Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

A Comprehensive View of Food Microbiota: Introducing FoodMicrobionet v5

Amplicon-targeted metagenomics is now the standard approach for the study of the composition and dynamics of food microbial communities. Hundreds of papers on this subject have been published in scientific journals and the information is dispersed in a variety of sources, while raw sequences and their metadata are available in public repositories for some, but not all, of the published studies. A limited number of web resources and databases allow scientists to access this wealth of information but their level of annotation on studies and samples varies. Here, we report on the release of FoodMicrobionet v5, a comprehensive database of metataxonomic studies on bacterial and fungal communities of foods. The current version of the database includes 251 published studies (11 focusing on fungal microbiota, 230 on bacterial microbiota, and 10 providing data for both bacterial and fungal microbiota) and 14,035 samples with data on bacteria and 1114 samples with data on fungi. The new structure of the database is compatible with interactive apps and scripts developed for previous versions and allows scientists, R&D personnel in industries and regulators to access a wealth of information on food microbial communities.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BACKGROUND

Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.

RESULTS

In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.

CONCLUSION

This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Techniques, procedures, and applications in microbiome analysis

Microbiota is a complex community of microorganisms living in a defined environment. Until the 20th century, knowledge of microbiota was partial, as the techniques available for their characterization were primarily based on bacteriological culture. In the last twenty years, the development of DNA sequencing technologies, multi-omics, and bioinformatics has expanded our understanding of microorganisms. We have moved from mainly considering them isolated disease-causing agents to recognizing the microbiota as an essential component of host biology. These techniques have shown that the microbiome plays essential roles in various host phenotypes, influencing development, physiology, reproduction, and evolution. This chapter provides researchers with a summary of the primary concepts, sample collection, experimental techniques, and bioinformatics analysis commonly used in microbiome research. The main features, applications in microbiome studies, and their advantages and limitations are included in each section.

How reliable is metabarcoding for pollen identification? An evaluation of different taxonomic assignment strategies by cross-validation

Metabarcoding is a powerful tool, increasingly used in many disciplines of environmental sciences. However, to assign a taxon to a DNA sequence, bioinformaticians need to choose between different strategies or parameter values and these choices sometimes seem rather arbitrary. In this work, we present a case study on ITS2 and rbcL databases used to identify pollen collected by bees in Belgium. We blasted a random sample of sequences from the reference database against the remainder of the database using different strategies and compared the known taxonomy with the predicted one. This in silico cross-validation (CV) approach proved to be an easy yet powerful way to (1) assess the relative accuracy of taxonomic predictions, (2) define rules to discard dubious taxonomic assignments and (3) provide a more objective basis to choose the best strategy. We obtained the best results with the best blast hit (best bit score) rather than by selecting the majority taxon from the top 10 hits. The predictions were further improved by favouring the most frequent taxon among those with tied best bit scores. We obtained better results with databases containing the full sequences available on NCBI rather than restricting the sequences to the region amplified by the primers chosen in our study. Leaked CV showed that when the true sequence is present in the database, blast might still struggle to match the right taxon at the species level, particularly with rbcL. Classical 10-fold CV-where the true sequence is removed from the database-offers a different yet more realistic view of the true error rates. Taxonomic predictions with this approach worked well up to the genus level, particularly for ITS2 (5-7% of errors). Using a database containing only the local flora of Belgium did not improve the predictions up to the genus level for local species and made them worse for foreign species. At the species level, using a database containing exclusively local species improved the predictions for local species by ∼12% but the error rate remained rather high: 25% for ITS2 and 42% for rbcL. Foreign species performed worse even when using a world database (59-79% of errors). We used classification trees and GLMs to model the % of errors vs. identity and consensus scores and determine appropriate thresholds below which the taxonomic assignment should be discarded. This resulted in a significant reduction in prediction errors, but at the cost of a much higher proportion of unassigned sequences. Despite this stringent filtering, at least 1/5 sequences deemed suitable for species-level identification ultimately proved to be misidentified. An examination of the variability in prediction accuracy between plant families showed that rbcL outperformed ITS2 for only two of the 27 families examined, and that the % correct species-level assignments were much better for some families (e.g. 95% for Sapindaceae) than for others (e.g. 35% for Salicaceae).

