Current Applications of Absolute Bacterial Quantification in Microbiome Studies and Decision-Making Regarding Different Biological Questions

Construction of the time since deposition (TsD) model in saliva stains with 16S rRNA full-length sequencing technology and microbial markers

Determining the time since deposition (TsD) and sex of saliva stains is crucial for revealing the time of the crime's occurrence and clarifying the nature of the crime. This process not only shortens the time required to solve the case but also helps narrow down the scope of investigation, thereby enhancing the efficiency of case resolution. Currently, the forensic study of the microbial composition in long-term saliva stains remains a relatively underexplored field. The purpose of this study was to explore the succession pattern of long-placed human saliva stains microbial communities and identify relevant microbial markers for estimating TsD and identifying the sex of the donor, in order to be an effective alternative tool for solving practical forensic cases. Therefore, in this study, saliva stains exposed to indoor environmental conditions for up to 140 days were collected and 16S rRNA full-length sequencing was performed using single-molecule real-time sequencing technology based on the PacBio sequencing platform. The study reveals that after 140 days of placement, the relative abundance of Firmicutes significantly decreased (p = 0.00304). At the genus level, the relative abundances of Streptococcus (p = 0.0008), Rothia (p = 0.0448), Gemella (p = 0.016), and Veillonella (p = 0.0208) also significantly decreased. Additionally, significant differences were found in the microbial communities between saliva stains from males and females (p = 0.00013). Then, we constructed a TsD estimating model for microbial community markers based on random forest, and the results showed that the mean absolute error was 9.59 days, and the accuracy of sex classification model based on stepwise logistic regression model and 4 bacterial markers was 84.21%. This indicates that saliva stains that have been in place for a long time still retain significant forensic value, and microbial markers can be used to determine the time since deposition (TsD) of dried saliva stains as well as to identify the sex of the donor.

Antibiotic-perturbed microbiota and the role of probiotics

The disruptive effect of antibiotics on the composition and function of the human microbiota is well established. However, the hypothesis that probiotics can help restore the antibiotic-disrupted microbiota has been advanced, with little consideration of the strength of evidence supporting it. Some clinical data suggest that probiotics can reduce antibiotic-related side effects, including Clostridioides difficile-associated diarrhoea, but there are no data that causally link these clinical effects to microbiota protection or recovery. Substantial challenges hinder attempts to address this hypothesis, including the absence of consensus on the composition of a 'normal' microbiota, non-standardized and evolving microbiome measurement methods, and substantial inter-individual microbiota variation. In this Review, we explore these complexities. First, we review the known benefits and risks of antibiotics, the effect of antibiotics on the human microbiota, the resilience and adaptability of the microbiota, and how microbiota restoration might be defined and measured. Subsequently, we explore the evidence for the efficacy of probiotics in preventing disruption or aiding microbiota recovery post-antibiotic treatment. Finally, we offer insights into the current state of research and suggest directions for future research.

Method for Identification and Bacterial Count Quantification in a Case of Ureaplasma Meningitis

Intrauterine Ureaplasma infection is associated with chorioamnionitis and preterm birth. The difficulty of detecting Ureaplasma species by conventional culture methods makes definitive diagnosis of clinical infection challenging. Thus far, quantitative tests for Ureaplasma have been performed using adult cervical samples, amniotic fluid, and pediatric bronchial secretions, but quantification of bacterial count in central nervous system infections caused by Ureaplasma species has not been unreported. We report a case of culture-negative Ureaplasma meningitis in a preterm infant in whom novel techniques to identify this pathogen and quantify bacterial count were effective. We suspected meningitis based on a sustained reduction in cerebrospinal fluid (CSF) glucose levels. Multiple CSF cultures were sterile. We confirmed infection by Ureaplasma species using the melting temperature mapping method. Treatment with erythromycin and ciprofloxacin resulted in a gradual decrease in the bacterial count in the CSF to 0. Our study highlights the potential utility of the melting temperature mapping method as a new diagnostic tool for culture-negative Ureaplasma meningitis and establishes the utility of serial quantification of bacterial count to monitor response to therapy.

