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

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.

A Mixed Effect Similarity Matrix Regression Model (SMRmix) for Integrating Multiple Microbiome Datasets at Community Level and its Application in HIV

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. Integrative analysis of multiple microbiome datasets is necessary. However, statistical methods that incorporate multiple microbiome datasets and account for the study heterogeneity are not available in the literature. 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. Via extensive simulations, we show that SMRmix has well-controlled type I error and higher power than some potential competitors. We also applied SMRmix to data from the HIV-reanalysis consortium, a collective effort that obtained all publicly available data on gut microbiome and HIV at December 2017, and obtained consistent associations of gut microbiome with HIV infection, and with MSM status (i.e. men who have sex with men).

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.

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.

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.

Univariate and Multivariate Statistical Analysis of Microbiome Data: An Overview

Microbiome data is high dimensional, sparse, compositional, and over-dispersed. Therefore, modeling microbiome data is very challenging and it is an active research area. Microbiome analysis has become a progressing area of research as microorganisms constitute a large part of life. Since many methods of microbiome data analysis have been presented, this review summarizes the challenges, methods used, and the advantages and disadvantages of those methods, to serve as an updated guide for those in the field. This review also compared different methods of analysis to progress the development of newer methods.

Comparing Vaginal and Endometrial Microbiota Using Culturomics: Proof of Concept

It is generally accepted that microorganisms can colonize a non-pathological endometrium. However, in a clinical setting, endometrial samples are always collected by passing through the vaginal-cervical route. As such, the vaginal and cervical microbiomes can easily cross-contaminate endometrial samples, resulting in a biased representation of the endometrial microbiome. This makes it difficult to demonstrate that the endometrial microbiome is not merely a reflection of contamination originating from sampling. Therefore, we investigated to what extent the endometrial microbiome corresponds to that of the vagina, applying culturomics on paired vaginal and endometrial samples. Culturomics could give novel insights into the microbiome of the female genital tract, as it overcomes sequencing-related bias. Ten subfertile women undergoing diagnostic hysteroscopy and endometrial biopsy were included. An additional vaginal swab was taken from each participant right before hysteroscopy. Both endometrial biopsies and vaginal swabs were analyzed using our previously described WASPLab-assisted culturomics protocol. In total, 101 bacterial and two fungal species were identified among these 10 patients. Fifty-six species were found in endometrial biopsies and 90 were found in vaginal swabs. On average, 28 % of species were found in both the endometrial biopsy and vaginal swab of a given patient. Of the 56 species found in the endometrial biopsies, 13 were not found in the vaginal swabs. Of the 90 species found in vaginal swabs, 47 were not found in the endometrium. Our culturomics-based approach sheds a different light on the current understanding of the endometrial microbiome. The data suggest the potential existence of a unique endometrial microbiome that is not merely a presentation of cross-contamination derived from sampling. However, we cannot exclude cross-contamination completely. In addition, we observe that the microbiome of the vagina is richer in species than that of the endometrium, which contradicts the current sequence-based literature.

An Update on the Ocular Surface Bacterial Microbiota in Small Animals

High-throughput sequencing (HTS) techniques have revolutionized the way we understand microbial communities in both research and clinical settings and are bringing new insights into what constitutes a healthy ocular surface (and a diseased one). As more diagnostic laboratories incorporate HTS into their technique repertoire, practitioners can expect this technology to become increasingly accessible for clinical practice, potentially becoming the new standard. However, particularly regarding ophthalmic microbiota, considerable research remains to render HTS accessible and applicable.

Lower respiratory tract microbiome composition and community interactions in smokers

The lung microbiome impacts on lung function, making any smoking-induced changes in the lung microbiome potentially significant. The complex co-occurrence and co-avoidance patterns between the bacterial taxa in the lower respiratory tract (LRT) microbiome were explored for a cohort of active (AS), former (FS) and never (NS) smokers. Bronchoalveolar lavages (BALs) were collected from 55 volunteer subjects (9 NS, 24 FS and 22 AS). The LRT microbiome composition was assessed using 16S rRNA amplicon sequencing. Identification of differentially abundant taxa and co-occurrence patterns, discriminant analysis and biomarker inferences were performed. The data show that smoking results in a loss in the diversity of the LRT microbiome, change in the co-occurrence patterns and a weakening of the tight community structure present in healthy microbiomes. The increased abundance of the genus Ralstonia in the lung microbiomes of both former and active smokers is significant. Partial least square discriminant and DESeq2 analyses suggested a compositional difference between the cohorts in the LRT microbiome. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS. The linear discriminant analysis effect size (LEfSe) analyses identified several bacterial taxa as potential biomarkers of smoking status. Network-based clustering analysis highlighted different co-occurring and co-avoiding microbial taxa in the three groups. The analysis found a cluster of bacterial taxa that co-occur in smokers and non-smokers alike. The clusters exhibited tighter and more significant associations in NS compared to FS and AS. Higher degree of rivalry between clusters was observed in the AS. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS.