Explaining the optimistic performance evaluation of newly proposed methods: A cross-design validation experiment

The constant development of new data analysis methods in many fields of research is accompanied by an increasing awareness that these new methods often perform better in their introductory paper than in subsequent comparison studies conducted by other researchers. We attempt to explain this discrepancy by conducting a systematic experiment that we call "cross-design validation of methods". In the experiment, we select two methods designed for the same data analysis task, reproduce the results shown in each paper, and then reevaluate each method based on the study design (i.e., datasets, competing methods, and evaluation criteria) that was used to show the abilities of the other method. We conduct the experiment for two data analysis tasks, namely cancer subtyping using multiomic data and differential gene expression analysis. Three of the four methods included in the experiment indeed perform worse when they are evaluated on the new study design, which is mainly caused by the different datasets. Apart from illustrating the many degrees of freedom existing in the assessment of a method and their effect on its performance, our experiment suggests that the performance discrepancies between original and subsequent papers may not only be caused by the nonneutrality of the authors proposing the new method but also by differences regarding the level of expertise and field of application. Authors of new methods should thus focus not only on a transparent and extensive evaluation but also on comprehensive method documentation that enables the correct use of their methods in subsequent studies.

Analysis of the Nasal Microbiota in Healthy and Diseased Pigs

Massive sequencing of a fragment of 16S rRNA gene allows the characterization of bacterial communities in different body sites: the microbiota. Nasal microbiota can be analyzed by DNA extraction from nasal swabs, amplification of the specific fragment of interest, and posterior sequencing. The raw sequences obtained need to go through a computational process to check their quality and then assign the taxonomy. Here, we will describe the complete process from sampling to get the microbial diversity of nasal microbiota in health and disease.

Getting off tract: contributions of intraorgan microbiota to cancer in extraintestinal organs

The gastrointestinal ecosystem has received the most attention when examining the contributions of the human microbiome to health and disease. This concentration of effort is logical due to the overwhelming abundance of microbes in the gut coupled with the relative ease of sampling compared with other organs. However, the intestines are intimately connected to multiple extraintestinal organs, providing an opportunity for homeostatic microbial colonisation and pathogenesis in organs traditionally thought to be sterile or only transiently harbouring microbiota. These habitats are challenging to sample, and their low microbial biomass among large amounts of host tissue can make study challenging. Nevertheless, recent findings have shown that many extraintestinal organs that are intimately linked to the gut harbour stable microbiomes, which are colonised from the gut in selective manners and have highlighted not just the influence of the bacteriome but that of the mycobiome and virome on oncogenesis and health.

MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics

The R-Shiny package MolPad provides an interactive dashboard for understanding the dynamics of longitudinal molecular co-expression in microbiomics. The main idea for addressing the issue is first to use a network to overview major patterns among their predictive relationships and then zoom into specific clusters of interest. It is designed with a focus-plus-context analysis strategy and automatically generates links to online curated annotations. The dashboard consists of a cluster-level network, a bar plot of taxonomic composition, a line plot of data modalities, and a table for each pathway. Further, the package includes functions that handle the data processing for creating the dashboard. This makes it beginner-friendly for users with less R programming experience. We illustrate these methods with a case study on a longitudinal metagenomics analysis of the cheese microbiome.

Exercise and the gut microbiome: implications for supportive care in cancer

PURPOSE

Growing recognition of the gut microbiome as an influential modulator of cancer treatment efficacy and toxicity has led to the emergence of clinical interventions targeting the microbiome to enhance cancer and health outcomes. The highly modifiable nature of microbiota to endogenous, exogenous, and environmental inputs enables interventions to promote resilience of the gut microbiome that have rapid effects on host health, or response to cancer treatment. While diet, probiotics, and faecal microbiota transplant are primary avenues of therapy focused on restoring or protecting gut function in people undergoing cancer treatment, the role of physical activity and exercise has scarcely been examined in this population.