Highly accurate and sensitive absolute quantification of bacterial strains in human fecal samples

BACKGROUND

Next-generation sequencing (NGS) approaches have revolutionized gut microbiome research and can provide strain-level resolution, but these techniques have limitations in that they are only semi-quantitative, suffer from high detection limits, and generate data that is compositional. The present study aimed to systematically compare quantitative PCR (qPCR) and droplet digital PCR (ddPCR) for the absolute quantification of Limosilactobacillus reuteri strains in human fecal samples and to develop an optimized protocol for the absolute quantification of bacterial strains in fecal samples.

RESULTS

Using strain-specific PCR primers for L. reuteri 17938, ddPCR showed slightly better reproducibility, but qPCR was almost as reproducible and showed comparable sensitivity (limit of detection [LOD] around 104 cells/g feces) and linearity (R2 > 0.98) when kit-based DNA isolation methods were used. qPCR further had a wider dynamic range and is cheaper and faster. Based on these findings, we conclude that qPCR has advantages over ddPCR for the absolute quantification of bacterial strains in fecal samples. We provide an optimized and easy-to-follow step-by-step protocol for the design of strain-specific qPCR assays, starting from primer design from genome sequences to the calibration of the PCR system. Validation of this protocol to design PCR assays for two L. reuteri strains, PB-W1 and DSM 20016 T, resulted in a highly accurate qPCR with a detection limit in spiked fecal samples of around 103 cells/g feces. Applying our strain-specific qPCR assays to fecal samples collected from human subjects who received live L. reuteri PB-W1 or DSM 20016 T during a human trial demonstrated a highly accurate quantification and sensitive detection of these two strains, with a much lower LOD and a broader dynamic range compared to NGS approaches (16S rRNA gene sequencing and whole metagenome sequencing).

CONCLUSIONS

Based on our analyses, we consider qPCR with kit-based DNA extraction approaches the best approach to accurately quantify gut bacteria at the strain level in fecal samples. The provided step-by-step protocol will allow scientists to design highly sensitive strain-specific PCR systems for the accurate quantification of bacterial strains of not only L. reuteri but also other bacterial taxa in a broad range of applications and sample types. Video Abstract.

The impact of DNA extraction on the quantification of Legionella, with implications for ecological studies

Monitoring the levels of opportunistic pathogens in drinking water is important to plan interventions and understand the ecological niches that allow them to proliferate. Quantitative PCR is an established alternative to culture methods that can provide a faster, higher-throughput, and more precise enumeration of the bacteria in water samples. However, PCR-based methods are still not routinely applied for Legionella monitoring, and techniques, such as DNA extraction, differ notably between laboratories. Here, we quantify the impact that DNA extraction methods had on downstream PCR quantification and community sequencing. Through a community science campaign, we collected 50 water samples and corresponding shower hoses, and compared two commonly used DNA extraction methodologies to the same biofilm and water phase samples. The two methods showed clearly different extraction efficacies, which were reflected in both the quantity of DNA extracted and the concentrations of Legionella enumerated in both the matrices. Notably, one method resulted in higher enumeration in nearly all samples by about one order of magnitude and detected Legionella in 21 samples that remained undetected by the other method. 16S rRNA amplicon sequencing revealed that the relative abundance of individual taxa, including sequence variants of Legionella, significantly varied depending on the extraction method employed. Given the implications of these findings, we advocate for improvement in documentation of the performance of DNA extraction methods used in drinking water to detect and quantify Legionella, and characterize the associated microbial community.IMPORTANCEMonitoring for the presence of the waterborne opportunistic pathogen Legionella is important to assess the risk of infection and plan remediation actions. While monitoring is traditionally carried on through cultivation, there is an ever-increasing demand for rapid and high-throughput molecular-based approaches for Legionella detection. This paper provides valuable insights on how DNA extraction affects downstream molecular analysis such as the quantification of Legionella through droplet digital PCR and the characterization of natural microbial communities through sequencing analysis. We analyze the results from a risk-assessment, legislative, and ecological perspective, showing how initial DNA processing is an important step to take into account when shifting to molecular-based routine monitoring and discuss the central role of consistent and detailed reporting of the methods used.