Validation of 16S rRNA Gene Sequencing of the Periocular Microbiome and Lack of Alteration by Topical Eyedrops

Purpose

Genomic techniques for characterizing the ocular microbiome require further validation. We compared the microbiome of patients' eyelids through both conventional culture and 16S rRNA analysis and analyzed the impact of eyedrop use on microbiome diversity.

Methods

Ninety-eight patients followed for management of glaucoma or suspicion of glaucoma had eyelid swabs performed with Isohelix MS Mini DNA Swabs (98 participants) and ESwabs (49 participants) for 16S rRNA analysis and conventional culture, respectively. The effect of preservative-containing eyedrops on the microbiomes detected using these two techniques were analyzed and compared across techniques.

Results

Forty-five of the 50 (non-unique) genera (90%) identified by conventional culture were also identified by each individual's 16S rRNA analysis within the top 14 most abundant organisms present based on operational taxonomic unit. All conventional cultures performed had at least one or more genera also identified by each participant's 16S rRNA analysis. There was no difference in the conventional culture positivity rate or proportion of participants with a particular genus present on conventional culture based on whether preservative-containing eyedrops were regularly used. Similarly, in eyes using versus not using eyedrops, no differences were observed in the proportions of participants with a particular genus present or the Shannon index as determined by 16S rRNA analysis.

Conclusions

16S rRNA analysis correlates well with conventional culture results for the eyelid microbiome, with results from neither technique demonstrating an association of microbiome composition and eyedrop use. The clinical relevance of the large numbers of microbes detected via 16S rRNA analysis requires further study.

Translational Relevance

16S rRNA analysis of the periocular microbiome is consistent with conventional culture and enables further study of physiologic and pathologic ocular processes possibly related to microbiome diversity.

Fine-scale evaluation of two standard 16S rRNA gene amplicon primer pairs for analysis of total prokaryotes and archaeal nitrifiers in differently managed soils

The advance of high-throughput molecular biology tools allows in-depth profiling of microbial communities in soils, which possess a high diversity of prokaryotic microorganisms. Amplicon-based sequencing of 16S rRNA genes is the most common approach to studying the richness and composition of soil prokaryotes. To reliably detect different taxonomic lineages of microorganisms in a single soil sample, an adequate pipeline including DNA isolation, primer selection, PCR amplification, library preparation, DNA sequencing, and bioinformatic post-processing is required. Besides DNA sequencing quality and depth, the selection of PCR primers and PCR amplification reactions arguably have the largest influence on the results. This study tested the performance and potential bias of two primer pairs, i.e., 515F (Parada)-806R (Apprill) and 515F (Parada)-926R (Quince) in the standard pipelines of 16S rRNA gene Illumina amplicon sequencing protocol developed by the Earth Microbiome Project (EMP), against shotgun metagenome-based 16S rRNA gene reads. The evaluation was conducted using five differently managed soils. We observed a higher richness of soil total prokaryotes by using reverse primer 806R compared to 926R, contradicting to in silico evaluation results. Both primer pairs revealed various degrees of taxon-specific bias compared to metagenome-derived 16S rRNA gene reads. Nonetheless, we found consistent patterns of microbial community variation associated with different land uses, irrespective of primers used. Total microbial communities, as well as ammonia oxidizing archaea (AOA), the predominant ammonia oxidizers in these soils, shifted along with increased soil pH due to agricultural management. In the unmanaged low pH plot abundance of AOA was dominated by the acid-tolerant NS-Gamma clade, whereas limed agricultural plots were dominated by neutral-alkaliphilic NS-Delta/NS-Alpha clades. This study stresses how primer selection influences community composition and highlights the importance of primer selection for comparative and integrative studies, and that conclusions must be drawn with caution if data from different sequencing pipelines are to be compared.