METHODS

A narrative review was conducted to explore the nexus between cancer care and the gut microbiome in the context of physical activity and exercise as a widely available and clinically effective supportive care strategy used by cancer survivors.

RESULTS

Exercise can facilitate a more diverse gut microbiome and functional metabolome in humans; however, most physical activity and exercise studies have been conducted in healthy or athletic populations, primarily using aerobic exercise modalities. A scarcity of exercise and microbiome studies in cancer exists.

CONCLUSIONS

Exercise remains an attractive avenue to promote microbiome health in cancer survivors. Future research should elucidate the various influences of exercise modalities, intensities, frequencies, durations, and volumes to explore dose-response relationships between exercise and the gut microbiome among cancer survivors, as well as multifaceted approaches (such as diet and probiotics), and examine the influences of exercise on the gut microbiome and associated symptom burden prior to, during, and following cancer treatment.

Edible flora in pre-Columbian Caribbean coprolites: Expected and unexpected data

Coprolites, or mummified feces, are valuable sources of information on ancient cultures as they contain ancient DNA (aDNA). In this study, we analyzed ancient plant DNA isolated from coprolites belonging to two pre-Columbian cultures (Huecoid and Saladoid) from Vieques, Puerto Rico, using shotgun metagenomic sequencing to reconstruct diet and lifestyles. We also analyzed DNA sequences of putative phytopathogenic fungi, likely ingested during food consumption, to further support dietary habits. Our findings show that pre-Columbian Caribbean cultures had a diverse diet consisting of maize (Zea mays), sweet potato (Ipomoea batatas), chili peppers (Capsicum annuum), peanuts (Arachis spp.), papaya (Carica papaya), tomato (Solanum lycopersicum) and, very surprisingly cotton (Gossypium barbadense) and tobacco (Nicotiana sylvestris). Modelling of putative phytopathogenic fungi and plant interactions confirmed the potential consumption of these plants as well as edible fungi, particularly Ustilago spp., which suggest the consumption of maize and huitlacoche. These findings suggest that a variety of dietary, medicinal, and hallucinogenic plants likely played an important role in ancient human subsistence and societal customs. We compared our results with coprolites found in Mexico and the United States, as well as present-day faeces from Mexico, Peru, and the United States. The results suggest that the diet of pre-Columbian cultures resembled that of present-day hunter-gatherers, while agriculturalists exhibited a transitional state in dietary lifestyles between the pre-Columbian cultures and larger scale farmers and United States individuals. Our study highlights differences in dietary patterns related to human lifestyles and provides insight into the flora present in the pre-Columbian Caribbean area. Importantly, data from ancient fecal specimens demonstrate the importance of ancient DNA studies to better understand pre-Columbian populations.

Differences in Microbial Community Composition between Uterine Horns Ipsilateral and Contralateral to the Corpus Luteum in Beef Cows on Day 15 of the Estrous Cycle

This study evaluated differences in uterine microbiota composition between uterine horns ipsilateral and contralateral to the corpus luteum of beef cows on day 15 of the estrous cycle. Cows (n = 23) were exposed to an estrus synchronization protocol to exogenously induce synchronized ovulation. Cows were then euthanized on day 15 of the estrous cycle, and individual swabs were collected from uterine horns ipsilateral and contralateral to the corpus luteum using aseptic techniques. DNA was extracted, and the entire (V1-V9 hypervariable regions) 16s rRNA gene was sequenced. Sequences were analyzed, and amplicon sequence variants (ASVs) were determined. Across all samples, 2 bacterial domains, 24 phyla, and 265 genera were identified. Butyribirio, Cutibacterium, BD7-11, Bacteroidales BS11 gut group, Ruminococcus, Bacteroidales RF16 group, and Clostridia UCG-014 differed in relative abundances between uterine horns. Rikenellaceae RC9 gut group, Bacteroidales UCG-001, Lachnospiraceae AC2044 group, Burkholderia-Caballeronia-Paraburkholderia, Psudobutyribibrio, and an unidentified genus of the family Chitinophagaceae and dgA-11 gut group differed between cows that expressed estrus and those that did not. The composition of the microbial community differed between the ipsilateral and contralateral horns and between cows that expressed estrus and cows that failed to express estrus, indicating that the uterine microbiota might play a role in cow fertility.