Metatranscriptomic Analysis Reveals Synergistic Activities of Comammox and Anammox Bacteria in Full-Scale Attached Growth Nitrogen Removal System

Leveraging comammox Nitrospira and anammox bacteria for shortcut nitrogen removal can drastically lower the carbon footprint of wastewater treatment facilities by decreasing aeration energy, carbon, alkalinity, and tank volume requirements while also potentially reducing nitrous oxide emissions. However, their co-occurrence as dominant nitrifying bacteria is rarely reported in full-scale wastewater treatment. As a result, there is a poor understanding of how operational parameters, in particular, dissolved oxygen, impact their activity and synergistic behavior. Here, we report the impact of dissolved oxygen concentration (DO = 2, 4, 6 mg/L) on the microbial community's transcriptomic expression in a full-scale integrated fixed film activated sludge (IFAS) municipal wastewater treatment facility where nitrogen removal is predominantly performed by comammox Nitrospira and anammox bacterial populations. 16S rRNA transcript compositions revealed anammox bacteria and Nitrospira were significantly more active in IFAS biofilms compared to suspended sludge biomass. In IFAS biofilms, anammox bacteria significantly increased hzo expression at lower dissolved oxygen concentrations and this increase was highly correlated with the amoA expression levels of comammox bacteria. Interestingly, the genes involved in nitrite oxidation by comammox bacteria were significantly more upregulated, relative to the genes involved in ammonia oxidation with decreasing dissolved oxygen concentrations. Ultimately, our findings suggest that comammox Nitrospira supplies anammox bacteria with nitrite via ammonia oxidation and that this synergistic behavior is dependent on dissolved oxygen concentrations.

Dynamic changes of gut microbiota in mouse models of metabolic dysfunction-associated steatohepatitis and its transition to hepatocellular carcinoma

Dysbiosis of gut microbiota may account for pathobiology in simple fatty liver (SFL), metabolic dysfunction-associated steatohepatitis (MASH), fibrotic progression, and transformation to MASH-associated hepatocellular carcinoma (MASH-HCC). The aim of the present study is to investigate gut dysbiosis in this progression. Fecal microbial rRNA-16S sequencing, absolute quantification, histopathologic, and biochemical tests were performed in mice fed high fat/calorie diet plus high fructose and glucose in drinking water (HFCD-HF/G) or control diet (CD) for 2, 16 weeks, or 14 months. Histopathologic examination verified an early stage of SFL, MASH, fibrotic, or MASH-HCC progression with disturbance of lipid metabolism, liver injury, and impaired gut mucosal barrier as indicated by loss of occludin in ileum mucosa. Gut dysbiosis occurred as early as 2 weeks with reduced α diversity, expansion of Kineothrix, Lactococcus, Akkermansia; and shrinkage in Bifidobacterium, Lactobacillus, etc., at a genus level. Dysbiosis was found as early as MAHS initiation, and was much more profound through the MASH-fibrotic and oncogenic progression. Moreover, the expansion of specific species, such as Lactobacillus johnsonii and Kineothrix alysoides, was confirmed by an optimized method for absolute quantification. Dynamic alterations of gut microbiota were characterized in three stages of early SFL, MASH, and its HCC transformation. The findings suggest that the extent of dysbiosis was accompanied with MASH progression and its transformation to HCC, and the shrinking or emerging of specific microbial species may account at least in part for pathologic, metabolic, and immunologic alterations in fibrogenic progression and malignant transition in the liver.

Development of a digital droplet PCR approach for the quantification of soil micro-organisms involved in atmospheric CO2 fixation

Carbon-fixing micro-organisms (CFMs) play a pivotal role in soil carbon cycling, contributing to carbon uptake and sequestration through various metabolic pathways. Despite their importance, accurately quantifying the absolute abundance of these micro-organisms in soils has been challenging. This study used a digital droplet polymerase chain reaction (ddPCR) approach to measure the abundance of key and emerging CFMs pathways in fen and bog soils at different depths, ranging from 0 to 15 cm. We targeted total prokaryotes, oxygenic phototrophs, aerobic anoxygenic phototrophic bacteria and chemoautotrophs, optimizing the conditions to achieve absolute quantification of these genes. Our results revealed that oxygenic phototrophs were the most abundant CFMs, making up 15% of the total prokaryotic abundance. They were followed by chemoautotrophs at 10% and aerobic anoxygenic phototrophic bacteria at 9%. We observed higher gene concentrations in fen than in bog. There were also variations in depth, which differed between fen and bog for all genes. Our findings underscore the abundance of oxygenic phototrophs and chemoautotrophs in peatlands, challenging previous estimates that relied solely on oxygenic phototrophs for microbial carbon dioxide fixation assessments. Incorporating absolute gene quantification is essential for a comprehensive understanding of microbial contributions to soil processes. This approach sheds light on the complex mechanisms of soil functioning in peatlands.

