The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies

Absolute quantification of prokaryotes in the microbiome by 16S rRNA qPCR or ddPCR

Measurements of prokaryotic absolute abundance can provide important insights into human gut microbiome biology and correct misinterpretations of relative abundance data. Despite the existence of several relatively well-established methods for making these measurements, most microbiome studies do not report absolute abundance. To enable researchers equipped with standard molecular biology capabilities to incorporate absolute quantification into their microbiome studies, we present a detailed, step-by-step protocol for rigorous and reproducible quantification of prokaryotic concentration in stool samples. We include methods for measuring stool sample moisture content, quantifying the concentration of the 16S rRNA prokaryotic marker gene by qPCR or digital droplet PCR (ddPCR) and analyzing the resulting data. We also highlight and provide strategies to overcome common pitfalls of the quantification method, such as 16S rRNA gene contamination. The final output of this approach is 16S rRNA copies per wet or dry gram of stool. In cases where samples have matched metagenomic sequencing information, data can be converted into absolute concentration of prokaryotes and taxon-specific absolute concentrations. To enable researchers to choose the appropriate method for their specific applications, we also compare and contrast our qPCR and ddPCR methods. In 4 days, ~80 samples can be taken from frozen stool to absolute concentration by using qPCR or ddPCR without the need for resequencing. Overall, this protocol provides a sensitive and straightforward way to measure the absolute concentration of prokaryotes in human gut microbiome samples stored with or without preservative.

Pseudomonas in the spotlight: emerging roles in the nodule microbiome

While rhizobia have long been recognised as the primary colonisers of legume nodules, microbiome studies have revealed the presence of other bacteria in these organs. This opinion delves into the factors shaping the nodule microbiome and explores the potential roles of non-rhizobial endophytes, focusing particularly on Pseudomonas as prominent players. We explore the mechanisms by which Pseudomonas colonise nodules, their interactions with rhizobia, and their remarkable potential to promote plant growth and protect against pathogens. Furthermore, we discuss the promising prospects of using Pseudomonas as inoculants alongside rhizobia to enhance crop growth and promote sustainable agricultural practices.

From air to insight: the evolution of airborne DNA sequencing technologies

Historically, the analysis of airborne biological organisms relied on microscopy and culture-based techniques. However, technological advances such as PCR and next-generation sequencing now provide researchers with the ability to gather vast amounts of data on airborne environmental DNA (eDNA). Studies typically involve capturing airborne biological material, followed by nucleic acid extraction, library preparation, sequencing and taxonomic identification to characterize the eDNA at a given location. These methods have diverse applications, including pathogen detection in agriculture and human health, air quality monitoring, bioterrorism detection and biodiversity monitoring. A variety of methods are used for airborne eDNA analysis, as no single pipeline meets all needs. This review outlines current methods for sampling, extraction, sequencing and bioinformatic analysis, highlighting how different approaches can influence the resulting data and their suitability for specific use cases. It also explores current applications of airborne eDNA sampling and identifies research gaps in the field.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe, for example, different DNA extraction efficiency for Gram-positive bacteria. These processing biases impede the identification of signals that are biologically interpretable and generalizable across studies. 'Batch-correction' methods have been used to address these issues computationally with some success, but they are largely non-interpretable and often require the use of an outcome variable in a manner that risks overfitting. We present DEBIAS-M (domain adaptation with phenotype estimation and batch integration across studies of the microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using diverse benchmarks including 16S rRNA and metagenomic sequencing, classification and regression, and a variety of clinical and molecular targets, we demonstrate that using DEBIAS-M improves cross-study prediction accuracy compared with commonly used batch-correction methods. Notably, we show that the inferred bias-correction factors are stable, interpretable and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M facilitates improved modelling of microbiome data and identification of interpretable signals that generalize across studies.

Profiling bacterial communities in feedlot cattle affected with bovine foot rot and bovine digital dermatitis lesions using 16S rRNA gene sequencing and quantitative real-time PCR

BACKGROUND

The primary infectious foot diseases in cattle, bovine foot rot (BFR) and bovine digital dermatitis (BDD), commonly associated with Fusobacterium necrophorum and Treponema spp., respectively, are considered polybacterial in etiology with several additional bacteria involved such as Porphyromonas levii, Bacteroides pyogenes, and Fusobacterium mortiferum. BDD is further classified into several M-stages (M2: active and ulcerative; M4: chronic proliferative). Using quantitative real-time PCR and 16S rRNA gene (V3-V4 region) sequencing, we quantified several specific bacteria and analyzed bacterial communities present in biopsies of visually diagnosed cases of BFR (n = 32), M2 (n = 17), and M4 (n = 12) stages of BDD in feedlot cattle in contrast to inconclusive (n = 14) clinical cases and healthy (n = 25) cattle.

RESULTS

Bacterial composition of healthy skin differed significantly from that of skin lesions, and between BFR and both lesion stages of BDD, which also differed from each other. All animal groups had generally the same bacterial species, albeit in distinct ratios. Differential abundance analysis relative to the healthy group identified a higher abundance of Fusobacterium spp. in BFR and Treponema spp. in both BDD-M2 and BDD-M4. P. levii had the highest absolute abundance in all animal groups. A significantly higher abundance of F. necrophorum was observed in BFR compared to BDD-M2, and F. mortiferum in both stages of BDD compared to the inconclusive group. Both BDD M-stages had a significantly higher abundance of Treponema phagedenis and Treponema pedis. Treponema medium was significantly more abundant in BDD-M4 compared to BDD-M2.

CONCLUSION

These results further the evidence of the involvement of Treponema spp., in BDD in feedlot cattle. However, it suggests further exploration of the role of Fusobacterium spp. in BFR and BDD. Importantly, a discriminating polybacterial involvement in these infections was evident demonstrated by changes in the population of multiple bacteria when compared to healthy animals.

Using short-read 16S rRNA sequencing of multiple variable regions to generate high-quality results to a species level

Introduction

Short-read amplicon sequencing studies have typically focused on 1-2 variable regions of the 16S rRNA gene. Species-level resolution is limited in these studies, as each variable region enables the characterisation of a different subsection of the microbiome. Although long-read sequencing techniques can take advantage of all 9 variable regions by sequencing the entire 16S rRNA gene, short-read sequencing has remained a commonly used approach in 16S rRNA research. This work assessed the feasibility of accurate species-level resolution and reproducibility using a relatively new sequencing kit and bioinformatics pipeline developed for short-read sequencing of multiple variable regions of the 16S rRNA gene. In addition, we evaluated the potential impact of different sample collection methods on our outcomes.

Methods

Using xGen™ 16S Amplicon Panel v2 kits, sequencing of all 9 variable regions of the 16S rRNA gene was carried out on an Illumina MiSeq platform. Mock cells and mock DNA for 8 bacterial species were included as extraction and sequencing controls respectively. Within-run and between-run replicate samples, and pairs of stool and rectal swabs collected at 0-5 weeks from the same infants, were incorporated. Observed relative abundances of each species were compared to theoretical abundances provided by ZymoBIOMICS. Paired Wilcoxon rank sum tests and distance-based intraclass correlation coefficients were used to statistically compare alpha and beta diversity measures, respectively, for pairs of replicates and stool/rectal swab sample pairs.