A tool to assess the mock community samples in 16S rRNA gene-based microbiota profiling studies

Inclusion and investigation of technical controls in microbiome sequencing studies is important for understanding technical biases and errors. Here, we present chkMocks, a general R-based tool that allows researchers to compare the composition of mock communities that are processed along with samples to their theoretical composition. A visual comparison between experimental and theoretical community composition and their correlation is provided for researchers to assess the quality of their sample processing workflows.

Metataxonomic insights in the distribution of Lactobacillaceae in foods and food environments

Members of the family Lactobacillaceae, which now includes species formerly belonging to the genera Lactobacillus and Pediococcus, but also Leuconostocaceae, are of foremost importance in food fermentations and spoilage, but also as components of animal and human microbiota and as potentially pathogenic microorganisms. Knowledge of the ecological distribution of a given species and genus is important, among other things, for the inclusion in lists of microorganisms with a Qualified Presumption of Safety or with beneficial use. The objective of this work is to use the data in FoodMicrobionet database to obtain quantitative insights (in terms of both abundance and prevalence) on the distribution of these bacteria in foods and food environments. We first explored the reliability of taxonomic assignments using the SILVA v138.1 reference database with full length and partial sequences of the 16S rRNA gene for type strain sequences. Full length 16S rRNA gene sequences allow a reasonably good classification at the genus and species level in phylogenetic trees but shorter sequences (V1-V3, V3-V4, V4) perform much worse, with type strains of many species sharing identical V4 and V3-V4 sequences. Taxonomic assignment at the genus level of 16S rRNA genes sequences and the SILVA v138.1 reference database can be done for almost all genera of the family Lactobacillaceae with a high degree of confidence for full length sequences, and with a satisfactory level of accuracy for the V1-V3 regions. Results for the V3-V4 and V4 region are still acceptable but significantly worse. Taxonomic assignment at the species level for sequences for the V1-V3, V3-V4, V4 regions of the 16S rRNA gene of members of the family Lactobacillaceae is hardly possible and, even for full length sequences, and only 49.9 % of the type strain sequences can be unambiguously assigned to species. We then used the FoodMicrobionet database to evaluate the prevalence and abundance of Lactobacillaceae in food samples and in food related environments. Generalist and specialist genera were clearly evident. The ecological distribution of several genera was confirmed and insights on the distribution and potential origin of rare genera (Dellaglioa, Holzapfelia, Schleiferilactobacillus) were obtained. We also found that combining Amplicon Sequence Variants from different studies is indeed possible, but provides little additional information, even when strict criteria are used for the filtering of sequences.

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

Nanotechnological interventions of the microbiome as a next-generation antimicrobial therapy