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.

Uterine microbial profiles in healthy postpartum dairy cows do not vary with sampling techniques or phases of estrous cycle

In this study, we aimed to compare uterine microbial profiles in postpartum dairy cows, determined by bacteriological culture and next-generation sequencing, using three uterine sampling techniques (swab, cytobrush, and lavage) and induced phases of the estrous cycle (estrus and diestrus). Fifteen healthy postpartum dairy cows at 53 ± 5 days postpartum were enrolled in the study. Uterine samples were collected during a fixed-time artificial insemination protocol. Viable bacteria were aerobically cultured from part of each sample, and bacterial isolates were identified through Sanger sequencing of the 16S rRNA gene. Total genomic DNA was extracted from the remainder of undiluted samples to quantify bacterial load using 16S rRNA qPCR and characterize the microbiome by metagenomic sequencing of the V1-V3 region of the 16S rRNA gene. Microbial profiles and composition were analyzed using the Shannon-Weaver diversity index and principal component analysis, respectively. Out of 87 samples, 88 % (77/87) were culture positive. The proportion of culture-positive uterine samples did not differ between sampling techniques (P = 0.39) or estrous cycle phases (P = 0.99). However, swab, cytobrush, and lavage techniques yielded 1.5, 9 and 9 times greater bacterial loads (P < 0.01), respectively, during diestrus than estrus phase. Moreover, during diestrus phase, the cytobrush method yielded 3 and 6 times more bacteria (P < 0.01) than both the lavage and swab methods. The most abundant bacterial genera identified from both bacteriological culture and metagenomic sequencing were Bacillus and Enterococcus, regardless of sampling technique or phases of the estrous cycle. Bacterial genera in moderate to low abundance through metagenomic sequencing included Streptococcus, Oscillospiraceae, and Lachnospiraceae. Notably, the uterine microbial profiles and composition, determined by metagenomic sequencing, did not differ by sampling techniques (P = 0.55 and P = 0.60, respectively) or estrous cycle phases (P = 0.34 and P = 0.17, respectively). In conclusion, our results suggest that any of the sampling techniques can be reliably used to study the uterine microbiome of healthy cows at random phases of the estrous cycle. However, it is important to consider potential differences in bacterial yield as a confounding factor.

Metabarcoding Analysis of Microorganisms Inside Household Washing Machines in Shanghai, China

Washing machines are one of the tools that bring great convenience to people's daily lives. However, washing machines that have been used for a long time often develop issues such as odor and mold, which can pose health hazards to consumers. There exists a conspicuous gap in our understanding of the microorganisms that inhabit the inner workings of washing machines. In this study, samples were collected from 22 washing machines in Shanghai, China, including both water eluted from different parts of washing machines and biofilms. Quantitative qualitative analysis was performed using fluorescence PCR quantification, and microbial communities were characterized by high-throughput sequencing (HTS). This showed that the microbial communities in all samples were predominantly composed of bacteria. HTS results showed that in the eluted water samples, the bacteria mainly included Pseudomonas, Enhydrobacter, Brevibacterium, and Acinetobacter. Conversely, in the biofilm samples, Enhydrobacter and Brevibacterium were the predominant bacterial microorganisms. Correlation analysis results revealed that microbial colonies in washing machines were significantly correlated with years of use and the type of detergent used to clean the washing machine. As numerous pathogenic microorganisms can be observed in the results, effective preventive measures and future research are essential to mitigate these health problems and ensure the continued safe use of these household appliances.