Results

Using multiple variable regions of the 16S ribosomal Ribonucleic Acid (rRNA) gene, we found that we could accurately identify taxa to a species level and obtain highly reproducible results at a species level. Yet, the microbial profiles of stool and rectal swab sample pairs differed substantially despite being collected concurrently from the same infants.

Conclusion

This protocol provides an effective means for studying infant gut microbial samples at a species level. However, sample collection approaches need to be accounted for in any downstream analysis.

DeepMAP: Deep CNN Classifiers Applied to Optical Mapping for Fast and Precise Species-Level Metagenomic Analysis

DNA optical mapping is a powerful technique commonly used for structural variant calling and genome assembly verification. Despite being inherently high-throughput, the method has not yet been applied to highly complex settings such as species identification in microbiome analysis due to the lack of alignment algorithms that can both assign large numbers of reads in minutes and handle large database size. In this work, we present a novel genomic classification pipeline based on deep convolutional neural networks for optical mapping data (DeepMAP), which can perform fast and accurate assignment of individual optical maps to their respective genomes. We furthermore achieve a superior performance of DeepMAP in the presence of evolutionary divergent sequences, making it robust to the presence of unknown strains within metagenomic samples. We evaluate DeepMAP on genomic DNA extracted from bacterial mixtures, reaching species-level resolution with true positive rates of around 75% and a false positive rate of less than 1%, with measured classification speeds significantly outpacing those of previously developed approaches for high-density optical mapping data alignment.

De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation

INTRODUCTION

Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties.

METHODS

We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks.

RESULTS

Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions.

CONCLUSIONS

Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis. Video Abstract.

The quest for environmental analytical microbiology: absolute quantitative microbiome using cellular internal standards

BACKGROUND

High-throughput sequencing has revolutionized environmental microbiome research, providing both quantitative and qualitative insights into nucleic acid targets in the environment. The resulting microbial composition (community structure) data are essential for environmental analytical microbiology, enabling characterization of community dynamics and assessing microbial pollutants for the development of intervention strategies. However, the relative abundances derived from sequencing impede comparisons across samples and studies.

RESULTS

This review systematically summarizes various absolute quantification (AQ) methods and their applications to obtain the absolute abundance of microbial cells and genetic elements. By critically comparing the strengths and limitations of AQ methods, we advocate the use of cellular internal standard-based high-throughput sequencing as an appropriate AQ approach for studying environmental microbiome originated from samples of complex matrices and high heterogeneity. To minimize ambiguity and facilitate cross-study comparisons, we outline essential reporting elements for technical considerations, and provide a checklist as a reference for environmental microbiome research.

CONCLUSIONS

In summary, we propose absolute microbiome quantification using cellular internal standards for environmental analytical microbiology, and we anticipate that this approach will greatly benefit future studies. Video Abstract.

Stepping towards pollen DNA metabarcoding: A breakthrough in forensic sciences

This review is engaged in determining the capability of plant pollen as a significant source of evidence for the linkage between suspects and crime location in forensic sciences. Research and review articles were collected from Google Scholar, the Web of Science, and PubMed. Articles were searched using specific keywords such as "Forensic Palynology," "Pollen metabarcoding," "Plant forensics," and "Pollen" AND "criminal investigation." Boolean logic was also utilized to narrow the articles to be included in this review article. Through the literature and exploratory research, it has been observed in the current study that with advancements in technology, forensic palynology has found its application in creating an association between the crime scene and suspected individuals to have a link to it, as pollen DNA is a long-lasting investigative tool that can effectively help forensic investigations. Moreover, the literature shows that the DNA of pollen and spores has helped forensic scientists link suspects to crime scenes, and the introduction of pollen DNA metabarcoding tools has eased the efforts of palynologists to analyze pollen DNA. The introduction of DNA metabarcoding techniques to analyze pollen from plants has helped identify the geological locations of the plants and ultimately identify the culprit.

A Mixed Effect Similarity Matrix Regression Model (SMRmix) for Integrating Multiple Microbiome Datasets at Community Level

BACKGROUND

Recent studies have highlighted the importance of human microbiota in our health and diseases. However, in many areas of research, individual microbiome studies often offer inconsistent results due to the limited sample sizes and the heterogeneity in study populations and experimental procedures. This inconsistency underscores the necessity for integrative analysis of multiple microbiome datasets. Despite the critical need, statistical methods that incorporate multiple microbiome datasets and account for the study heterogeneity are not available in the literature.

METHODS

In this paper, we develop a mixed effect similarity matrix regression (SMRmix) approach for identifying community level microbiome shifts between outcomes. SMRmix has a close connection with the microbiome kernel association test, one of the most popular approaches for such a task but is only applicable when we have a single study. SMRmix enables researchers to consolidate findings from diverse microbiome studies.

RESULTS

Via extensive simulations, we show that SMRmix has well-controlled type I error and higher power than some potential competitors. We applied the SMRmix to two real-world datasets. The first, from the HIV-reanalysis consortium, integrated data from 17 studies on gut dysbiosis in HIV. Our analysis confirmed consistent associations between the gut microbiome and HIV infection as well as MSM (men who have sex with men) status, demonstrating greater power than competing methods. The second dataset involved 11 studies on the gut microbiome in colorectal cancer; analysis with SMRmix confirmed significant dysbiosis in affected individuals compared to healthy controls.

CONCLUSION

The development of SMRmix enables the integration of multiple studies and effectively managing study heterogeneity, and provides a powerful tool for uncovering consistent associations between diseases and community-level microbiome data.

Evaluation of DNA extraction kits for long-read shotgun metagenomics using Oxford Nanopore sequencing for rapid taxonomic and antimicrobial resistance detection

During a bacterial infection or colonization, the detection of antimicrobial resistance (AMR) is critical, but slow due to culture-based approaches for clinical and screening samples. Culture-based phenotypic AMR detection and confirmation require up to 72 hours (h) or even weeks for slow-growing bacteria. Direct shotgun metagenomics by long-read sequencing using Oxford Nanopore Technologies (ONT) may reduce the time for bacterial species and AMR gene identification. However, screening swabs for metagenomics is complex due to the range of Gram-negative and -positive bacteria, diverse AMR genes, and host DNA present in the samples. Therefore, DNA extraction is a critical initial step. We aimed to compare the performance of different DNA extraction protocols for ONT applications to reliably identify species and AMR genes using a shotgun long-read metagenomic approach. We included three different sample types: ZymoBIOMICS Microbial Community Standard, an in-house mock community of ESKAPE pathogens including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE Mock), and anonymized clinical swab samples. We processed all sample types with four different DNA extraction kits utilizing different lysis (enzymatic vs. mechanical) and purification (spin-column vs. magnetic beads) methods. We used kits from Qiagen (QIAamp DNA Mini and QIAamp PowerFecal Pro DNA) and Promega (Maxwell RSC Cultured Cells and Maxwell RSC Buccal Swab DNA). After extraction, samples were subject to the Rapid Barcoding Kit (RBK004) for library preparation followed by sequencing on the GridION with R9.4.1 flow cells. The fast5 files were base called to fastq files using Guppy in High Accuracy (HAC) mode with the inbuilt MinKNOW software. Raw read quality was assessed using NanoPlot and human reads were removed using Minimap2 alignment against the Hg38 genome. Taxonomy identification was performed on the raw reads using Kraken2 and on assembled contigs using Minimap2. The AMR genes were identified using Minimap2 with alignment against the CARD database on both the raw reads and assembled contigs. We identified all bacterial species present in the Zymo Mock Community (8/8) and ESKAPE Mock (6/6) with Qiagen PowerFecal Pro DNA kit (chemical and mechanical lysis) at read and assembly levels. Enzymatic lysis retrieved fewer aligned bases for the Gram-positive species (Staphylococcus aureus and Enterococcus faecium) from the ESKAPE Mock on the assembly level compared to the mechanical lysis. We detected the AMR genes from Gram-negative and -positive species in the ESKAPE Mock with the QIAamp PowerFecal Pro DNA kit on reads level with a maximum median time of 1.9 h of sequencing. Long-read metagenomics with ONT may reduce the turnaround time in screening for AMR genes. Currently, the QIAamp PowerFecal Pro DNA kit (chemical and mechanical lysis) for DNA extraction along with the Rapid Barcoding Kit for the ONT sequencing captured the best taxonomy and AMR identification for our specific use case.