The rise of antimicrobial resistance (AMR) impacts public health due to the diminished potency of existing antibiotics. The microbiome plays an important role in the host's immune system activity and shows the history of exposure to antimicrobials and its manipulation in combating antimicrobial resistance. Advancements in gene technologies, DNA sequencing, and computational biology have emerged as powerful platforms to better understand the relationship between animals and microorganisms (MOs). The past few years have witnessed an increase in the use of nanotechnology, both in industry and in academia, as tools to tackle antimicrobial resistance. New strategies of microbiome manipulation have been developed, such as the use of prebiotics, probiotics, peptides, antibodies, an appropriate diet, phage therapy, and the use of various nanotechnological techniques. Owing to the research outcomes, targeted delivery of antimicrobials with some modifications with nanoparticles can lead to the destruction of resistant microbial cells. In addition, nanoparticles have been studied for their potential antimicrobial effects both in vitro and in vivo. In this review, we highlight key opportunistic areas for applying nanotechnologies with the aim of manipulating the microbiome for the treatment of antimicrobial resistance. Besides providing a detailed review on various nanomaterials, technologies, opportunities, technical needs, and potential approaches for the manipulation of the microbiome to address these challenges, we discuss future challenges and our perspective.

SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

Large amounts of data from microbiome-related studies have been (and are currently being) deposited on international public databases. These datasets represent a valuable resource for the microbiome research community and could serve future researchers interested in integrating multiple datasets into powerful meta-analyses. However, this huge amount of data lacks harmonization and it is far from being completely exploited in its full potential to build a foundation that places microbiome research at the nexus of many subdisciplines within and beyond biology. Thus, it urges the need for data accessibility and reusability, according to findable, accessible, interoperable and reusable (FAIR) principles, as supported by National Microbiome Data Collaborative and FAIR Microbiome. To tackle the challenge of accelerating discovery and advances in skin microbiome research, we collected, integrated and organized existing microbiome data resources from human skin 16S rRNA amplicon-sequencing experiments. We generated a comprehensive collection of datasets, enriched in metadata, and organized this information into data frames ready to be integrated into microbiome research projects and advanced post-processing analyses, such as data science applications (e.g. machine learning). Furthermore, we have created a data retrieval and curation framework built on three different stages to maximize the retrieval of datasets and metadata associated with them. Lastly, we highlighted some caveats regarding metadata retrieval and suggested ways to improve future metadata submissions. Overall, our work resulted in a curated skin microbiome datasets collection accompanied by a state-of-the-art analysis of the last 10 years of the skin microbiome field. Database URL:  https://github.com/giuliaago/SKIOMEMetadataRetrieval.

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture

BACKGROUND

The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities.

RESULTS

By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production.

CONCLUSIONS

Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

Elevated functional versatility of the soil microbial community associated with the invader Carpobrotus edulis across a broad geographical scale

Exotic invasive plants may shape their own rhizosphere microbial community during global invasions. Nevertheless, the impacts of such plant invasions on the functional capacities of soil microbial communities remain poorly explored. We used an approach at a broad geographical scale to estimate the composition and abundance of the fungal functional groups, as well as the bacterial metabolic functions, associated with the rhizospheres of Carpobrotus edulis (L.) L. Bolus and the predominant native plants in coastal ecosystems located in different geographical regions. We used the ASV method to infer the potential functions of the soil microbial community with the PICRUSt2 and FUNGuild tools. The predictive functional profiling of the bacterial communities differed between the rhizospheres of the invasive and native plants, regardless of the biogeographic location of the invaded soil. Some predicted pathways related to the biosynthesis of nucleotides such as ppGpp and pppGpp, lipids, carbohydrates and secondary metabolites and the degradation of organic matter were enriched in the C. edulis rhizosphere. Moreover, the invasive microbiota was characterised by a greater richness and diversity of catabolic enzymes involved in nutrients cycling and higher relative abundances of saprotrophs and pathotrophs. Invasion by C. edulis promoted a shift in the potential functional versatility of the soil microbial communities, which can cope with nutrient limitations and biotic stress, and can favour the establishment of the invasive plant, but also alter the functioning and stability of the invaded ecosystems.

