus, Codonopsis, and Glycyrrhiza. Each CHS had its indicator species. Functional annotations suggest that potential microbial transformation uses CHSs as substrates and microbial communities as transformants. Overall, it was demonstrated that, based on their complementary advantages, high-throughput sequencing technology and traditional pure-culture technology together can fully assess the microbial load of CHSs and reduce the misdetection rate. We observed large microbial communities in typical CHSs, demonstrating differences and similarities among different CHS types. These results provide a reference for establishing new microbial limit criteria for CHSs and highlight the importance of further correlating CHS microbial community structure and function.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

Diatom eDNA metabarcoding and morphological methods for bioassessment of karstic river

Karst ecosystems play a unique role as exceptional natural habitats in sustaining biodiversity. This study focuses on diatoms, a diverse group of microeukaryotes in the periphytic community of a karstic river. In a multi-microhabitat study along the Krka River (Croatia), our goal was to obtain a detailed overview of diatom diversity and community structure using morphological and molecular approaches, and to assess the applicability of eDNA metabarcoding as a reliable tool for biomonitoring assessment. The results revealed a relatively low agreement in the diatom community composition between the two approaches, but also provided complementary information, with no differences in beta diversity detected between microhabitats. The SIMPER analysis underlined the importance of the molecular approach in identifying diatom community composition, due to errors in distinguishing between deposited diatom cells that occurred in the morphological analysis. In contrast, the morphological approach indicated a clear diatom community separation along the river with a strong location effect. Despite certain differences, both approaches provided a feasible assessment of the ecological status according to the relationship to environmental pressures, classifying the Krka River as High (morphological approach) or Good (molecular approach) throughout the most of its course. Moreover, diatom diversity based on both approaches provides a reliable dataset applicable in routine monitoring assessment and offers a deeper understanding of the presented ecological status. The incompleteness of a reference database presents one major drawback of the molecular approach, which needs further updating in order to improve routine diatom metabarcoding.

Snaq: A Dynamic Snakemake Pipeline for Microbiome Data Analysis With QIIME2

Optimizing and automating a protocol for 16S microbiome data analysis with QIIME2 is a challenging task. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.