Development of a clinical metagenomics workflow for the diagnosis of wound infections

BACKGROUND

Wound infections are a common complication of injuries negatively impacting the patient's recovery, causing tissue damage, delaying wound healing, and possibly leading to the spread of the infection beyond the wound site. The current gold-standard diagnostic methods based on microbiological testing are not optimal for use in austere medical treatment facilities due to the need for large equipment and the turnaround time. Clinical metagenomics (CMg) has the potential to provide an alternative to current diagnostic tests enabling rapid, untargeted identification of the causative pathogen and the provision of additional clinically relevant information using equipment with a reduced logistical and operative burden.

METHODS

This study presents the development and demonstration of a CMg workflow for wound swab samples. This workflow was applied to samples prospectively collected from patients with a suspected wound infection and the results were compared to routine microbiology and real-time quantitative polymerase chain reaction (qPCR).

RESULTS

Wound swab samples were prepared for nanopore-based DNA sequencing in approximately 4 h and achieved sensitivity and specificity values of 83.82% and 66.64% respectively, when compared to routine microbiology testing and species-specific qPCR. CMg also enabled the provision of additional information including the identification of fungal species, anaerobic bacteria, antimicrobial resistance (AMR) genes and microbial species diversity.

CONCLUSIONS

This study demonstrates that CMg has the potential to provide an alternative diagnostic method for wound infections suitable for use in austere medical treatment facilities. Future optimisation should focus on increased method automation and an improved understanding of the interpretation of CMg outputs, including robust reporting thresholds to confirm the presence of pathogen species and AMR gene identifications.

The Ocular Microbiome: Micro-Steps Towards Macro-Shift in Targeted Treatment? A Comprehensive Review

A healthy ocular surface is inhabited by microorganisms that constitute the ocular microbiome. The core of the ocular microbiome is still a subject of debate. Numerous culture-dependent and gene sequencing studies have revealed the composition of the ocular microbiome. There was a confirmed correlation between the ocular microbiome and ocular surface homeostasis as well as between ocular dysbiosis and pathologies such as blepharitis, microbial keratitis, and conjunctivitis. However, the role of the ocular microbiome in the pathogenesis and treatment of ocular surface diseases remains unclear. This article reviews available data on the ocular microbiome and microbiota, their role in maintaining ocular homeostasis, and the impact of dysbiosis on several ophthalmic disorders. Moreover, we aimed to discuss potential treatment targets within the ocular microbiota.

A cross-systems primer for synthetic microbial communities

The design and use of synthetic communities, or SynComs, is one of the most promising strategies for disentangling the complex interactions within microbial communities, and between these communities and their hosts. Compared to natural communities, these simplified consortia provide the opportunity to study ecological interactions at tractable scales, as well as facilitating reproducibility and fostering interdisciplinary science. However, the effective implementation of the SynCom approach requires several important considerations regarding the development and application of these model systems. There are also emerging ethical considerations when both designing and deploying SynComs in clinical, agricultural or environmental settings. Here we outline current best practices in developing, implementing and evaluating SynComs across different systems, including a focus on important ethical considerations for SynCom research.

Illumina Sequencing in Conjunction with Propidium Monoazide to Identify Live Bacteria After Antiseptic Treatment in a Complex Oral Biofilm: A Study Using an Ex Vivo Supragingival Biofilm Model

Background/Objectives: The evaluation of the efficacy of antibacterial treatments in complex oral ecosystems is limited by the inability to differentiate live from dead bacteria using omic techniques. The objective of this study was therefore to assess the ability of the combination of the 16S rRNA Illumina sequencing methodology and the action of propidium monoazide (PMA) to study viable bacterial profiles in oral biofilms after exposure to an antiseptic compound. Methods: Cariogenic supragingival biofilms were developed in an ex vivo model for 96 h, using saliva from healthy volunteers. The biofilms were treated with 0.12% chlorhexidine (CHX) combined with 0.05% cetylpyridinium chloride (CPC), for 60 s, using phosphate buffered saline as a control. After exposure, each biofilm was treated or not with PMA to then extract the bacterial DNA, quantify it by Qubit, quantify the bacterial population using qPCR, and perform the metataxonomic study of the samples using Illumina 16S rRNA sequencing. Results: A significantly lower DNA concentration in the PMA-treated biofilms (p < 0.05 compared with those not exposed to PMA) was observed. The viable bacterial count obtained by qPCR differed significantly from the total bacterial count in the biofilm samples exposed to the antiseptic (p < 0.05). The viable microbiome differed significantly from the total bacterial profile of the samples treated with CHX/CPC after exposure to PMA (p < 0.05 at the α- and β-diversity levels). Conclusions: The combination of Illumina 16S rRNA sequencing and PMA helps solve the inability to evaluate the efficacy of antibacterial treatments in the bacterial profile of complex ecosystems such as oral biofilms.

Towards predicting the geographical origin of ancient samples with metagenomic data

Reconstructing the history-such as the place of birth and death-of an individual sample is a fundamental goal in ancient DNA (aDNA) studies. However, knowing the place of death can be particularly challenging when samples come from museum collections with incomplete or erroneous archives. While analyses of human DNA and isotope data can inform us about the ancestry of an individual and provide clues about where the person lived, they cannot specifically trace the place of death. Moreover, while ancient human DNA can be retrieved, a large fraction of the sequenced molecules in ancient DNA studies derive from exogenous DNA. This DNA-which is usually discarded in aDNA analyses-is constituted mostly by microbial DNA from soil-dwelling microorganisms that have colonized the buried remains post-mortem. In this study, we hypothesize that remains of individuals buried in the same or close geographic areas, exposed to similar microbial communities, could harbor more similar metagenomes. We propose to use metagenomic data from ancient samples' shotgun sequencing to locate the place of death of a given individual which can also help to solve cases of sample mislabeling. We used a k-mer-based approach to compute similarity scores between metagenomic samples from different locations and propose a method based on dimensionality reduction and logistic regression to assign a geographical origin to target samples. We apply our method to several public datasets and observe that individual samples from closer geographic locations tend to show higher similarities in their metagenomes compared to those of different origin, allowing good geographical predictions of test samples. Moreover, we observe that the genus Streptomyces commonly infiltrates ancient remains and represents a valuable biomarker to trace the samples' geographic origin. Our results provide a proof of concept and show how metagenomic data can also be used to shed light on the place of origin of ancient samples.