Comparison of the Effects of Microbial Inoculants on Fermentation Quality and Microbiota in Napier Grass (Pennisetum purpureum) and Corn (Zea mays L.) Silage

Forage preservation for livestock feeding is usually done by drying the plant material and storing it as hay or ensiling it into silage. During the ensiling process, the pH in the system is lowered by the activities of lactic acid-producing bacteria (LAB), inhibiting the growth of spoilage microorganisms and maintaining the quality of the ensiled product. To improve this process, inoculation of LAB could be used as starter cultures to shorten the ensiling time and control the fermentation process. Here, we compared fermentation quality and bacterial dynamics in two plant materials, whole-plant corn (Zea mays L.) and Napier grass (Pennisetum purpureum), with and without starter inoculation. The efficacy of Lactobacillus plantarum, L. brevis, and Pediococcus pentosaceus as starter cultures were also compared in the ensiling system. In whole-plant corn, pH decreased significantly, while lactic acid content increased significantly on Day 3 in both the non-inoculated and LAB-inoculated groups. Prior to ensiling, the predominant LAB bacteria were Weissella, Enterococcus, and Lactococcus, which shifted to Lactobacillus during ensiling of whole-plant corn in both the non-inoculated and LAB inoculated groups. Interestingly, the epiphytic LAB associated with Napier grass were much lower than those of whole-plant corn before ensiling. Consequently, the fermentation quality of Napier grass was improved by the addition of LAB inoculants, especially L. plantarum and a combination of all three selected LAB strains showed better fermentation quality than the non-inoculated control. Therefore, the different abundance and diversity of epiphytic LAB in plant raw materials could be one of the most important factors determining whether LAB starter cultures would be necessary for silage fermentation.

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

RESCRIPt: Reproducible sequence taxonomy reference database management

Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt.

Generating and managing sequence and taxonomy reference data presents a bottleneck to many researchers, whether they are generating custom databases or attempting to format existing, curated reference databases for use with standard sequence analysis tools. Evaluating database quality and choosing the “best” database can be an equally formidable challenge. We developed RESCRIPt to alleviate this bottleneck, supporting reproducible, streamlined generation, curation, and evaluation of reference sequence databases. RESCRIPt uses QIIME 2 artifact file formats, which store all processing steps as data provenance within each file, allowing researchers to retrace the computational steps used to generate any given file. We used RESCRIPt to benchmark several commonly used marker-gene sequence databases for 16S rRNA genes, ITS, and COI sequences, demonstrating both the utility of RESCRIPt to streamline use of these databases, but also to evaluate several qualitative and quantitative characteristics of each database. We show that larger databases are not always best, and curation steps to reduce redundancy and filter out noisy sequences may be beneficial for some applications. We anticipate that RESCRIPt will streamline the use, management, and evaluation/selection of reference database materials for microbiomics, diet metabarcoding, eDNA, and other diverse applications.

Polystyrene microplastics induce microbial dysbiosis and dysfunction in surrounding seawater

Microplastics are ubiquitously present in the environment, accumulate in aquaculture water, and cause toxicological effects on aquatic organisms. Besides, microplastics provide ecological niches for microorganisms in aquatic environments. However, the effects of microplastics on microbial balance and function in surrounding water are still unclear, especially for aquaculture water. Therefore, 16S rRNA gene sequencing was employed to uncover polystyrene microplastics (PS)-induced microbial dysbiosis in surrounding seawater cultivating marine medaka (Oryzias melastigmas) and to screen related potential bacterial biomarkers. We found that Proteobacteria and Bacteroidetes were the dominant phyla in each group, accounting for more than 95% of the total abundance, and that 26 bacterial taxa belonging to Proteobacteria and Bacteroidetes were significantly altered in surrounding seawater after 10- and 200-µm PS exposure. Functional analysis revelated that photosynthesis, carbon metabolism (such as carbon fixation, glycolysis, tricarboxylic acid cycle, and glycan biosynthesis and metabolism), amino acid metabolism, lipid synthesis, and nucleotide metabolism were decreased, while environmental stress responses, such as xenobiotics biodegradation and metabolism, glutathione metabolism, and taurine and hypotaurine metabolism, were increased in surrounding seawater microbiota after separate 10- and 200-µm PS exposure. Pathway analysis and correlation networks demonstrated that changes in relative abundances of bacterial taxa belonging to Proteobacteria and Bacteroidetes were highly correlated with those in the liver metabolism of marine medaka. Subsequently, 8 bacterial taxa were discovered to be able to be used separately as the potential biomarker for assessing the surrounding seawater microbial dysbiosis and metabolic responses of marine medaka, with a diagnostic accuracy of 100.0%. This study provides novel insights into toxicological effects of microplastics on microbial dysbiosis and function in surrounding water and ecosystems, and suggests potential roles of biomarkers involved in surrounding microbial dysbiosis in assessing microplastic ecotoxicology, microbial dysbiosis, and the health status of organisms at higher trophic levels.