Insights into the Methodological, Biotic and Abiotic Factors Influencing the Characterization of Xylem-Inhabiting Microbial Communities of Olive Trees

Vascular pathogens are the causal agents of some of the most devastating plant diseases in the world, which can cause, under specific conditions, the destruction of entire crops. These plant pathogens activate a range of physiological and immune reactions in the host plant following infection, which may trigger the proliferation of a specific microbiome to combat them by, among others, inhibiting their growth and/or competing for space. Nowadays, it has been demonstrated that the plant microbiome can be modified by transplanting specific members of the microbiome, with exciting results for the control of plant diseases. However, its practical application in agriculture for the control of vascular plant pathogens is hampered by the limited knowledge of the plant endosphere, and, in particular, of the xylem niche. In this review, we present a comprehensive overview of how research on the plant microbiome has evolved during the last decades to unravel the factors and complex interactions that affect the associated microbial communities and their surrounding environment, focusing on the microbial communities inhabiting the xylem vessels of olive trees (Olea europaea subsp. europaea), the most ancient and important woody crop in the Mediterranean Basin. For that purpose, we have highlighted the role of xylem composition and its associated microorganisms in plants by describing the methodological approaches explored to study xylem microbiota, starting from the methods used to extract xylem microbial communities to their assessment by culture-dependent and next-generation sequencing approaches. Additionally, we have categorized some of the key biotic and abiotic factors, such as the host plant niche and genotype, the environment and the infection with vascular pathogens, that can be potential determinants to critically affect olive physiology and health status in a holobiont context (host and its associated organisms). Finally, we have outlined future directions and challenges for xylem microbiome studies based on the recent advances in molecular biology, focusing on metagenomics and culturomics, and bioinformatics network analysis. A better understanding of the xylem olive microbiome will contribute to facilitate the exploration and selection of specific keystone microorganisms that can live in close association with olives under a range of environmental/agronomic conditions. These microorganisms could be ideal targets for the design of microbial consortia that can be applied by endotherapy treatments to prevent or control diseases caused by vascular pathogens or modify the physiology and growth of olive trees.

The Skin Microbiome: Current Landscape and Future Opportunities

Our skin is the largest organ of the body, serving as an important barrier against the harsh extrinsic environment. Alongside preventing desiccation, chemical damage and hypothermia, this barrier protects the body from invading pathogens through a sophisticated innate immune response and co-adapted consortium of commensal microorganisms, collectively termed the microbiota. These microorganisms inhabit distinct biogeographical regions dictated by skin physiology. Thus, it follows that perturbations to normal skin homeostasis, as occurs with ageing, diabetes and skin disease, can cause microbial dysbiosis and increase infection risk. In this review, we discuss emerging concepts in skin microbiome research, highlighting pertinent links between skin ageing, the microbiome and cutaneous repair. Moreover, we address gaps in current knowledge and highlight key areas requiring further exploration. Future advances in this field could revolutionise the way we treat microbial dysbiosis associated with skin ageing and other pathologies.

Optimization of Molecular Methods for Detecting Duckweed-Associated Bacteria

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

MinION Sequencing of Yeast Mock Communities To Assess the Effect of Databases and ITS-LSU Markers on the Reliability of Metabarcoding Analysis

Microbial communities play key roles both for humans and the environment. They are involved in ecosystem functions, maintaining their stability, and provide important services, such as carbon cycle and nitrogen cycle. Acting both as symbionts and as pathogens, description of the structure and composition of these communities is important. Metabarcoding uses ribosomal DNA (rDNA) (eukaryotic) or rRNA gene (prokaryotic) sequences for identification of species present in a site and measuring their abundance. This procedure requires several technical steps that could be source of bias producing a distorted view of the real community composition. In this work, we took advantage of an innovative "long-read" next-generation sequencing (NGS) technology (MinION) amplifying the DNA spanning from the internal transcribed spacer (ITS) to large subunit (LSU) that can be read simultaneously in this platform, providing more information than "short-read" systems. The experimental system consisted of six fungal mock communities composed of species present at various relative amounts to mimic natural situations characterized by predominant and low-frequency species. The influence of the sequencing platform (MinION and Illumina MiSeq) and the effect of different reference databases and marker sequences on metagenomic identification of species were evaluated. The results showed that the ITS-based database provided more accurate species identification than LSU. Furthermore, a procedure based on a preliminary identification with standard reference databases followed by the production of custom databases, including only the best outputs of the first step, is proposed. This additional step improved the estimate of species proportion of the mock communities and reduced the number of ghost species not really present in the simulated communities. IMPORTANCE Metagenomic analyses are fundamental in many research areas; therefore, improvement of methods and protocols for the description of microbial communities becomes more and more necessary. Long-read sequencing could be used for reducing biases due to the multicopy nature of rDNA sequences and short-read limitations. However, these novel technologies need to be assessed and standardized with controlled experiments, such as mock communities. The interest behind this work was to evaluate how long reads performed identification and quantification of species mixed in precise proportions and how the choice of database affects such analyses. Development of a pipeline that mitigates the effect of the barcoding sequences and the impact of the reference database on metagenomic analyses can help microbiome studies go one step further.