Metrological evaluation of DNA extraction method effects on the bacterial microbiome and resistome in sputum

Targeted high-throughput sequencing (HTS) has revolutionized the way we look at bacterial communities. It can be used for the species-specific detection of bacteria as well as for the determination of the microbiome and resistome and can be applied to samples from almost any environment. However, the results of targeted HTS can be influenced by many factors, which poses a major challenge for its use in clinical diagnostics. In this study, we investigated the impact of the DNA extraction method on the determination of the bacterial microbiome and resistome by targeted HTS using principles from metrology and diagnostics such as repeatability and analytical sensitivity. Sputum samples spiked with Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa at three different concentrations (103-106 cells/mL) were used. DNA was extracted from each sample on 2 separate days in three replicates each using three different extraction methods based on cetrimonium bromide, magnetic beads, and silica membranes. All three spiked bacteria were detected in sputum, and the DNA extraction method had no significant effect on detection. However, the DNA extraction method had significant effects on the composition of the microbiome and the resistome. The sequencing results were repeatable in the majority of cases. The silica membrane-based DNA extraction kit provided the most repeatable results and the highest diversity of the microbiome and resistome. Targeted HTS has been shown to be a reliable tool for determining the microbiome and resistome; however, the method of DNA extraction should be carefully selected to minimize its impact on the results.

IMPORTANCE

High-throughput sequencing (HTS) is one of the crucial new technologies that gives us insights into previously hidden parts of microbial communities. The DNA extraction method is an important step that can have a major impact on the results, and understanding this impact is of paramount importance for their reliable interpretation. Our results are of great value for the interpretation of sputum microbiome and resistome results obtained by targeted HTS. Our findings allow for a more rational design of future microbiome studies, which would lead to higher repeatability of results and easier comparison between different laboratories. This could also facilitate the introduction of targeted HTS in clinical microbiology for reliable identification of pathogenic bacteria and testing for antimicrobial resistance (AMR). As AMR is a major threat to public health, the improved methods for determining AMR would bring great benefits to both the healthcare system and society as a whole.

Symbiotic symphony: Understanding host-microbiota dialogues in a spatial context

Modern precision sequencing techniques have established humans as a holobiont that live in symbiosis with the microbiome. Microbes play an active role throughout the life of a human ranging from metabolism and immunity to disease tolerance. Hence, it is of utmost significance to study the eukaryotic host in conjunction with the microbial antigens to obtain a complete picture of the host-microbiome crosstalk. Previous attempts at profiling host-microbiome interactions have been either superficial or been attempted to catalogue eukaryotic transcriptomic profile and microbial communities in isolation. Additionally, the nature of such immune-microbial interactions is not random but spatially organised. Hence, for a holistic clinical understanding of the interplay between hosts and microbiota, it's imperative to concurrently analyze both microbial and host genetic information, ensuring the preservation of their spatial integrity. Capturing these interactions as a snapshot in time at their site of action has the potential to transform our understanding of how microbes impact human health. In examining early-life microbial impacts, the limited presence of communities compels analysis within reduced biomass frameworks. However, with the advent of spatial transcriptomics we can address this challenge and expand our horizons of understanding these interactions in detail. In the long run, simultaneous spatial profiling of host-microbiome dialogues can have enormous clinical implications especially in gaining mechanistic insights into the disease prognosis of localised infections and inflammation. This review addresses the lacunae in host-microbiome research and highlights the importance of profiling them together to map their interactions while preserving their spatial context.

Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective

As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research. We highlight key approaches to alleviate these potential pitfalls, combining experimental planning strategies and data analysis methods.

Unabsorbed Fecal Fat Content Correlates with a Reduction of Immunoglobulin a Coating of Gut Bacteria in High-Lard Diet-Fed Mice

SCOPE

Immunoglobulin A (IgA) selectively coats gut bacteria and contributes to regulatory functions in gastrointestinal inflammation and glucose metabolism. Excess intake of lard leads to decrease in the IgA coating of gut bacteria, although the underlying mechanisms remain unknown. This study validates how unabsorbed fat derived from a high-lard diet in the gut affects the IgA coating of bacteria, as assessed in mouse models using three types of dietary fat (lard, medium-, and long-chain triglycerides [MLCTs], and medium-chain triglycerides [MCTs]) exhibiting different digestibilities.

METHODS AND RESULTS

C57BL/6J mice are maintained on diets containing lard, MLCTs, or MCTs at 7% or 30% w/w for 10 weeks (n = 6 per group). The fecal fatty acid concentration is measured to quantify unabsorbed fat content. The ratio of IgA-coated bacteria to total bacteria (IgA coating ratio) in the feces is measured by flow cytometry. Compared to lard-fed mice, MLCT- and MCT-fed mice exhibit lower fecal concentrations of palmitic acid, stearic acid, and oleic acid and higher IgA coating ratios at both 7% and 30% dietary fat, and these parameters exhibit significant negative correlations.

CONCLUSION

Unabsorbed fat content in the gut may result in attenuated IgA coating of bacteria in high-lard diet-fed mice.

Unveiling errors in soil microbial community sequencing: a case for reference soils and improved diagnostics for nanopore sequencing

The sequencing platform and workflow strongly influence microbial community analyses through potential errors at each step. Effective diagnostics and experimental controls are needed to validate data and improve reproducibility. This cross-laboratory study evaluates sources of variability and error at three main steps of a standardized amplicon sequencing workflow (DNA extraction, polymerase chain reaction [PCR], and sequencing) using Oxford Nanopore MinION to analyze agricultural soils and a simple mock community. Variability in sequence results occurs at each step in the workflow with PCR errors and differences in library size greatly influencing diversity estimates. Common bioinformatic diagnostics and the mock community are ineffective at detecting PCR abnormalities. This work outlines several diagnostic checks and techniques to account for sequencing depth and ensure accuracy and reproducibility in soil community analyses. These diagnostics and the inclusion of a reference soil can help ensure data validity and facilitate the comparison of multiple sequencing runs within and between laboratories.