Contribution of sample processing to gut microbiome analysis in the model Lepidoptera, silkworm Bombyx mori

Microbes that live inside insects play various roles in host biology, ranging from nutrient supplementation to host defense. Although Lepidoptera (butterflies and moths) are one of the most diverse insect taxa and important in natural ecosystems, their microbiotas are little-studied, and to understand their structure and function, it is necessary to identify potential factors that affect microbiome analysis. Using a model organism, the silkworm Bombyx mori, we investigated the effects of different sample types (whole gut, gut content, gut tissue, starvation, or frass) and metagenomic DNA extraction methodologies (small-scale versus large-scale) on the composition and diversity of the caterpillar gut microbial communities. High-throughput 16S rRNA gene sequencing and computational analysis of the resulting data unraveled that DNA extraction has a large effect on the outcome of metagenomic analysis: significant biases were observed in estimates of community diversity and in the ratio between Gram-positive and Gram-negative bacteria. Furthermore, bacterial communities differed significantly among sample types. The gut content and whole gut samples differed least, both had a higher percentage of Enterococcus and Acinetobacter species; whereas the frass and starvation samples differed substantially from the whole gut and were poor representatives of the gut microbiome. Thus, we recommend a small-scale DNA extraction methodology for sampling the whole gut under normal insect rearing conditions whenever possible, as this approach provides the most accurate assessment of the gut microbiome. Our study highlights that evaluation of the optimal sample-processing approach should be the first step taken to confidently assess the contributions of microbiota to Lepidoptera.

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.

Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences

Naive Bayes classifiers (NBC) have dominated the field of taxonomic classification of amplicon sequences for over a decade. Apart from having runtime requirements that allow them to be trained and used on modest laptops, they have persistently provided class-topping classification accuracy. In this work we compare NBC with random forest classifiers, neural network classifiers, and a perfect classifier that can only fail when different species have identical sequences, and find that in some practical scenarios there is little scope for improving on NBC for taxonomic classification of 16S rRNA gene sequences. Further improvements in taxonomy classification are unlikely to come from novel algorithms alone, and will need to leverage other technological innovations, such as ecological frequency information.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

Gut Microbiome and Metabolites in Patients with NAFLD and after Bariatric Surgery: A Comprehensive Review

The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing, as are other manifestations of metabolic syndrome such as obesity and type 2 diabetes. NAFLD is currently the number one cause of chronic liver disease worldwide. The pathophysiology of NAFLD and disease progression is poorly understood. A potential contributing role for gut microbiome and metabolites in NAFLD is proposed. Currently, bariatric surgery is an effective therapy to prevent the progression of NAFLD and other manifestations of metabolic syndrome such as obesity and type 2 diabetes. This review provides an overview of gut microbiome composition and related metabolites in individuals with NAFLD and after bariatric surgery. Causality remains to be proven. Furthermore, the clinical effects of bariatric surgery on NAFLD are illustrated. Whether the gut microbiome and metabolites contribute to the metabolic improvement and improvement of NAFLD seen after bariatric surgery has not yet been proven. Future microbiome and metabolome research is necessary for elucidating the pathophysiology and underlying metabolic pathways and phenotypes and providing better methods for diagnostics, prognostics and surveillance to optimize clinical care.