Quantitative Aggregation of Microbiome Sequencing Data Provides Insights into the Associations between the Skin Microbiome and Psoriasis

Although prior studies have reported distinct skin microbiome profiles associated with psoriasis, differences in methods and analyses limit generalizable conclusions. Individual studies have actually reported conflicting findings; for example, Propionibacterium and Staphylococcus have been significantly associated with both psoriatic lesions and healthy skin. Qualitative reviews have attempted to summarize this body of work, but there is great variability across the studies' findings and methods. To better unify these data, we created a meta-analysis of all publicly available datasets by utilizing a uniform bioinformatics pipeline and reference database to investigate associations of the skin microbiome in psoriasis. A total of 977 skin swab samples (341 lesional, 295 nonlesional, and 341 healthy) from 6 studies were analyzed. The aggregated analysis revealed a higher relative abundance of microorganisms, including Staphylococcus aureus and Corynebacterium simulans, among others, from patients with psoriasis than those from healthy swab samples; in addition, Cutibacterium acnes, Lawsonella unclassified, and S warneri were significantly higher in healthy samples. Furthermore, comparison of functional pathways predicted from 16S gene markers showed that L-ornithine biosynthesis and L-histidine biosynthesis were lower in psoriatic lesions than in healthy controls. Taken together, this meta-analysis allows for a more generalizable association between the skin microbiome and psoriasis.

Ketogenic Diet Has Moderate Effects on the Fecal Microbiota of Wild-Type Mice

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that has been reported to have neuroprotective effects. The health effects of KD might be linked to an altered gut microbiome, which plays a major role in host health, leading to neuroprotective effects via the gut-brain axis. However, results from different studies, most often based on the 16S rRNA gene and metagenome sequencing, have been inconsistent. In this study, we assessed the effect of a 4-week KD compared to a western diet (WD) on the colonic microbiome of female C57Bl/6J mice by analyzing fecal samples using fluorescence in situ hybridization. Our results showed distinct changes in the total number of gut bacteria following the 4-week KD, in addition to changes in the composition of the microbiome. KD-fed mice showed higher absolute numbers of Actinobacteria (especially Bifidobacteria spp.) and lower absolute levels of Proteobacteria, often linked to gut inflammation, in comparison with WD-fed mice. Furthermore, an increased abundance of the typically rare genus Atopobium was observed. These changes may indicate the possible anti-inflammatory effects of the KD. However, since the overall changes in the microbiota seem low, the KD effects might be linked to the differential abundance of only a few key genera in mice.

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

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

Best practice for wildlife gut microbiome research: A comprehensive review of methodology for 16S rRNA gene investigations

Extensive research in well-studied animal models underscores the importance of commensal gastrointestinal (gut) microbes to animal physiology. Gut microbes have been shown to impact dietary digestion, mediate infection, and even modify behavior and cognition. Given the large physiological and pathophysiological contribution microbes provide their host, it is reasonable to assume that the vertebrate gut microbiome may also impact the fitness, health and ecology of wildlife. In accordance with this expectation, an increasing number of investigations have considered the role of the gut microbiome in wildlife ecology, health, and conservation. To help promote the development of this nascent field, we need to dissolve the technical barriers prohibitive to performing wildlife microbiome research. The present review discusses the 16S rRNA gene microbiome research landscape, clarifying best practices in microbiome data generation and analysis, with particular emphasis on unique situations that arise during wildlife investigations. Special consideration is given to topics relevant for microbiome wildlife research from sample collection to molecular techniques for data generation, to data analysis strategies. Our hope is that this article not only calls for greater integration of microbiome analyses into wildlife ecology and health studies but provides researchers with the technical framework needed to successfully conduct such investigations.

Gut Microbiome Analysis for Personalized Nutrition: The State of Science

Whereas most concepts of personalized nutrition (PN) in the past, included genotyping, recent years have brought new approaches that include microbiome analysis to optimize recommendations for diet and lifestyle changes. The new approach, offered by companies, that microbiome analysis provides a real benefit to either more concise recommendations or for increased compliance to PN, is largely lacking scientific validation. Although the microbiome field shows enormous proliferation, it has some major flaws that make its use in the public health domain currently critical. Starting with the quality and representative character of the stool samples, its processing and analysis as well as assembly of metagenome data and the interpretation. Moreover, there is still no consensus of what constitutes a "normal/healthy" microbiome, nor what features characterize a dysbiotic microbiome. And, based on hundreds of individual parameters and environmental factors, the intestinal microbiome shows a huge variability and consequently changing one factor-such as food intake-is likely to have a limited impact in achieving optimized health. The present review intends to summarize the state of consolidated knowledge on human gut microbiome in the context of diet and disease, its key features, and its influencing factors as well as its "add-on" quality for PN offers.