Comparative evaluation of 16S rRNA primer pairs in identifying nitrifying guilds in soils under long-term organic fertilization and water management

Compared with 454 sequencing technology, short-read sequencing (e.g., Illumina) technology generates sequences of high accuracy, but limited length (<500 bp). Such a limitation can prove that studying a target gene using a large amplicon (>500 bp) is challenging. The ammonia monooxygenase subunit A (amoA) gene of ammonia-oxidizing archaea (AOA), which plays a crucial part in the nitrification process, is such a gene. By providing a full overview of the community of a functional microbial guild, 16S ribosomal ribonucleic acid (rRNA) gene sequencing could overcome this problem. However, it remains unclear how 16S rRNA primer selection influences the quantification of relative abundance and the identification of community composition of nitrifiers, especially AOA. In the present study, a comparison was made between the performance of primer pairs 338F-806R, 515F-806R, and 515F-907R to a shotgun metagenome approach. The structure of nitrifier communities subjected to different long-term organic matter amendment and water management protocols was assessed. Overall, we observed higher Chao1 richness diversity of soil total bacteria by using 515F-806R compared to 338F-806R and 515F-907R, while higher Pielou's evenness diversity was observed by using 515F-806R and 515F-907R compared to 338F-806R. The studied primer pairs revealed different performances on the relative abundance of Thaumarchaeota, AOB, and NOB. The Thaumarchaeota 16S rRNA sequence was rarely detected using 338F-806R, while the relative abundances of Thaumarchaeota detected using 515F-806R were higher than those detected by using 515F-907R. AOB showed higher proportions in the 338F-806R and 515F-907R data, than in 515F-806R data. Different primers pairs showed significant change in relative proportion of NOB. Nonetheless, we found consistent patterns of the phylotype distribution of nitrifiers in different treatments. Nitrosopumilales (NP) and Nitrososphaerales (NS) clades were the dominant members of the AOA community in soils subject to controlled irrigation, whereas Ca. Nitrosotaleales (NT) and NS clades dominated the AOA community in soils subject to flooding irrigation. Nitrospira lineage II was the dominant NOB phylotype in all samples. Overall, ideal 16S rRNA primer pairs were identified for the analysis of nitrifier communities. Moreover, NP and NT clades of AOA might have distinct environmental adaptation strategies under different irrigation treatments.

Deciphering the microbial communities of alkaline hot spring in Panamik, Ladakh, India using a high-throughput sequencing approach

Due to their distinctive physicochemical characteristics, hot springs are extremely important. The whole genome metagenomic sequencing technology can be utilized to analyze the diverse microbial community that thrives in this habitat due to the particular selection pressure that prevails there. The current investigation emphasizes on culture-independent metagenomic study of the Panamik hot spring and its nearby areas from Ladakh, India. Based on different diversity indices, sequence analysis of the soil reservoir showed higher species richness and diversity in comparison to water and sediment samples. The mineral content and various physicochemical pameters like temperature, pH had an impact on the composition of the microbial community of the geothermal springs. The phyla Proteobacteria, Cyanobacteria, Bacteroidetes, Actinobacter, Firmicutes, and Verrucomicrobia in bacterial domain dominate the thermos-alkaline spring at Panamik in different concentrations. Economically significant microbes from the genera Actinobacter, Thermosynechoccus, Candidatus Solibacter, Chthoniobacter, Synechoccus, Pseudomonas and Sphingomonas, were prevalent in hot spring. In the archaeal domain, the most dominant phylum and genera were Euryarchaeota and Thermococcus in all the samples. Further, the most abundant species were Methanosarcina barkeri, Nitrospumilus maritimus and Methanosarcina acetivorans. The present study which only examined one of the several thermal springs present in the Himalayan geothermal area, should be regarded as a preliminary investigation of the microbiota that live in the hot springs on these remote areas. These findings suggest that further investigations should be undertaken to characterize the ecosystems of the Panamik hot spring, which serve as a repository for unidentified microbial lineages.

Evaluating the antibacterial efficacy of a silver nanocomposite surface coating against nosocomial pathogens as an antibiofilm strategy to prevent hospital infections

Antimicrobial nanocoatings may be a means of preventing nosocomial infections, which account for significant morbidity and mortality. The role of hospital sink traps in these infections is also increasingly appreciated. We describe the preparation, material characterization and antibacterial activity of a pipe cement-based silver nanocoating applied to unplasticized polyvinyl chloride, a material widely used in wastewater plumbing. Three-dimensional surface topography imaging and scanning electron microscopy showed increased roughness in all surface finishes versus control, with grinding producing the roughest surfaces. Silver stability within nanocoatings was >99.89% in deionized water and bacteriological media seeded with bacteria. The nanocoating exhibited potent antibiofilm (99.82-100% inhibition) and antiplanktonic (99.59-99.99% killing) activity against three representative bacterial species and a microbial community recovered from hospital sink traps. Hospital sink trap microbiota were characterized by sequencing the 16S rRNA gene, revealing the presence of opportunistic pathogens from genera including Pseudomonas, Enterobacter and Clostridioides. In a benchtop model sink trap system, nanocoating antibiofilm activity against this community remained significant after 11 days but waned following 25 days. Silver nanocoated disks in real-world sink traps in two university buildings had a limited antibiofilm effect, even though in vitro experiments using microbial communities recovered from the same traps demonstrated that the nanocoating was effective, reducing biofilm formation by >99.6% and killing >98% of planktonic bacteria. We propose that conditioning films forming in the complex conditions of real-world sink traps negatively impact nanocoating performance, which may have wider relevance to development of antimicrobial nanocoatings that are not tested in the real-world.

Using short-read 16S rRNA sequencing of multiple variable regions to generate high-quality results to a species level

Introduction

Short-read amplicon sequencing studies have typically focused on 1-2 variable regions of the 16S rRNA gene. Species-level resolution is limited in these studies, as each variable region enables the characterisation of a different subsection of the microbiome. Although long-read sequencing techniques take advantage of all 9 variable regions by sequencing the entire 16S rRNA gene, they are substantially more expensive. This work assessed the feasibility of accurate species-level resolution and reproducibility using a relatively new sequencing kit and bioinformatics pipeline developed for short-read sequencing of multiple variable regions of the 16S rRNA gene. In addition, we evaluated the potential impact of different sample collection methods on our outcomes.

Methods

Using xGen™ 16S Amplicon Panel v2 kits, sequencing of all 9 variable regions of the 16S rRNA gene was carried out on an Illumina MiSeq platform. Mock cells and mock DNA for 8 bacterial species were included as extraction and sequencing controls respectively. Within-run and between-run replicate samples, and pairs of stool and rectal swabs collected at 0-5 weeks from the same participants, were incorporated. Observed relative abundances of each species were compared to theoretical abundances provided by ZymoBIOMICS. Paired Wilcoxon rank sum tests and distance-based intraclass correlation coefficients were used to statistically compare alpha and beta diversity measures, respectively, for pairs of replicates and stool/rectal swab sample pairs.

Results

Using multiple variable regions of the 16S ribosomal Ribonucleic Acid (rRNA) gene, we found that we could accurately identify taxa to a species level and obtain highly reproducible results at a species level. Yet, the microbial profiles of stool and rectal swab sample pairs differed substantially despite being collected concurrently from the same infants.

Conclusion

This protocol provides an effective means for studying infant gut microbial samples at a species level. However, sample collection approaches need to be accounted for in any downstream analysis.