Physical activity and diet associations with the gut microbiota in the Coronary Artery Risk Development in Young Adults (CARDIA) study

BACKGROUND

Gut microbiota may influence metabolic pathways related to chronic health conditions. Evidence for physical activity and diet influences on gut microbial composition exists, but data from diverse population-based cohort studies are limited.

OBJECTIVES

We hypothesized that gut microbial diversity and genera are associated with physical activity and diet quality.

METHODS

Data were from 537 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort, who attended the year 30 follow-up examination (2015-2016; aged 47-61 y; 45% Black race/55% White race; 45% men/55% women). The 16S ribosomal RNA marker gene was sequenced from stool DNA, and genus-level taxonomy was assigned. Within-person microbial diversity (α-diversity) was assessed with Shannon diversity index and richness scores; between-person diversity (β-diversity) measures were generated with principal coordinates analysis (PCoA). Current and long-term physical activity and diet quality measures were derived from data collected over 30 y of follow-up. Multivariable-adjusted regression analysis controlled for: sociodemographic variables (age, race, sex, education, and field center), other health behaviors (smoking, alcohol consumption, and medication use), and adjusted for multiple comparisons with the false discovery rate (<0.20).

RESULTS

Based on PCoA β-diversity, participants' microbial community compositions differed significantly (P < 0.001), with respect to both current and long-term physical activity and diet quality. α-Diversity was associated only with current physical activity (positively) in multivariable-adjusted analysis. Multiple genera (n = 45) were associated with physical activity and fewer with diet (n = 5), including positive associations with Lachnospiraceae UCG-001 and Ruminococcaceae IncertaeSedis with both behaviors.

CONCLUSIONS

Physical activity and diet quality were associated with gut microbial composition among 537 participants in the CARDIA study. Multiple genera were associated with physical activity. Physical activity and diet quality were associated with genera consistent with pathways related to inflammation and short-chain fatty acid production.

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

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

Gut microbiome studies in CKD: opportunities, pitfalls and therapeutic potential

Interest in gut microbiome dysbiosis and its potential association with the development and progression of chronic kidney disease (CKD) has increased substantially in the past 6 years. In parallel, the microbiome field has matured considerably as the importance of host-related and environmental factors is increasingly recognized. Past research output in the context of CKD insufficiently considered the myriad confounding factors that are characteristic of the disease. Gut microbiota-derived metabolites remain an interesting therapeutic target to decrease uraemic (cardio)toxicity. However, future studies on the effect of dietary and biotic interventions will require harmonization of relevant readouts to enable an in-depth understanding of the underlying beneficial mechanisms. High-quality standards throughout the entire microbiome analysis workflow are also of utmost importance to obtain reliable and reproducible results. Importantly, investigating the relative composition and abundance of gut bacteria, and their potential association with plasma uraemic toxins levels is not sufficient. As in other fields, the time has come to move towards in-depth quantitative and functional exploration of the patient's gut microbiome by relying on confounder-controlled quantitative microbial profiling, shotgun metagenomics and in vitro simulations of microorganism-microorganism and host-microorganism interactions. This step is crucial to enable the rational selection and monitoring of dietary and biotic intervention strategies that can be deployed as a personalized intervention in CKD.

Investigating the Ocular Surface Microbiome: What Can It Tell Us?

While pathogens of the eye have been studied for a very long time, the existence of resident microbes on the surface of healthy eyes has gained interest only recently. It appears that commensal microbes are a normal feature of the healthy eye, whose role and properties are currently the subject of extensive research. This review provides an overview of studies that have used 16s rRNA gene sequencing and whole metagenome shotgun sequencing to characterize microbial communities associated with the healthy ocular surface from kingdom to genus level. Bacteria are the primary colonizers of the healthy ocular surface, with three predominant phyla: Proteobacteria, Actinobacteria, and Firmicutes, regardless of the host, environment, and method used. Refining the microbial classification to the genus level reveals a highly variable distribution from one individual and study to another. Factors accounting for this variability are intriguing - it is currently unknown to what extent this is attributable to the individuals and their environment and how much is artifactual. Clearly, it is technically challenging to accurately describe the microorganisms of the ocular surface because their abundance is relatively low, thus, permitting substantial contaminations. More research is needed, including better experimental standards to prevent biases, and the exploration of the ocular surface microbiome's role in a spectrum of healthy to pathological states. Outcomes from such research include the opportunity for therapeutic interventions targeting the microbiome.

Technical versus biological variability in a synthetic human gut community

Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2-3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.