Microbial DNA extraction of high-host content and low biomass samples: Optimized protocol for nasopharynx metagenomic studies

Introduction

Low microbial biomass and high human DNA content in nasopharyngeal aspirate samples hinder comprehensive characterization of microbiota and resistome. We obtained samples from premature infants, a group with increased risk of developing respiratory disorders and infections, and consequently frequent exposure to antibiotics. Our aim was to devise an optimal protocol for handling nasopharyngeal aspirate samples from premature infants, focusing on host DNA depletion and microbiome and resistome characterization.

Methods

Three depletion and three DNA extraction protocols were compared, using RT-PCR and whole metagenome sequencing to determine the efficiency of human DNA removal, taxonomic profiling and assignment of antibiotic resistance genes. Protocols were tested using mock communities, as well as pooled and individual patient samples.

Results

The only extraction protocol to retrieve the expected DNA yield from mock community samples was based on a lytic method to improve Gram positive recovery (MasterPure™). Host DNA content in non-depleted aliquots from pooled patient samples was 99%. Only samples depleted with MolYsis™ showed satisfactory, but varied reduction in host DNA content, in both pooled and individual patient samples, allowing for microbiome and resistome characterisation (host DNA content from 15% to 98%). Other depletion protocols either retrieved too low total DNA yields, preventing further analysis, or failed to reduce host DNA content. By using Mol_MasterPure protocol on aliquots from pooled patient samples, we increased the number of bacterial reads by 7.6 to 1,725.8-fold compared to non-depleted reference samples. PCR results were indicative of achieved microbial enrichment. Individual patient samples processed with Mol_MasterPure protocol varied greatly in total DNA yield, host DNA content (from 40% to 98%), species and antibiotic resistance gene richness.

Discussion

Despite high human DNA and low microbial biomass content in nasopharynx aspirates of preterm infants, we were able to reduce host DNA content to levels compatible with downstream shotgun metagenomic analysis, including bacterial species identification and coverage of antibiotic resistance genes. Whole metagenomic sequencing of microbes colonizing the nasopharynx may contribute to explaining the possible role of airway microbiota in respiratory conditions and reveal carriage of antibiotic resistance genes.

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

BACKGROUND

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

RESULTS

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

The Environmental Microbiome, Allergic Disease, and Asthma

The environmental microbiome represents the entirety of the microbes and their metabolites that we encounter in our environments. A growing body of evidence supports the role of the environmental microbiome in risk for and severity of allergic diseases and asthma. The environmental microbiome represents a ubiquitous, lifelong exposure to non-self antigens. During the critical window between birth and 1 year of life, interactions between our early immune system and the environmental microbiome have 2 consequences: our individual microbiome is populated by environmental microbes, and our immune system is trained regarding which antigens to tolerate. During this time, a diversity of exposures appears largely protective, dramatically decreasing the risk of developing allergic diseases and asthma. As we grow older, our interactions with the environmental microbiome change. While it continues to exert influence over the composition of the human microbiome, the environmental microbiome becomes increasingly a source for antigenic stimulation and infection. The same microbial exposure protective against disease development may exacerbate disease severity. Although much has been learned about the importance of the environmental microbiome in allergic disease, much more remains to be understood about these complicated interactions between our environment, our microbiome, our immune system, and disease.

Molecular Methods for Pathogenic Bacteria Detection and Recent Advances in Wastewater Analysis

With increasing concerns about public health and the development of molecular techniques, new detection tools and the combination of existing approaches have increased the abilities of pathogenic bacteria monitoring by exploring new biomarkers, increasing the sensitivity and accuracy of detection, quantification, and analyzing various genes such as functional genes and antimicrobial resistance genes (ARG). Molecular methods are gradually emerging as the most popular detection approach for pathogens, in addition to the conventional culture-based plate enumeration methods. The analysis of pathogens in wastewater and the back-estimation of infections in the community, also known as wastewater-based epidemiology (WBE), is an emerging methodology and has a great potential to supplement current surveillance systems for the monitoring of infectious diseases and the early warning of outbreaks. However, as a complex matrix, wastewater largely challenges the analytical performance of molecular methods. This review synthesized the literature of typical pathogenic bacteria in wastewater, types of biomarkers, molecular methods for bacterial analysis, and their recent advances in wastewater analysis. The advantages and limitation of these molecular methods were evaluated, and their prospects in WBE were discussed to provide insight for future development.