Shotgun metagenomic analysis of saliva microbiome suggests Mogibacterium as a factor associated with chronic bacterial osteomyelitis

Osteomyelitis of the jaw is a severe inflammatory disorder that affects bones, and it is categorized into two main types: chronic bacterial and nonbacterial osteomyelitis. Although previous studies have investigated the association between these diseases and the oral microbiome, the specific taxa associated with each disease remain unknown. In this study, we conducted shotgun metagenome sequencing (≥10 Gb from ≥66,395,670 reads per sample) of bulk DNA extracted from saliva obtained from patients with chronic bacterial osteomyelitis (N = 5) and chronic nonbacterial osteomyelitis (N = 10). We then compared the taxonomic composition of the metagenome in terms of both taxonomic and sequence abundances with that of healthy controls (N = 5). Taxonomic profiling revealed a statistically significant increase in both the taxonomic and sequence abundance of Mogibacterium in cases of chronic bacterial osteomyelitis; however, such enrichment was not observed in chronic nonbacterial osteomyelitis. We also compared a previously reported core saliva microbiome (59 genera) with our data and found that out of the 74 genera detected in this study, 47 (including Mogibacterium) were not included in the previous meta-analysis. Additionally, we analyzed a core-genome tree of Mogibacterium from chronic bacterial osteomyelitis and healthy control samples along with a reference complete genome and found that Mogibacterium from both groups was indistinguishable at the core-genome and pan-genome levels. Although limited by the small sample size, our study provides novel evidence of a significant increase in Mogibacterium abundance in the chronic bacterial osteomyelitis group. Moreover, our study presents a comparative analysis of the taxonomic and sequence abundances of all genera detected using deep salivary shotgun metagenome data. The distinct enrichment of Mogibacterium suggests its potential as a marker to distinguish between patients with chronic nonbacterial osteomyelitis and chronic bacterial osteomyelitis, particularly at the early stages when differences are unclear.

Informed development of a multi-species biofilm in chronic obstructive pulmonary disease

Recent evidence indicates that microbial biofilm aggregates inhabit the lungs of COPD patients and actively contribute towards chronic colonization and repeat infections. However, there are no contextually relevant complex biofilm models for COPD research. In this study, a meta-analysis of the lung microbiome in COPD was used to inform development of an optimized biofilm model composed of genera highly associated with COPD. Bioinformatic analysis showed that although diversity matrices of COPD microbiomes were similar to healthy controls, and internal compositions made it possible to accurately differentiate between these cohorts (AUC = 0.939). Genera that best defined these patients included Haemophilus, Moraxella and Streptococcus. Many studies fail to account for fungi; therefore, Candida albicans was included in the creation of an interkingdom biofilm model. These organisms formed a biofilm capable of tolerating high concentrations of antimicrobial therapies with no significant reductions in viability. However, combined therapies of antibiotics and an antifungal resulted in significant reductions in viable cells throughout the biofilm (p < 0.05). This biofilm model is representative of the COPD lung microbiome and results from in vitro antimicrobial challenge experiments indicate that targeting both bacteria and fungi in these interkingdom communities will be required for more positive clinical outcomes.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

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

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

Andean soil-derived lignocellulolytic bacterial consortium as a source of novel taxa and putative plastic-active enzymes

An easy and straightforward way to engineer microbial environmental communities is by setting up liquid enrichment cultures containing a specific substrate as the sole source of carbon. Here, we analyzed twenty single-contig high-quality metagenome-assembled genomes (MAGs) retrieved from a microbial consortium (T6) that was selected by the dilution-to-stimulation approach using Andean soil as inoculum and lignocellulose as a selection pressure. Based on genomic metrics (e.g., average nucleotide and amino acid identities) and phylogenomic analyses, 15 out of 20 MAGs were found to represent novel bacterial species, with one of those (MAG_26) belonging to a novel genus closely related to Caenibius spp. (Sphingomonadaceae). Following the rules and requirements of the SeqCode, we propose the name Andeanibacterium colombiense gen. nov., sp. nov. for this taxon. A subsequent functional annotation of all MAGs revealed that MAG_7 (Pseudobacter hemicellulosilyticus sp. nov.) contains 20, 19 and 16 predicted genes from carbohydrate-active enzymes families GH43, GH2 and GH92, respectively. Its lignocellulolytic gene profile resembles that of MAG_2 (the most abundant member) and MAG_3858, both of which belong to the Sphingobacteriaceae family. Using a database that contains experimentally verified plastic-active enzymes (PAZymes), twenty-seven putative bacterial polyethylene terephthalate (PET)-active enzymes (i.e., alpha/beta-fold hydrolases) were detected in all MAGs. A maximum of five putative PETases were found in MAG_3858, and two PETases were found to be encoded by A. colombiense. In conclusion, we demonstrate that lignocellulose-enriched liquid cultures coupled with genome-resolved metagenomics are suitable approaches to unveil the hidden bacterial diversity and its polymer-degrading potential in Andean soil ecosystems.

Comparison of culture and PCR-DGGE methods to evaluate the airways of cystic fibrosis patients and determination of their antibiotic resistance profile

Background and Objectives

Respiratory infections are the most serious condition in cystic fibrosis (CF) patients; therefore, a thorough comprehension of the diversity and dominant microbial species in CF airways has a crucial role in treatment. Our objective was to determine the antibiotic resistance profile of CF airways microbiota and compare culture methods and PCR-DGGE to evaluate bacterial diversity.

Materials and Methods

Pharyngeal swabs from 121 CF patients were collected. The samples were then cultured, identified and antibiotic resistance testing was performed. Thirty samples were subjected to further molecular surveys. DNA contents of these samples were extracted and amplified using nested-PCR technique and their bacterial diversity was assessed by DGGE. The DGGE patterns were visualized and certain bands were excised and purified. Next, the DNA was amplified by another round of PCR and sent out for sequencing.

Results

Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae were the most prevalent species isolated using culture methods. S. aureus was the most common bacteria among 6 years and younger patients; while, P. aeruginosa had more prevalence among older ones. The PCR-DGGE results showed more diversity than culture methods, particularly in younger patients who exhibited more bacterial diversity than the older groups. Sequencing results unveiled the presence of certain bacterial species including Haemophilus parainfluenzae and Stenotrophomonas maltophilia which were completely missed in culture.

Conclusion

Even though culture-dependent methods are cost-effective, PCR-DGGE appeared to be more efficient to determine bacterial diversity. PCR-DGGE detects less abundant species, though their viability could not be determined using this method.

Primer selection impacts the evaluation of microecological patterns in environmental microbiomes

This study revealed that primer selection substantially influences the taxonomic and predicted functional composition and the characterization of microecological patterns, which was not alleviated by close-reference clustering. Biases were relatively consistent across different habitats in community profiling but not in microecological patterns. These primer biases could be attributed to multiple aspects, including taxa specificity, regional hypervariability, and amplification efficiency.

GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry

Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.

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.

Culturomics in Unraveling the Upper Female Reproductive Tract Microbiota

In recent years, the study of the human microbiome has surged, shedding light on potential connections between microbiome composition and various diseases. One specific area of intense interest within this research is the female reproductive tract, as it holds the potential to influence the process of embryo implantation. Advanced sequencing technologies have delivered unprecedented insights into the microbial communities, also known as microbiota, residing in the female reproductive tract. However, their efficacy encounters significant challenges when analyzing low-biomass microbiota, such as those present in the endometrium. These molecular techniques are susceptible to contamination from laboratory reagents and extraction kits, leading to sequencing bias that can significantly alter the perceived taxonomy of a sample. Consequently, investigating the microbiota of the upper female reproductive tract necessitates the exploration of alternative methods. In this context, the current review delves into the application of culturomics in unraveling the upper female reproductive tract microbiota. While culturomics holds value in research, its transition to routine clinical practice appears remote, at least in the foreseeable future.

Guidelines for microbiome studies in renal physiology

Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.

Quantitative differences in synthetic gut microbial inoculums do not affect the final stabilized in vitro community compositions

In vitro studies of synthetic gut microbial communities (SGMCs) can provide valuable insights into the ecological structure and function of gut microbiota. However, the importance of the quantitative composition of an SGMC inoculum and its effect on the eventual stable in vitro microbial community has not been studied. To address this, we constructed two 114-member SGMCs differing only in their quantitative composition-one reflecting the average human fecal microbiome and another mixed in equal proportions based on cell counts. We inoculated each in an automated anaerobic multi-stage in vitro gut fermentor simulating two different colonic conditions, mimicking proximal and distal colons. We replicated this setup with two different nutrient media, periodically sampled the cultures for 27 days, and profiled their microbiome compositions using 16S rRNA gene amplicon sequencing. While nutrient medium explained 36% of the variance in microbiome composition, initial inoculum composition failed to show a statistically significant effect. Under all four conditions, paired fecal and equal SGMC inoculums converged to reach stable community compositions resembling each other. Our results have broad implications for simplifying in vitro SGMC investigations. IMPORTANCE In vitro cultivation of synthetic gut microbial communities (SGMCs) can provide valuable insights into the ecological structure and function of gut microbiota. However, it is currently not known whether the quantitative composition of the initial inoculum can influence the eventual stable in vitro community structure. Hence, using two SGMC inoculums consisting of 114 unique species mixed in either equal proportions (Eq inoculum) or resembling proportions in an average human fecal microbiome (Fec inoculum), we show that initial inoculum compositions did not influence the final stable community structure in a multi-stage in vitro gut fermentor. Under two different nutrient media and two different colon conditions (proximal and distal), both Fec and Eq communities converged to resemble each other's community structure. Our results suggest that the time-consuming preparation of SGMC inoculums may not be needed and has broad implications for in vitro SGMC studies.

Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems

Microbes continually shape Earth's biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as "microbial dark matter," the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of "microbial dark matter" in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.

Evaluating cpn60 for high-resolution profiling of the mammalian skin microbiome and detection of phylosymbiosis

Despite being the most widely used phylogenetic marker for amplicon-based profiling of microbial communities, limited phylogenetic resolution of the 16S rRNA gene limits its use for studies of host-microbe co-evolution. In contrast, the cpn60 gene is a universal phylogenetic marker with greater sequence variation capable of species-level resolution. This research compared mammalian skin microbial profiles generated from cpn60 and 16S rRNA gene sequencing approaches, testing for patterns of phylosymbiosis that suggest co-evolutionary host-microbe associations. An ~560 bp fragment of the cpn60 gene was amplified with universal primers and subjected to high-throughput sequencing. Taxonomic classification of cpn60 sequences was completed using a naïve-Bayesian QIIME2 classifier created for this project, trained with an NCBI-supplemented curated cpn60 database (cpnDB_nr). The cpn60 dataset was then compared to published 16S rRNA gene amplicon data. Beta diversity comparisons of microbial community profiles generated with cpn60 and 16S rRNA gene amplicons were not significantly different, based on Procrustes analysis of Bray-Curtis and UniFrac distances. Despite similar relationships among skin microbial profiles, improved phylogenetic resolution provided by the cpn60 gene sequencing permitted observations of phylosymbiosis between microbial community profiles and their mammalian hosts that were not previously observed with 16S rRNA gene profiles. Subsequent investigation of Staphylococcaceae taxa using the cpn60 gene showed increased phylogenetic resolution compared the 16S rRNA gene profiles, revealing potential co-evolutionary host-microbe associations. Overall, our results demonstrate that 16S rRNA and cpn60 marker genes generate comparable microbial community composition patterns while cpn60 better facilitates analyses, such as phylosymbiosis, that require increased phylogenetic resolution.

Succession and determinants of the early life nasopharyngeal microbiota in a South African birth cohort

BACKGROUND

Bacteria colonizing the nasopharynx play a key role as gatekeepers of respiratory health. Yet, dynamics of early life nasopharyngeal (NP) bacterial profiles remain understudied in low- and middle-income countries (LMICs), where children have a high prevalence of risk factors for lower respiratory tract infection. We investigated longitudinal changes in NP bacterial profiles, and associated exposures, among healthy infants from low-income households in South Africa.

METHODS

We used short fragment (V4 region) 16S rRNA gene amplicon sequencing to characterize NP bacterial profiles from 103 infants in a South African birth cohort, at monthly intervals from birth through the first 12 months of life and six monthly thereafter until 30 months.

RESULTS

Corynebacterium and Staphylococcus were dominant colonizers at 1 month of life; however, these were rapidly replaced by Moraxella- or Haemophilus-dominated profiles by 4 months. This succession was almost universal and largely independent of a broad range of exposures. Warm weather (summer), lower gestational age, maternal smoking, no day-care attendance, antibiotic exposure, or low height-for-age z score at 12 months were associated with higher alpha and beta diversity. Summer was also associated with higher relative abundances of Staphylococcus, Streptococcus, Neisseria, or anaerobic gram-negative bacteria, whilst spring and winter were associated with higher relative abundances of Haemophilus or Corynebacterium, respectively. Maternal smoking was associated with higher relative abundances of Porphyromonas. Antibiotic therapy (or isoniazid prophylaxis for tuberculosis) was associated with higher relative abundance of anerobic taxa (Porphyromonas, Fusobacterium, and Prevotella) and with lower relative abundances of health associated-taxa Corynebacterium and Dolosigranulum. HIV-exposure was associated with higher relative abundances of Klebsiella or Veillonella and lower relative abundances of an unclassified genus within the family Lachnospiraceae.

CONCLUSIONS

In this intensively sampled cohort, there was rapid and predictable replacement of early profiles dominated by health-associated Corynebacterium and Dolosigranulum with those dominated by Moraxella and Haemophilus, independent of exposures. Season and antibiotic exposure were key determinants of NP bacterial profiles. Understudied but highly prevalent exposures prevalent in LMICs, including maternal smoking and HIV-exposure, were associated with NP bacterial profiles. Video Abstract.

Human skin bacterial microbiota homeostasis: A delicate balance between health and disease

As the largest organ of the body, the skin acts as a barrier to prevent diseases and harbors a variety of beneficial bacteria. Furthermore, the skin bacterial microbiota plays a vital role in health and disease. Disruption of the barrier or an imbalance between symbionts and pathogens can lead to skin disorders or even systemic diseases. In this review, we first provide an overview of research on skin bacterial microbiota and human health, including the composition of skin bacteria in a healthy state, as well as skin bacterial microbiota educating the immune system and preventing the invasion of pathogens. We then discuss the diseases that result from skin microbial dysbiosis, including atopic dermatitis, common acne, chronic wounds, psoriasis, viral transmission, cutaneous lupus, cutaneous lymphoma, and hidradenitis suppurativa. Finally, we highlight the progress that utilizes skin microorganisms for disease therapeutics, such as bacteriotherapy and skin microbiome transplantation. A deeper knowledge of the interaction between human health and disease and the homeostasis of the skin bacterial microbiota will lead to new insights and strategies for exploiting skin bacteria as a novel therapeutic target.

Improving the assessment of ecosystem and wildlife health: microbiome as an early indicator

Human activities are causing dramatic declines in ecosystem health, compromising the functioning of the life-support system, economic activity, and animal and human health. In this context, monitoring the health of ecosystems and wildlife populations is crucial for determining ecological dynamics and assessing management interventions. A growing body of evidence indicates that microbiome provides a meaningful early indicator of ecosystem and wildlife health. Microbiome is ubiquitous and both environmental and host-associated microbiomes rapidly reflect anthropogenic disturbances. However, we still need to overcome current limitations such as nucleic acid degradation, sequencing depth, and the establishment of baseline data to maximize the potential of microbiome studies.

Cecal Microbiota Development and Physiological Responses of Broilers Following Early Life Microbial Inoculation Using Different Delivery Methods and Microbial Sources

Broilers in intensive systems may lack commensal microbes that have coevolved with chickens in nature. This study evaluated the effects of microbial inocula and delivery methods applied to day-old chicks on the development of the cecal microbiota. Specifically, chicks were inoculated with cecal contents or microbial cultures, and the efficacies of three delivery methods (oral gavage, spraying inoculum into the bedding, and cohousing) were evaluated. Also, a competitive study evaluated the colonization ability of bacteria sourced from extensive or intensive poultry production systems. The microbiota of inoculated birds presented higher phylogenetic diversity values (PD) and higher relative abundance values of Bacteroidetes, compared with a control. Additionally, a reduction in the ileal villus height/crypt depth ratio and increased cecal IL-6, IL-10, propionate, and valerate concentrations were observed in birds that were inoculated with cecal contents. Across the experiments, the chicks in the control groups presented higher relative abundance values of Escherichia/Shigella than did the inoculated birds. Specific microbes from intensively or extensively raised chickens were able to colonize the ceca, and inocula from intensive production systems promoted higher relative abundance values of Escherichia/Shigella. We concluded that Alistipes, Bacteroides, Barnesiella, Mediterranea, Parabacteroides, Megamonas, and Phascolarctobacterium are effective colonizers of the broiler ceca. In addition, oral gavage, spray, and cohousing can be used as delivery methods for microbial transplantation, as indicated by their effects on the cecal microbiota, intestinal morphology, short-chain fatty acids concentration, and cytokine/chemokine levels. These findings will guide future research on the development of next-generation probiotics that are able to colonize and persist in the chicken intestinal tract after a single exposure. IMPORTANCE The strict biosecurity procedures employed in the poultry industry may inadvertently hinder the transmission of beneficial commensal bacteria that chickens would encounter in natural environments. This research aims at identifying bacteria that can colonize and persist in the chicken gut after a single exposure. We evaluated different microbial inocula that were obtained from healthy adult chicken donors as well as three delivery methods for their effects on microbiota composition and bird physiology. In addition, we conducted a competitive assay to test the colonization abilities of bacteria sourced from intensively versus extensively raised chickens. Our results indicated that some bacteria are consistently increased in birds that are exposed to microbial inoculations. These bacteria can be isolated and employed in future research on the development of next-generation probiotics that contain species that are highly adapted to the chicken gut.

Reconstructing the landscape of gut microbial species across 29,000 diverse individuals

The human gut microbiome has been linked to health and disease. Investigation of the human microbiome has largely employed 16S amplicon sequencing, with limited ability to distinguish microbes at the species level. Herein, we describe the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants that enables mapping of microbial species from standard 16S sequencing data. RExMap analysis of 16S data captures ∼75% of microbial species identified by whole-genome shotgun sequencing, despite hundreds-fold less sequencing depth. RExMap re-analysis of existing 16S data from 29,349 individuals across 16 regions from around the world reveals a detailed landscape of gut microbial species across populations and geography. Moreover, RExMap identifies a core set of fifteen gut microbes shared by humans. Core microbes are established soon after birth and closely associate with BMI across multiple independent studies. RExMap and the human microbiome dataset are presented as resources with which to explore the role of the human microbiome.

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.

Comparison of commercial DNA extraction kits for whole metagenome sequencing of human oral, vaginal, and rectal microbiome samples

Introduction

Advancements in DNA extraction and sequencing technologies have been fundamental in deciphering the significance of the microbiome related to human health and pathology. Whole metagenome shotgun sequencing (WMS) is gaining popularity in use compared to its predecessor (i.e., amplicon-based approaches). However, like amplicon-based approaches, WMS is subject to bias from DNA extraction methods that can compromise the integrity of sequencing and subsequent findings. The purpose of this study was to evaluate systematic differences among four commercially available DNA extraction kits frequently used for WMS analysis of the microbiome.

Methods

Oral, vaginal, and rectal swabs were collected in replicates of four by a healthcare provider from five participants and randomized to one of four DNA extraction kits. Two extraction blanks and three replicate mock community samples were also extracted using each extraction kit. WMS was completed with NovaSeq 6000 for all samples. Sequencing and microbial communities were analyzed using nonmetric multidimensional scaling and compositional bias analysis.

Results

Extraction kits differentially biased the percentage of reads attributed to microbial taxa across samples and body sites. The PowerSoil Pro kit performed best in approximating expected proportions of mock communities. While HostZERO was biased against gram-negative bacteria, the kit outperformed other kits in extracting fungal DNA. In clinical samples, HostZERO yielded a smaller fraction of reads assigned to Homo sapiens across sites and had a higher fraction of reads assigned to bacterial taxa compared to other kits. However, HostZERO appears to bias representation of microbial communities and demonstrated the most dispersion by site, particularly for vaginal and rectal samples.

Conclusions

Systematic differences exist among four frequently referenced DNA extraction kits when used for WMS analysis of the human microbiome. Consideration of such differences in study design and data interpretation is imperative to safeguard the integrity of microbiome research and reproducibility of results.

Evaluation of DNA extraction methods and direct PCR in metabarcoding of mock and marine bacterial communities

Recent advances in new molecular biology methods and next-generation sequencing (NGS) technologies have revolutionized metabarcoding studies investigating complex microbial communities from various environments. The inevitable first step in sample preparation is DNA extraction which introduces its own set of biases and considerations. In this study, we assessed the influence of five DNA extraction methods [B1: phenol/chloroform/isoamyl extraction, B2 and B3: isopropanol and ethanol precipitations, respectively-both modifications of B1, K1: DNeasy PowerWater Kit (QIAGEN), K2: modified DNeasy PowerWater Kit (QIAGEN) and direct PCR approach (P) that completely circumvents this step on community composition and DNA yield of mock and marine sample communities from the Adriatic Sea]. B1-B3 methods generally produced higher DNA yields and more similar microbial communities, but with higher interindividual variability. Each method demonstrated significant differences in a specific community structure, where rare taxa seem to play a crucial role. There was not one superior method closest to the theoretically expected mock community composition, they all demonstrated skewed ratios, but in a similar way which might be attributed to other factors, such as primer bias or 16S rRNA gene count for specific taxa. Direct PCR represents an interesting approach when high throughput in sample processing is required. We emphasize the importance of making a cautious decision about the choice of the extraction method or direct PCR approach, but even more importantly its consistent application throughout the study.